Canopy near-infrared reflectance and terrestrial photosynthesis

Radar vegetation indices for monitoring surface vegetation: Developments, challenges, and trends

Monitoring surface vegetation is essential for environmental protection, disaster prevention, and carbon sequestration in forests. However, optical remote-sensing methods and their derivative technologies typically fail to fully meet this requirement due to constraints such as lighting and weather. Radar vegetation indices (RVIs), developed based on microwave remote-sensing data, describe the dielectric properties and morphological structure of vegetation and have been applied for vegetation monitoring at various scales. This technical review is the first to systematically summarize RVIs; it analyzes and discusses their principles, developments, categories and applications, and provides a comprehensive guide for their use. Additionally, the challenges faced by RVIs, as well as their applicability, were analyzed, and future improvements and development trends were carefully projected. The selection of RVIs must consider the type of data used, the terrain and location of the study area, and the major vegetation types. The effectiveness of RVIs applied to vegetation monitoring can be affected by various factors, including index performance, sensor type, study area, and data type and quality. These factors reduce the reliability and robustness of results, as well as guide the improvement direction of RVIs. The development of technologies, such as artificial intelligence, in remote sensing offers new possibilities for RVIs, enabling the removal of background scattering, improvement in interpretation accuracy, and reduction in application thresholds. Additionally, the development trends in high resolution, multi-polarization, multi-base, multi-dimensional, and networked synthetic aperture radar (SAR) and their satellite platforms offer data support for the next generation of RVIs. The rapid development of RVIs strongly supports the use of surface vegetation monitoring and terrestrial ecosystem research.

Mapping and analyzing the spatiotemporal dynamics of forest aboveground biomass in the ChangZhuTan urban agglomeration using a time series of Landsat images and meteorological data from 2010 to 2020

In the context of global warming, there is a substantial demand for accurate and cost-effective assessment and comprehensive understanding of forest above-ground biomass (AGB) dynamics. The timeliness and low cost of optical remote sensing data enable the mapping of large-scale forest AGB dynamics. However, mapping forest AGB with optical remote sensing data presents challenges primarily due to data uncertainty and the complex nature of the forest environment. Previous studies have demonstrated the potential of meteorological data in enhancing forest AGB mapping. To accurately capture the dynamics of forest AGB, we initially acquired Landsat datasets, digital elevation model (DEM), and meteorological datasets (temperature, humidity, and precipitation) from 2010 to 2020 in Changsha-Zhuzhou-Xiangtan urban agglomeration (CZT) located in Hunan Province, China. Spectral variables (SVs), including spectral bands and vegetation indices, were extracted from Landsat images, while meteorological variables (MVs) were derived from the monthly meteorological data using the Savitzky-Golay (S-G) filtering algorithm. Additionally, terrain variables (TVs) were also extracted from the DEM data. Three modelling models, multiple linear regression (MLR), K nearest neighbor (KNN) and random forest (RF), were developed for mapping the dynamics of forest AGB in CZT. The result revealed that MVs have the potential to improve forest AGB mapping. Integration of MVs into the models resulted in a significant reduction in root mean square error (RMSE) ranging from 32.85 % to 19.25 % compared to utilizing only SVs. However, minimal improvement was observed with the inclusion of TVs due to negligible topographic relief within the study area. An upward trend of forest AGB in CZT was observed during this period, which can be attributed to the effective implementation of government environmental protection policies. It is confirmed that the meteorological data has significant contribution to forest AGB mapping, thereby endorsing advancements in forest resource monitoring and management programs.

Capturing woody aboveground biomass historical change and potential under climate change using Landsat time-series for afforestation in dryland of China

Capturing long-term dynamics and the potential under climate change of woody aboveground biomass (AGB) is imperative for calculating and raising carbon sequestration of afforestation in dryland. It is always been a great challenge to accurately capture AGB dynamics of sparse woody vegetation mixed with grassland using only Landsat time-series, resulting in changing trajectory of woody AGB estimates cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was firstly selected and interannual time-series of composited SR was smoothed using S-G filter for each pixel, and then optimal machine learning algorithm was selected to estimate woody AGB time-series. Pixels that have reached AGB potential were detected based on the AGB changing trajectory, and the potential was spatial-temporal extended using random forest model combining environmental variables under current climate condition and CMIP6 climate models. Results show that: 1) minimum value composite based on NIRv during Jul.-Sep. is more capable of explaining woody AGB variation in dryland (R = 0.87, p < 0.01), and Random Forest (RF) model has the best performance in estimating woody AGB (R2 = 0.75, RMSE = 4.74 t·ha-1) among sis commonly used machine learning models. 2) Annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R2 = 0.97, p < 0.001) indicating explicit growth regularity of woody vegetation, which provides physiological foundation for determining woody AGB potential. 3) Woody AGB potential can be accurately simulated by RF combining environmental variables (R2 = 0.95, RMSE = 2.89 t·ha-1), and current woody AGB still has a potential of small increase, whereas the overall losses of woody AGB potential were observed in 2030, 2040 and 2050 under CMIP6 SSP-RCP scenarios.

The 30 m vegetation maps from 1990 to 2020 in the Tibetan Plateau

The Tibetan Plateau (TP) is crucial for global climate change and China's ecological security. Given recent drastic changes in vegetation from climate change and human activities, long-term vegetation monitoring is urgently required. This study produced the vegetation maps of the TP from 1990 to 2020 every ten years using random forest classifier and Landsat imagery. We selected the same stable samples and features for mapping to reduce errors between years and proposed spatial filtering to further improve the accuracy. The overall accuracy surpassed 95.00%, with all Kappa coefficients exceeding 0.95. A further assessment based on sampling sites from literature and field survey was higher than 80%. The importance ranking results indicated that in the TP, climate factors and terrain factors are the most important factors in the vegetation mapping. This study provides a method for mapping vegetation in alpine areas and data support for researching the dynamic change of vegetation on the TP and evaluating its response to climate change.

Atmospheric drought dominates changes in global water use efficiency

Water use efficiency (defined as the ratio of gross primary productivity to plant transpiration, WUET) describes the tradeoff between ecosystem carbon uptake and water loss. However, a comprehensive understanding of the impact of soil and atmospheric moisture deficits on WUET across large regions remains incomplete. Solar-induced chlorophyll fluorescence (SIF) serves as an effective signal for measuring both terrestrial vegetation photosynthesis and transpiration, thereby enabling a rapid response to changes in the physiological status of plants under water stress. The objectives of this study were to: 1) mechanistically calculate WUET using top-of-canopy SIF data and meteorological information by using the revised mechanistic light response model and the Penman-Monteith equation; 2) analyze the effects of atmospheric and soil water deficits on SIF-based WUET by using decoupled soil water content (SWC) and vapor pressure deficit (VPD); 3) evaluate estimated SIