UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe

Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.

Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation

Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area index (LAI), which can be accurately assessed using proximal or remote sensing data based on plant canopies. This study aimed to (1) develop a machine learning-based method for estimating the LAI in rice and soybean crops using proximal sensing data and (2) evaluate the performance of a Remote Sensing-Integrated Crop Model (RSCM) when integrated with the ML algorithms. To achieve these objectives, we analyzed rice and soybean datasets to identify the most effective ML algorithms for modeling the relationship between LAI and vegetation indices derived from canopy reflectance measurements. Our analyses employed a variety of ML regression models, including ridge, lasso, support vector machine, random forest, and extra trees. Among these, the extra trees regression model demonstrated the best performance, achieving test scores of 0.86 and 0.89 for rice and soybean crops, respectively. This model closely replicated observed LAI values under different nitrogen treatments, achieving Nash-Sutcliffe efficiencies of 0.93 for rice and 0.97 for soybean. Our findings show that incorporating ML techniques into RSCM effectively captures seasonal LAI variations across diverse field management practices, offering significant potential for improving crop growth and productivity monitoring.

CuticleTrace: A toolkit for capturing cell outlines from leaf cuticle with implications for paleoecology and paleoclimatology

Premise

Leaf epidermal cell morphology is closely tied to the evolutionary history of plants and their growth environments and is therefore of interest to many plant biologists. However, cell measurement can be time consuming and restrictive with current methods. CuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.

Methods and Results

We evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand tracings across a taxonomically diverse 50-image data set of variable image qualities. We observed ~93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods.

Conclusions

CuticleTrace is a broadly applicable, modular, and customizable tool that integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape data sets.

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.

Remote Sensing Monitoring and Assessment of Global Vegetation Status and Changes during 2016-2020

Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.

Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region

The Silk Road Economic Belt and the 21st Century Maritime Silk Road Initiative (BRI) proposed in 2013 by China has greatly accelerated the social and economic development of the countries along the Belt and Road (B&R) region. However, the international community has questioned its impact on the ecological environment and a comprehensive assessment of ecosystem quality changes is lacking. Therefore, this study proposes an objective and automatic method to assess ecosystem quality and analyzes the spatiotemporal changes in the B&R region. First, an ecosystem quality index (EQI) is established by integrating the vegetation status derived from three remote sensing ecological parameters including the leaf area index, fractional vegetation cover and gross primary productivity. Then, the EQI values are automatically categorized into five ecosystem quality levels including excellent, good, moderate, low and poor to illustrate their spatiotemporal changes from the years 2016 to 2020. The results indicate that the spatial distributions of the EQIs across the B&R region exhibited similar patterns in the years 2016 and 2020. The regions with excellent levels accounted for the lowest proportion of less than 12%, while regions with moderate, low and poor levels accounted for more than 68% of the study area. Moreover, based on the EQI pattern analysis between the years 2016 and 2020, the regions with no significant EQI change accounted for up to 99.33% and approximately 0.45% experienced a significantly decreased EQI. Therefore, this study indicates that the ecosystem quality of the B&R region was relatively poor and experienced no significant change in the five years after the implementation of the "Vision and Action to Promote the Joint Construction of the Silk Road Economic Belt and the 21st Century Maritime Silk Road". This study can provide useful information for decision support on the future ecological environment management and sustainable development of the B&R region.

Basil (Ocimum basilicum) Landraces Can Be Used in a Water-Limited Environment

Basil (Ocimum basilicum L.) is a member of the Labiatae family and is one of the most widely consumed aromatic and medicinal plants in many countries due to its numerous properties and uses. The objective of the study was to determine whether landraces are better adapted to water-limited environments compared to commercial cultivars. Irrigation levels and genotypes affected plant height and leaf area index, with 25% and 33% higher values observed under complete irrigation, respectively. Additionally, limited water availability resulted in a 20% reduction in dry matter yield and a 21% reduction in essential oil yield over the three years in all of the genotypes tested, specifically in the lower irrigation treatment (d₄₀), compared to the control treatment (d₁₀₀). The landraces that performed the best under limited water supply were Athos white spike (AWS) and Gigas white spike (GWS), indicating their suitability for environments with limited water resources. The results demonstrate that there are landraces that can be utilized in dryland climates with appropriate water management, enabling water conservation and utilization of fields in water-scarce areas for irrigation purposes.

Crop coefficient determination and evapotranspiration estimation of watermelon under water deficit in a cold and arid environment

To investigate the evapotranspiration and crop coefficient of oasis watermelon under water deficit (WD), mild (60%-70% field capacity, FC)and moderate (50%-60% FC) WD levels were set up at the various growth stages of watermelon, including seedling stage (SS), vine stage (VS), flowering and fruiting stage (FS), expansion stage (ES), and maturity stage (MS), with adequate water supply (70%-80% FC) during the growing season as a control. A two-year (2020-2021) field trial was carried out in the Hexi oasis area of China to explore the effect of WD on watermelon evapotranspiration characteristics and crop coefficient under sub-membrane drip irrigation. The results indicated that the daily reference crop evapotranspiration showed a sawtooth fluctuation which was extremely significantly and positively correlated with temperature, sunshine hours, and wind speed. The water consumption during the entire growing season of watermelon varied from 281-323 mm (2020) and 290-334 mm (2021), among which the phasic evapotranspiration valued the maximum during ES, accounting for 37.85% (2020) and 38.94% (2021) in total, followed in the order of VS, SS, MS, and FS. The evapotranspiration intensity of watermelon increased rapidly from SS to VS, reaching the maximum with 5.82 mm·d^-1 at ES, after which it gradually decreased. The crop coefficient at SS, VS, FS, ES, and MS varied from 0.400 to 0.477, from 0.550 to 0.771, from 0.824 to 1.168, from 0.910 to 1.247, and from 0.541 to 0.803, respectively. Any period of WD reduced the crop coefficient and evapotranspiration intensity of watermelon at that stage. And then the relationship between LAI and crop coefficient can be characterized better by an exponential regression, thereby establishing a model for estimating the evapotranspiration of watermelon with a Nash efficiency coefficient of 0.9 or more. Hence, the water demand characteristics of oasis watermelon differ significantly during different growth stages, and reasonable irrigation and water control management measures need to be conducted in conjunction with the water requirements features of each growth stage. Also, this work aims to provide a theoretical basis for the irrigation management of watermelon under sub-membrane drip irrigation in desert oases of cold and arid environments.

The effect of the vertical gradients of photosynthetic parameters on the CO2 assimilation and transpiration of a Panamanian tropical forest

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama and then quantified the impact of the observed gradients on simulated canopy-scale CO₂ and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy-scale simulations of water vapor and CO₂ exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.

Optimal planting pattern of cotton is regulated by irrigation amount under mulch drip irrigation

Objective

It is of great importance to explore agronomic management measures for water conservation and cotton yield in arid areas.

Methods

A four-year field experiment was conducted to evaluate cotton yield and soil water consumption under four row spacing configurations (high/low density with 66+10 cm wide, narrow row spacing, RS_66+10H and RS_66+10L; high/low density with 76 cm equal row spacing, RS₇₆H and RS₇₆L) and two irrigation amounts (CI:conventional drip irrigation; LI:limited drip irrigation) during the growing seasons in Shihezi, Xinjiang.

Results

A quadratic relationship was observed between the maximum LAI (LAI_max) and seed yield. Canopy apparent transpiration rate(CAT), daily water consumption intensity (DWCI) and crop evapotranspiration (ET_C) were positively and linearly correlated with LAI. The seed yields, lint yields, and ET_C under CI were 6.6-18.3%,7.1-20.8% and 22.9-32.6%higher than those observed under LI, respectively. The RS_66+10H under CI had the highest seed and lint yields. RS₇₆L had an optimum LAI_max range, which ensured a higher canopy apparent photosynthesis and daily dry matter accumulation and reached the same yield level as RS_66+10H; however, soil water consumption in RS₇₆L was reduced ET_C by 51-60 mm at a depth of 20-60 cm at a radius of 19-38 cm from the cotton row,and water use efficiency increased by 5.6-8.3%compared to RS_66+10H under CI.

Conclusion

A 5.0<LAI_max<5.5 is optimum for cotton production in northern Xinjiang, and RS₇₆L under CI is recommended for high yield and can further reduce water consumption. Under LI, the seed and lint yield of RS_66+10H were 3.7-6.0% and 4.6-6.9% higher than those of RS₇₆L, respectively. In addition, high-density planting can exploit the potential of soil water to increase cotton yields under water shortage conditions.

Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features

Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R² = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.

Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean

Improvements in genetics, technology, and agricultural intensification have increased soybean yields; however, adverse climate conditions may prevent these gains from being fully realized in the future. Higher growing season temperatures reduce soybean yields in key production regions including the US Midwest, and better understanding of the developmental and physiological mechanisms that constrain soybean yield under high temperature conditions is needed. This study tested the response of two soybean cultivars to four elevated temperature treatments (+1.7, +2.6, +3.6, and +4.8 °C) in the field over three growing seasons and identified threshold temperatures for response and linear versus non-linear trait responses to temperature. Yield declined non-linearly to temperature, with decreases apparent when canopy temperature exceeded 20.9 °C for the locally adapted cultivar and 22.7°C for a cultivar adapted to more southern locations. While stem node number increased with increasing temperature, leaf area index decreased substantially. Pod production, seed size, and harvest index significantly decreased with increasing temperature. The seasonal average temperature of even the mildest treatment exceeded the threshold temperatures for yield loss, emphasizing the importance of improving temperature tolerance in soybean germplasm with intensifying climate change.

Effects of nitrogen application on winter wheat growth, water use, and yield under different shallow groundwater depths

Shallow groundwater plays a vital role in physiology morphological attributes, water use, and yield production of winter wheat, but little is known of its interaction with nitrogen (N) application. We aimed to explore the effects of N fertilization rate and shallow groundwater table depth (WTD) on winter wheat growth attributes, yield, and water use. Experiments were carried out in micro-lysimeters at WTD of 0.6, 0.9, 1.2, and 1.5 m with 0, 150, 240, and 300 kg/ha N application levels for the winter wheat (Triticum aestivum L.). The results showed that there was an optimum groundwater table depth (Op-wtd), in which the growth attributes, groundwater consumption (GC), yield, and water use efficiency (WUE) under each N application rate were maximum, and the Op-wtd decreased with the increase in N application. The Op-wtd corresponding to the higher velocity of groundwater consumption (Gv) appeared at the late jointing stage, which was significantly higher than other WTD treatments under the same N fertilization. WTD significantly affected the Gv during the seeding to the regreening stage and maturity stage; the interaction of N application, WTD, and N application was significant from the jointing to the filling stage. The GC, leaf area index (LAI), and yield increased with an increase of N application at 0.6-0.9-m depth-for example, the yield and the WUE of the NF300 treatment with 0.6-m depth were significantly higher than those of the NF150-NF240 treatment at 20.51%, and 14.81%, respectively. At 1.2-1.5-m depth, the N application amount exceeding 150-240 kg/ha was not conducive to wheat growth, groundwater use, grain yield, and WUE. The yield and the WUE of 150-kg/ha treatment were 15.02% and 10.67% higher than those of 240-300-kg/ha treatment at 1.2-m depth significantly. The optimum N application rate corresponding to yield indicated a tendency to decrease with the WTD increase. Considering the winter wheat growth attributes, GC, yield, and WUE, application of 150-240 kg/ha N was recommended in our experiment.

Effects of field soil warming on the growth and physiology of Juglans mandshurica seedlings

Global climate change will increase surface soil temperature, with consequences on plant seedling growth and population dynamics. In this study, we carried out a field experiment to investigate the effects of 2 ℃ soil warming on the growth and physiological characteristics of 1- and 2-year-old seedlings of a dominant tree species in broadleaved Korean pine forest, Juglans mandshurica. The results showed that soil warming significantly increased basal diameter, root length, total leaf area, leaf dry weight, root dry weight, total biomass, apparent photosynthetic electron transfer rate (ETR), PSⅡ actual photochemical efficiency (ΦPSⅡ), and apparent photosynthetic electrophotochemical quenching coefficient (qP) of 1-year-old seedlings by 18.3%, 66.7%, 94.4%, 105.9%, 95.8%, 37.8%, 89.5%, 100.0%, and 71.4%, respectively. Soil warming significantly increased basal diameter, total leaf area, leaf dry weight, total biomass, leaf superoxide dismutase activity, peroxidase activity, catalase activity and free proline content, ETR, ΦPSⅡ, and qP of 2-year-old seedlings by 12.5%, 180.5%, 97.3%, 42.5%, 23.9%, 20.4%, 14.9%, 20.7%, 66.7%, 283.3% and 284.6%, respectively. There was an interaction between seedling age and soil warming on the root-shoot ratio and the ΦPSⅡ and qP in chlorophyll fluorescence parameters, in that soil warming significantly reduced the root-shoot ratio of 2-year-old seedlings and that the increase of chlorophyll fluorescence parameters of 2-year-old seedlings (4.1-4.6 times) was much higher than that of 1-year-old seedlings (1.5-1.8 times). Soil warming of 2 ℃ was beneficial to the growth of 1- and 2-year-old J. mandshurica seedlings and increased their regeneration potential. In particular, 2-year-old J. mandshurica seedlings responded to soil warming by increasing leaf area, improving leaf photochemical efficiency, and enhancing protective enzyme activity to increase resistance.

Optimum Nitrogen Application Acclimatizes Root Morpho-Physiological Traits and Yield Potential in Rice under Subtropical Conditions

Nitrogen (N) is a highly essential macronutrient for plant root growth and grain yield (GY). To assess the relationship among N, root traits, and the yield of boro (dry season irrigated) rice, a pot experiment was performed in the Department of Agronomy, Bangladesh Agricultural University, Mymensingh, Bangladesh, during the boro rice season of 2020-2021. Three boro rice varieties, namely BRRI dhan29, Hira-2, and Binadhan-10, were planted at four N doses: 0 kg ha^-1 (N₀), 70 kg ha^-1 (N₇₀), 140 kg ha^-1 (N₁₄₀), and 210 kg ha^-1 (N₂₁₀). The experiment was conducted following a completely randomized design with three replicates. The varieties were evaluated for root number (RN), root length (RL), root volume (RV), root porosity (RP), leaf area index (LAI), total dry matter (TDM), and yield. The results indicated that the Binadhan-10, Hira-2, and BRRI dhan29 varieties produced better root characteristics under at the N₁₄₀ and N₂₁₀ levels. A substantial positive association was noticed between the grain yield and the root traits, except for root porosity. Based on the root traits and growth dynamics, Binadhan-10 performed the best at the N₁₄₀ level and produced the highest grain yield (26.96 g pot^-1), followed by Hira-2 (26.35 g pot^-1) and BRRI dhan29 (25.90 g pot^-1).

The Relationship between Perceptions and Objective Measures of Greenness

Exposure to greenness has been studied through objective measures of remote visualization of greenspace; however, the link to how individuals interpret spaces as green is missing. We examined the associations between three objective greenspace measures with perceptions of greenness. We used a subsample (n = 175; 2018-2019) from an environmental cardiovascular risk cohort to investigate perceptions of residential greenness. Participants completed a 17-item survey electronically. Objective measurements of greenness within 300 m buffer around participants home included normalized difference vegetation index (NDVI), tree canopy and leaf area index. Principal component analysis reduced the perceived greenspaces to three dimensions reflecting natural vegetation, tree cover and built greenspace such as parks. Our results suggest significant positive associations between NDVI, tree canopy and leaf area and perceived greenness reflecting playgrounds; also, associations between tree canopy and perceived greenness reflecting tree cover. These findings indicate that the most used objective greenness measure, NDVI, as well as tree canopy and leaf area may most align with perceptions of parks, whereas tree canopy alone captures individuals' perceptions of tree cover. This highlights the need for research to understand the complexity of green metrics and careful interpretation of data based on the use of subjective or objective measures of greenness.

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition's geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R C V 2 = 0.67 and RMSE _CV = 0.88 m² m^-2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloudprone agri-environments.

Comparative analysis of farmer practices and high yield experiments: Farmers could get more maize yield from maize-soybean relay intercropping through high density cultivation of maize

Intercropping is a high-yield, resource-efficient planting method. There is a large gap between actual yield and potential yield at farmer's field. Their actual yield of intercropped maize remains unclear under low solar radiation-area, whether this yield can be improved, and if so, what are the underlying mechanism for increasing yield? In the present study, we collected the field management and yield data of intercropping maize by conducting a survey comprising 300 farmer households in 2016-2017. Subsequently, based on surveyed data, we designed an experiment including a high density planting (Dense cultivation and high N fertilization with plough tillage; DC) and normal farmer practice (Common cultivation; CC) to analyze the yield, canopy structure, light interception, photosynthetic parameters, and photosynthetic productivity. Most farmers preferred rotary tillage with a low planting density and N fertilization. Survey data showed that farmer yield ranged between 4-6 Mg ha^-1, with highest yield recorded at 10-12 Mg ha^-1, suggesting a possibility for yield improvement by improved cropping practices. Results from high density experiment showed that the two-years average yield for DC was 28.8% higher than the CC. Compared to CC, the lower angle between stem and leaf (LA) and higher leaf area index (LAI) in DC resulted in higher light interception in middle canopy and increased the photosynthetic productivity under DC. Moreover, in upper and lower canopies, the average activity of phosphoenolpyruvate (PEP) carboxylase was 70% higher in DC than CC. Briefly, increase in LAI and high Pn improved both light interception and photosynthetic productivity, thereby mediating an increase in the maize yield. Overall, these results indicated that farmer's yields on average can be increased by 2.1 Mg ha^-1 by increasing planting density and N fertilization, under plough tillage.

Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub

Leaf trait variation enables plants to utilize large gradients of light availability that exist across canopies of high leaf area index (LAI), allowing for greater net carbon gain while reducing light availability for understory competitors. While these canopy dynamics are well understood in forest ecosystems, studies of canopy structure of woody shrubs in grasslands are lacking. To evaluate the investment strategy used by these shrubs, we investigated the vertical distribution of leaf traits and physiology across canopies of Cornus drummondii, the predominant woody encroaching shrub in the Kansas tallgrass prairie. We also examined the impact of disturbance by browsing and grazing on these factors. Our results reveal that leaf mass per area (LMA) and leaf nitrogen per area (Na) varied approximately threefold across canopies of C. drummondii, resulting in major differences in the physiological functioning of leaves. High LMA leaves had high photosynthetic capacity, while low LMA leaves had a novel strategy for maintaining light compensation points below ambient light levels. The vertical allocation of leaf traits in C. drummondii canopies was also modified in response to browsing, which increased light availability at deeper canopy depths. As a result, LMA and Na increased at lower canopy depths, leading to a greater photosynthetic capacity deeper in browsed canopies compared to control canopies. This response, along with increased light availability, facilitated greater photosynthesis and resource-use efficiency deeper in browsed canopies compared to control canopies. Our results illustrate how C. drummondii facilitates high LAI canopies and a compensatory growth response to browsing-both of which are key factors contributing to the success of C. drummondii and other species responsible for grassland woody encroachment.

Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use

Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on-ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor-based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1-1.1 and 0.3-0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14-35 mm mo^-1 and 10-82 g m^-2 mo^-1 are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation-informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states.

Effects of Living Mulch and Branches Mulching on Soil Moisture, Temperature and Growth of Rain-Fed Jujube Trees

The influence of different mulching measures on soil moisture, soil temperature, and crop growth was investigated during the jujube growing season in rain-fed jujube orchards using micro-plot experiments. The mulching treatments included clean tillage (CT, control treatment), jujube branches mulching (JBM), and white clover planting (WCP). The results revealed that: (1) The average soil moisture content of JBM was greater than that of CT by 3.76% and 2.34%, respectively, during the 2013 and 2014 jujube growth periods, and its soil water deficit was minimal in each soil layer from 0 to 70 cm. WCP had the greatest soil water deficit. The average soil moisture content of the 0−70 cm soil layer in WCP was 3.88% and 5.55% lower than that in CT during the 2013 and 2014 jujube growth seasons, respectively (p < 0.05). (2) JBM had the highest annual average soil moisture content in each soil layer from 0 to 70 cm, followed by CT, while WCP had the lowest. White clover and jujube competed for water in the 20−40 cm soil layer, and JBM had the lowest variation in soil moisture. (3) Mulching with jujube branches and planting white clover could both control the temperature of the 0−25 cm soil layer and narrow the daily temperature range, with JBM being the least affected by air temperature. (4) Jujube’s leaf area index and stem diameter increase in JBM were both significantly greater than in CT and WCP. In conclusion, using pruned jujube branches as surface mulch is appropriate for rain-fed jujube orchards because it can preserve soil moisture, regulate soil temperature, and promote jujube growth.

Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop's phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R² = 0.92, RMSE = 0.43 m² m^-2, CCC: R² = 0.80, RMSE = 0.27 g m^-2 and VWC: R² = 0.75, RMSE = 416 g m^-2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.

Predicting resilience through the lens of competing adjustments to vegetation function

There is a pressing need to better understand ecosystem resilience to droughts and heatwaves. Eco-evolutionary optimization approaches have been proposed as means to build this understanding in land surface models and improve their predictive capability, but competing approaches are yet to be tested together. Here, we coupled approaches that optimize canopy gas exchange and leaf nitrogen investment, respectively, extending both approaches to account for hydraulic impairment. We assessed model predictions using observations from a native Eucalyptus woodland that experienced repeated droughts and heatwaves between 2013 and 2020, whilst exposed to an elevated [CO₂ ] treatment. Our combined approaches improved predictions of transpiration and enhanced the simulated magnitude of the CO₂ fertilization effect on gross primary productivity. The competing approaches also worked consistently along axes of change in soil moisture, leaf area, and [CO₂ ]. Despite predictions of a significant percentage loss of hydraulic conductivity due to embolism (PLC) in 2013, 2014, 2016, and 2017 (99th percentile PLC > 45%), simulated hydraulic legacy effects were small and short-lived (2 months). Our analysis suggests that leaf shedding and/or suppressed foliage growth formed a strategy to mitigate drought risk. Accounting for foliage responses to water availability has the potential to improve model predictions of ecosystem resilience.

Estimation of Leaf Area Index with a Multi-Channel Spectral Micro-Sensor for Wireless Sensing Networks

The leaf area index (LAI) is a key parameter in the context of monitoring the development of tree crowns and plants in general. As parameters such as carbon assimilation, environmental stress on carbon, and the water fluxes within tree canopies are correlated to the leaves surface, this parameter is essential for understanding and modeling ecological processes. However, its continuous monitoring using manual state-of-the-art measurement instruments is still challenging. To address this challenge, we present an innovative sensor concept to obtain the LAI based on the cheap and easy to integrate multi-channel spectral sensor AS7341. Additionally, we present a method for processing and filtering the gathered data, which enables very high accuracy measurements with an nRMSE of only 0.098, compared to the manually-operated state-of-the-art instrument LAI-2200C (LiCor). The sensor that is embedded on a sensor node has been tested in long-term experiments, proving its suitability for continuous deployment over an entire season. It permits the estimation of both the plant area index (PAI) and leaf area index (LAI) and provides the first wireless system that obtains the LAI solely powered by solar cells. Its energy autonomy and wireless connectivity make it suitable for a massive deployment over large areas and at different levels of the tree crown. It may be upgraded to allow the parallel measurement of photosynthetic active radiation (PAR) and light quality, relevant parameters for monitoring processes within tree canopies.

Plant Population and Row Spacing Affects Growth and Yield of Rainfed Maize in Semi-arid Environments

Increased tolerance to competition for soil resources of modern maize (Zea mays L.) hybrids increases soil resource use efficiency and yield. Yet little information is available on the relationship between maize population density and yield under no-tillage in semi-arid environments. A 2-year field trial was conducted in South Africa during the 2017/2018 (Season 1) and 2018/2019 (Season 2) production seasons to evaluate growth and water use productivity of rainfed maize established at seven diverse plant population (20,000-60,000 plants ha^-1) and row spacing (0.52 and 0.76 m) configurations. In Season 1, light interception was 6.8% greater at 0.76 m row spacing compared to 0.52 m row spacing (p < 0.05). In Season 2, despite dry and hot growing conditions, a well-developed leaf canopy cover was present at 0.52 m row spacing indicating a 10.4% greater intercepted photosynthetically active radiation (IPAR) compared to 0.76 m row spacing. In Season 1, with more uniform rainfall distribution, no biomass or yield benefits were found with increased plant population, except at 50,000 plants ha^-1 at 0.76 m row spacing. In Season 2, plant populations at 0.76 m row spacing out-yielded any given plant population at 0.52 m row spacing. The optimal plant population and row spacing will ultimately be a compromise between obtaining high maize grain yield and minimizing the potential for crop failure in semi-arid environments.

Evaluating the Impact of Summer Drought on Vegetation Growth Using Space-Based Solar-Induced Chlorophyll Fluorescence Across Extensive Spatial Measures

Drought is the primary and dominant natural cause of stress on vegetation, and thus, it needs our full attention. Current understanding of drought across extensive spatial measures, around the world, is considerably limited. As case studies to evaluate the feasibility of utilizing space-based solar-induced chlorophyll fluorescence (SIF) across extensive spatial measures, here, we have used data from 2007 to 2017 in Heilongjiang and Jiangsu provinces of China. The onset of the 2015 drought was accompanied by a substantial response of SIF from vegetation in both the provinces; these data were associated with changes in soil moisture, standardized precipitation evapotranspiration index, and emissivity. Our findings suggest that SIF can effectively provide the spatial and temporal progress of drought, as inferred through substantial associations with SIF normalized by absorbed photosynthetically active radiation (related to Φ_F) and by photosynthetically active radiation (SIF_par). For the depiction of onset to drought, SIF, Φ_F, and SIF_par provide a significant association and a quicker response than the leaf area index and the normalized difference vegetation index. Furthermore, we found that the correlation between gross primary productivity and SIF is highly substantial in both Heilongjiang (R² = 0.85, p < 0.001) and Jiangsu (R² = 0.75, p < 0.001) during the drought period. Our results indicate that continuing evaluation from space-based SIF can indeed provide an understanding of the seasonal differences in vegetation for evaluating the impact of drought across extensive spatial measures.

Does growing atmospheric CO2 explain increasing carbon sink in a boreal coniferous forest?

The terrestrial net ecosystem productivity (NEP) has increased during the past three decades, but the mechanisms responsible are still unclear. We analyzed 17 years (2001-2017) of eddy-covariance measurements of NEP, evapotranspiration (ET) and light and water use efficiency from a boreal coniferous forest in Southern Finland for trends and inter-annual variability (IAV). The forest was a mean annual carbon sink (252 [ ± 42] gC m-2a-1 ), and NEP increased at rate +6.4-7.0 gC m-2a-1 (or ca. +2.5% a-1 ) during the period. This was attributed to the increasing gross-primary productivity GPP and occurred without detectable change in ET. The start of annual carbon uptake period was advanced by 0.7 d a-1 , and increase in GPP and NEP outside the main growing season contributed ca. one-third and one-fourth of the annual trend, respectively. Meteorological factors were responsible for the IAV of fluxes but did not explain the long-term trends. The growing season GPP trend was strongest in ample light during the peak growing season. Using a multi-layer ecosystem model, we showed that direct CO2 fertilization effect diminishes when moving from leaf to ecosystem, and only 30-40% of the observed ecosystem GPP increase could be attributed to CO2 . The increasing trend in leaf-area index (LAI), stimulated by forest thinning in 2002, was the main driver of the enhanced GPP and NEP of the mid-rotation managed forest. It also compensated for the decrease of mean leaf stomatal conductance with increasing CO2 and LAI, explaining the apparent proportionality between observed GPP and CO2 trends. The results emphasize that attributing trends to their physical and physiological drivers is challenged by strong IAV, and uncertainty of LAI and species composition changes due to the dynamic flux footprint. The results enlighten the underlying mechanisms responsible for the increasing terrestrial carbon uptake in the boreal zone.

Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation

Aboveground biomass (AGB) and leaf area index (LAI) are important indicators to measure crop growth and development. Rapid estimation of AGB and LAI is of great significance for monitoring crop growth and agricultural site-specific management decision-making. As a fast and non-destructive detection method, unmanned aerial vehicle (UAV)-based imaging technologies provide a new way for crop growth monitoring. This study is aimed at exploring the feasibility of estimating AGB and LAI of mung bean and red bean in tea plantations by using UAV multispectral image data. The spectral parameters with high correlation with growth parameters were selected using correlation analysis. It was found that the red and near-infrared bands were sensitive bands for LAI and AGB. In addition, this study compared the performance of five machine learning methods in estimating AGB and LAI. The results showed that the support vector machine (SVM) and backpropagation neural network (BPNN) models, which can simulate non-linear relationships, had higher accuracy in estimating AGB and LAI compared with simple linear regression (LR), stepwise multiple linear regression (SMLR), and partial least-squares regression (PLSR) models. Moreover, the SVM models were better than other models in terms of fitting, consistency, and estimation accuracy, which provides higher performance for AGB (red bean: R 2 = 0.811, root-mean-square error (RMSE) = 0.137 kg/m2, normalized RMSE (NRMSE) = 0.134; mung bean: R 2 = 0.751, RMSE = 0.078 kg/m2, NRMSE = 0.100) and LAI (red bean: R 2 = 0.649, RMSE = 0.36, NRMSE = 0.123; mung bean: R 2 = 0.706, RMSE = 0.225, NRMSE = 0.081) estimation. Therefore, the crop growth parameters can be estimated quickly and accurately using the models established by combining the crop spectral information obtained by the UAV multispectral system using the SVM method. The results of this study provide valuable practical guidelines for site-specific tea plantations and the improvement of their ecological and environmental benefits.

Recognition of Maize Phenology in Sentinel Images with Machine Learning

The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran's I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.

Hyperspectral Estimation of Winter Wheat Leaf Area Index Based on Continuous Wavelet Transform and Fractional Order Differentiation

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R2 of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology.

Disturbance-accelerated succession increases the production of a temperate forest

Many secondary deciduous forests of eastern North America are approaching a transition in which mature early-successional trees are declining, resulting in an uncertain future for this century-long carbon (C) sink. We initiated the Forest Accelerated Succession Experiment (FASET) at the University of Michigan Biological Station to examine the patterns and mechanisms underlying forest C cycling following the stem girdling-induced mortality of >6,700 early-successional Populus spp. (aspen) and Betula papyrifera (paper birch). Meteorological flux tower-based C cycling observations from the 33-ha treatment forest have been paired with those from a nearby unmanipulated forest since 2008. Following over a decade of observations, we revisit our core hypothesis: that net ecosystem production (NEP) would increase following the transition to mid-late-successional species dominance due to increased canopy structural complexity. Supporting our hypothesis, NEP was stable, briefly declined, and then increased relative to the control in the decade following disturbance; however, increasing NEP was not associated with rising structural complexity but rather with a rapid 1-yr recovery of total leaf area index as mid-late-successional Acer, Quercus, and Pinus assumed canopy dominance. The transition to mid-late-successional species dominance improved carbon-use efficiency (CUE = NEP/gross primary production) as ecosystem respiration declined. Similar soil respiration rates in control and treatment forests, along with species differences in leaf physiology and the rising relative growth rates of mid-late-successional species in the treatment forest, suggest changes in aboveground plant respiration and growth were primarily responsible for increases in NEP. We conclude that deciduous forests transitioning from early to middle succession are capable of sustained or increased NEP, even when experiencing extensive tree mortality. This adds to mounting evidence that aging deciduous forests in the region will function as C sinks for decades to come.

Greening drylands despite warming consistent with carbon dioxide fertilization effect

The rising atmospheric CO₂ concentration leads to a CO₂ fertilization effect on plants-that is, increased photosynthetic uptake of CO₂ by leaves and enhanced water-use efficiency (WUE). Yet, the resulting net impact of CO₂ fertilization on plant growth and soil moisture (SM) savings at large scale is poorly understood. Drylands provide a natural experimental setting to detect the CO₂ fertilization effect on plant growth since foliage amount, plant water-use and photosynthesis are all tightly coupled in water-limited ecosystems. A long-term change in the response of leaf area index (LAI, a measure of foliage amount) to changes in SM is likely to stem from changing water demand of primary productivity in water-limited ecosystems and is a proxy for changes in WUE. Using 34-year satellite observations of LAI and SM over tropical and subtropical drylands, we identify that a 1% increment in SM leads to 0.15% (±0.008, 95% confidence interval) and 0.51% (±0.01, 95% confidence interval) increments in LAI during 1982-1998 and 1999-2015, respectively. The increasing response of LAI to SM has contributed 7.2% (±3.0%, 95% confidence interval) to total dryland greening during 1999-2015 compared to 1982-1998. The increasing response of LAI to SM is consistent with the CO₂ fertilization effect on WUE in water-limited ecosystems, indicating that a given amount of SM has sustained greater amounts of photosynthetic foliage over time. The LAI responses to changes in SM from seven dynamic global vegetation models are not always consistent with observations, highlighting the need for improved process knowledge of terrestrial ecosystem responses to rising atmospheric CO₂ concentration.

A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles

Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.

Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc-and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.

Physiological traits, yield, and yield components relationship in winter and spring canola

BACKGROUND

Understanding the relationship between physiological traits with yield and yield components is an essential step towards developing high-yielding and high-quality canola (Brassica napus L.) cultivars. This study aimed to explore further the relationship between some physiological features, including radiation use efficiency (RUE), and seed yield in canola.

RESULTS

Significant differences were found among cultivars regarding maximum leaf area index (LAI_max ) and required days to achieve maximum LAI (DLAI_max ). All cultivars obtained the minimum LAI required to intercept 90% of the incident radiation, but at different times. Some cultivars like SW102 and Shirali had the same fraction of intercepted photosynthetically active radiation (IPAR) when LAI was maximal, but SW102 had higher IPAR. This indicated that SW102 was more efficient in irradiation capacity and may have a higher photosynthesis rate when exposed to the high irradiation conditions. The average canola RUE in the current study was 3.80 and 3.63 g MJ^-1 m^-2 in 2014 and 2015, respectively. In general, the crop growth rate was higher in the first year than in the second year due to the fewer cloudy days and more incident radiation.

CONCLUSION

Results indicated that duration of growth, crop growth rate, and harvest index were crucial for enhancing biomass and seed yield. Also, a relatively high correlation was found between the RUE and DLAI_max . The cultivars that reached their maximum LAI later demonstrated higher RUE, and consequently had higher biological and seed yield. The results obtained could be used to develop an improved canola crop growth model and breeding programs. © 2021 Society of Chemical Industry.

[Effects of environment and canopy structure on stem sap flow in a Larix principis-rupprechtii plantation.]

Accurately quantifying the impacts of environmental factors and canopy structure on stem sap flow is of great significance for deeply understanding water use strategies of trees in changing environment. The stem sap flow of Larix principis-rupprechtii plantation was observed using thermal diffusion probes from June to September of 2019 in the Xiangshuihe small watershed of Liupan Mountains, with the meteorological conditions, root-zone soil water content and canopy structure being simultaneously recorded. We first analyzed the relationships of sap flow rate (J_c) to potential evapotranspiration (PET), relative extract water (REW) and canopy leaf area index (LAI), and then quantified their relative contribution to J_c. The results showed that the response of J_c to PET, LAI, and REW conformed to binomial, linearly increase and saturated exponential function, respectively. The J_c model coupling multiple factors was established as a continuous multiplication of the response functions of J_c to PET, REW and LAI, which had good simulation precision. PET was the main factor leading to the difference of J_c in different weather conditions. The average contribution rate of PET had obvious difference in sunny (with a contribution rate of 40.3%), cloudy (4.3%), and rainy days (-26.3%). PET and LAI were the leading factors affecting the J_c variation among months. The ranges of the contribution rates of PET and LAI were from -23.1% to 16.8% and from -12.3% to 11.0%, respectively. The J_c model coupling the multi-factor effect developed in this study could be used to predict J_c, and quantify the impacts of each leading factor, which had the potential to be an effective tool to analyze the water use of trees in the changing environment.

Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study's outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

Shade tree species affect gas exchange and hydraulic conductivity of cacao cultivars in an agroforestry system

Shade tolerance is a widespread strategy of rainforest understory plants. Many understory species have green young stems that may assimilate CO2 and contribute to whole-plant carbon balance. Cacao commonly grows in the shaded understory and recent emphasis has been placed on diversifying the types of trees used to shade cacao plants to achieve additional ecosystem services. We studied three agricultural cacao cultivars growing in the shade of four timber species (Cedrela odorata L., Cordia thaisiana Agostini, Swietenia macrophylla King and Tabebuia rosea (Bertol) A.D.C.) in an agroforestry system to (i) evaluate the timber species for their effect on the physiological performance of three cacao cultivars; (ii) assess the role of green stems on the carbon economy of cacao; and (iii) examine coordination between stem hydraulic conductivity and stem photosynthesis in cacao. Green young stem photosynthetic CO2 assimilation rate was positive and double leaf CO2 assimilation rate, indicating a positive contribution of green stems to the carbon economy of cacao; however, green stem area is smaller than leaf area and its relative contribution is low. Timber species showed a significant effect on leaf gas exchange traits and on stomatal conductance of cacao, and stem water-use efficiency varied among cultivars. There were no significant differences in leaf-specific hydraulic conductivity among cacao cultivars, but sapwood-specific hydraulic conductivity varied significantly among cultivars and there was an interactive effect of cacao cultivar × timber species. Hydraulic efficiency was coordinated with stem-stomatal conductance, but not with leaf-stomatal conductance or any measure of photosynthesis. We conclude that different shade regimes determined by timber species and the interaction with cacao cultivar had an important effect on most of the physiological traits and growth variables of three cacao cultivars growing in an agroforestry system. Results suggested that C. odorata is the best timber species to provide partial shade for cacao cultivars in the Barlovento region in Venezuela, regardless of cultivar origin.

Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle

Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities' green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016-2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities.

Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R ²) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index

The impacts of the changing climate on the biological world vary across latitudes, habitats and spatial scales. By contrast, the time of day at which these changes are occurring has received relatively little attention. As biologically significant organismal activities often occur at particular times of day, any asymmetry in the rate of change between the daytime and night-time will skew the climatic pressures placed on them, and this could have profound impacts on the natural world. Here we determine global spatial variation in the difference in the mean annual rate at which near-surface daytime maximum and night-time minimum temperatures and mean daytime and mean night-time cloud cover, specific humidity and precipitation have changed over land. For the years 1983-2017, we derived hourly climate data and assigned each hour as occurring during daylight or darkness. In regions that showed warming asymmetry of >0.5°C (equivalent to mean surface temperature warming during the 20th century) we investigated corresponding changes in cloud cover, specific humidity and precipitation. We then examined the proportional change in leaf area index (LAI) as one potential biological response to diel warming asymmetry. We demonstrate that where night-time temperatures increased by >0.5°C more than daytime temperatures, cloud cover, specific humidity and precipitation increased. Conversely, where daytime temperatures increased by >0.5°C more than night-time temperatures, cloud cover, specific humidity and precipitation decreased. Driven primarily by increased cloud cover resulting in a dampening of daytime temperatures, over twice the area of land has experienced night-time warming by >0.25°C more than daytime warming, and has become wetter, with important consequences for plant phenology and species interactions. Conversely, greater daytime relative to night-time warming is associated with hotter, drier conditions, increasing species vulnerability to heat stress and water budgets. This was demonstrated by a divergent response of LAI to warming asymmetry.

Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

Accelerated rate of vegetation green-up related to warming at northern high latitudes

Mid- to high-latitude vegetation are experiencing changes in their seasonal cycles as a result of climate change. Although the rates of seasonal growth from winter dormancy to summer maturity have accelerated because of changes in environmental conditions, less attention has been paid to the rate of vegetation green-up (RVG) and its dynamics, which could advance vegetation maturity. We analyzed the long-term changes in RVG and the drivers at high northern latitudes for 35 years (1982-2016) using satellite-retrieved leaf area index data based on partial correlation analyses and multivariable linear regression. The rates tended to increase significantly with time, particularly at high latitudes above 60°N in North America (1.8% mon^-1 decade^-1 , p < .01) and Eurasia (1.0% mon^-1 decade^-1 , p < .01). The increasing trend in North America was mostly because of increased heat accumulation in spring (1.2% mon^-1 decade^-1 ), that is, more rapid green-up owing to warming, with an increased carbon dioxide concentration (0.6 mon^-1 decade^-1 ). The trend in Eurasia, however, was induced by warming, increased carbon dioxide concentration, and stronger radiation, 1.0%, 0.7%, and 0.5% mon^-1 decade^-1 , respectively, but was partly counteracted by earlier pregreen-up dates of -1.2% mon^-1 decade^-1 , that is, earlier initiation of growth which counteracted green-up rate acceleration. The results suggested that warming was the predominant factor influencing the accelerated RVG at high latitudes; however, Eurasian vegetation exhibited different green-up dynamics, mitigating the influence of warming with the earlier pregreen-up. Our findings imply that high-latitude warming will drive vegetation seasonality toward rapid green-up and early maturity, leading to the reinforcement of climate-vegetation interactions; however, the consequences will be more distinct in North America owing to the absence of alleviation by earlier pregreen-up.

Decomposition analysis on soybean productivity increase under elevated CO2 using 3-D canopy model reveals synergestic effects of CO2 and light in photosynthesis

BACKGROUND AND AIMS

Understanding how climate change influences crop productivity helps in identifying new options to increase crop productivity. Soybean is the most important dicotyledonous seed crop in terms of planting area. Although the impacts of elevated atmospheric [CO2] on soybean physiology, growth and biomass accumulation have been studied extensively, the contribution of different factors to changes in season-long whole crop photosynthetic CO2 uptake [gross primary productivity (GPP)] under elevated [CO2] have not been fully quantified.

METHODS

A 3-D canopy model combining canopy 3-D architecture, ray tracing and leaf photosynthesis was built to: (1) study the impacts of elevated [CO2] on soybean GPP across a whole growing season; (2) dissect the contribution of different factors to changes in GPP; and (3) determine the extent, if any, of synergism between [CO2] and light on changes in GPP. The model was parameterized from measurements of leaf physiology and canopy architectural parameters at the soybean Free Air CO2 Enrichment (SoyFACE) facility in Champaign, Illinois.

KEY RESULTS

Using this model, we showed that both a CO2 fertilization effect and changes in canopy architecture contributed to the large increase in GPP while acclimation in photosynthetic physiological parameters to elevated [CO2] and altered leaf temperature played only a minor role in the changes in GPP. Furthermore, at early developmental stages, elevated [CO2] increased leaf area index which led to increased canopy light absorption and canopy photosynthesis. At later developmental stages, on days with high ambient light levels, the proportion of leaves in a canopy limited by Rubisco carboxylation increased from 12.2 % to 35.6 %, which led to a greater enhancement of elevated [CO2] to GPP.

CONCLUSIONS

This study develops a new method to dissect the contribution of different factors to responses of crops under climate change. We showed that there is a synergestic effect of CO2 and light on crop growth under elevated CO2 conditions.

Canopy photosynthesis of six major arable crops is enhanced under diffuse light due to canopy architecture

Diffuse radiation generally increases photosynthetic rates if total radiation is kept constant. Different hypotheses have been proposed to explain this enhancement of photosynthesis, but conclusive results over a wide range of diffuse conditions or about the effect of canopy architecture are lacking. Here, we show the response of canopy photosynthesis to different fractions of diffuse light conditions for five major arable crops (pea, potato, wheat, barley, rapeseed) and cover crops characterized by different canopy architecture. We used 13 years of flux and microclimate measurements over a field with a typical 4 year crop rotation scheme in Switzerland. We investigated the effect of diffuse light on photosynthesis over a gradient of diffuse light fractions ranging from 100% diffuse (overcast sky) to 11% diffuse light (clear-sky conditions). Gross primary productivity (GPP) increased with diffuse fraction and thus was greater under diffuse than direct light conditions if the absolute photon flux density per unit surface area was kept constant. Mean leaf tilt angle (MTA) and canopy height were found to be the best predictors of the diffuse versus direct radiation effect on photosynthesis. Climatic factors, such as the drought index and growing degree days (GDD), had a significant influence on initial quantum yield under direct but not diffuse light conditions, which depended primarily on MTA. The maximum photosynthetic rate at 2,000 µmol m^-2  s^-1 photosynthetically active radiation under direct conditions strongly depended on GDD, MTA, leaf area index (LAI) and the interaction between MTA and LAI, while under diffuse conditions, this parameter depended mostly on MTA and only to a minor extent on canopy height and their interaction. The strongest photosynthesis enhancement under diffuse light was found for wheat, barley and rapeseed, whereas the lowest was for pea. Thus, we suggest that measuring canopy architecture and diffuse radiation will greatly improve GPP estimates of global cropping systems.

Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales

Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a 3 × 3 grid (or 36 km × 36 km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29-1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.

Yield Progress in Forage Maize in NW Europe-Breeding Progress or Climate Change Effects?

Yield increases in forage maize (Zea mays L.) in NW Europe over time are well documented. The driving causes for these, however, remain unclear as there is little information available regarding the role of plant traits triggering this yield progress. Ten different hybrids from the same maturity group, which have typically been cultivated in Northwest Germany from 1970 to recent and are thus representing breeding progress over four decades, were selected for a 2-year field study in northern Germany. Traits that were investigated included leaf area index, leaf architecture, photosynthesis, radiation use efficiency, root mass, root length density, and turnover. Based on a mixed model analysis with these traits as co-variates, parameters related to leaf characteristics, in particular the number and length of leaves, the radiation use efficiency, and the leaf orientation, were identified as most influential on the yield progress (0.13 tons ha^-1 year^-1). In contrast to our hypothesis, root biomass only increased negligibly in newer hybrids compared to older ones, confirming the 'functional equilibrium' theory for high input production systems. Due to an abundance of nutrients and water in such high input systems, there is no incentive for breeders to select for carbon partitioning toward the rooting system. Breeding evidence to increase forage quality were also negligible, with no change in cob starch concentration, forage digestibility, nor NDF content and NDF digestibility. The observed increase in yield over the last four decades is due to a combination of increased temperature sums (~240 GDD within 40 years), and a higher radiation interception and radiation use efficiency. This higher radiation interception was driven by an increased leaf area index, with a higher number of leaves (16 instead of 14 leaves within 40 years) and longer leaves of newer compared to older hybrids. Future selection and adaptation of maize hybrids to changing environmental conditions are likely to be the key for high productivity and quality and for the economic viability of maize growing and expansion in Northern Europe.

Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series

Knowledge of the dynamics of dryland vegetation in recent years is essential for combating desertification. Here, we aimed to characterize nonlinear changes in dryland vegetation greenness over East Inner Mongolia, an ecotone of forest–grassland–cropland in northern China, with time series of Moderate-resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GEOV2 leaf area index (LAI) values during 2000 to 2016. Changes in the growing season EVI and LAI were detected with the polynomial change fitting method. This method characterizes nonlinear changes in time series by polynomial fitting with the highest polynomial order of three, and simultaneously provides an estimation of monotonic trends over the time series by linear fitting. The relative contribution of climatic factors (precipitation and temperature) to changes in the EVI and LAI were analyzed using linear regression. In general, we observed similar patterns of change in the EVI and LAI. Nonlinear changes in the EVI were detected for about 21% of the region, and for the LAI, the percentage of nonlinear changes was about 16%. The major types of nonlinear changes include decrease–increase, decrease–increase–decrease, and increase–decrease–increase changes. For the overall monotonic trends, very small percentages of decrease (less than 1%) and widespread increases in the EVI and LAI were detected. Furthermore, large areas where the effects of climate variation on vegetation changes were not significant were observed for all major types of change in the grasslands and rainfed croplands. Changes with an increase–decrease–increase process had large percentages of non-significant effects of climate. The further analysis of increase–decrease–increase changes in different regions suggest that the increasing phases were likely to be mainly driven by human activities, and droughts induced the decreasing phase. In particular, some increase–decrease changes were observed around the large patch of bare areas. This may be an early signal of degradation, to which more attention needs to be paid to combat desertification.

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

Understanding the interaction of plant growth with environmental conditions is crucial to increase the resilience of current cropping systems to a changing climate. Here, we investigate PhenoCams as a high-throughput approach for field phenotyping experiments to assess growth dynamics of many different genotypes simultaneously in high temporal (daily) resolution. First, we develop a method that extracts a daily phenological signal that is normalized for the different viewing geometries of the pixels within the images. Second, we investigate the extraction of the in season traits of early vigor, leaf area index (LAI), and senescence dynamic from images of a soybean (Glycine max) field phenotyping experiment and show that it is possible to rate early vigor, senescence dynamics, and track the LAI development between LAI 1 and 4.5. Third, we identify the start of green up, green peak, senescence peak, and end of senescence in the phenological signal. Fourth, we extract the timing of these points and show how this information can be used to assess the impact of phenology on harvest traits (yield, thousand kernel weight, and oil content). The results demonstrate that PhenoCams can track growth dynamics and fill the gap of high temporal monitoring in field phenotyping experiments.

Bayesian Sigmoid-Type Time Series Forecasting with Missing Data for Greenhouse Crops

This paper follows an integrated approach of Internet of Things based sensing and machine learning for crop growth prediction in agriculture. A Dynamic Bayesian Network (DBN) relates crop growth associated measurement data to environmental control data via hidden states. The measurement data, having (non-linear) sigmoid-type dynamics, are instances of the two classes observed and missing, respectively. Considering that the time series of the logistic sigmoid function is the solution to a reciprocal linear dynamic model, the exact expectation-maximization algorithm can be applied to infer the hidden states and to learn the parameters of the model. At iterative convergence, the parameter estimates are then used to derive a predictor of the measurement data several days ahead. To evaluate the performance of the proposed DBN, we followed three cultivation cycles of micro-tomatoes (MicroTom) in a mini-greenhouse. The environmental parameters were temperature, converted into Growing Degree Days (GDD), and the solar irradiance, both at a daily granularity. The measurement data were Leaf Area Index (LAI) and Evapotranspiration (ET). Although measurement data were only available scarcely, it turned out that high quality measurement data predictions were possible up to three weeks ahead.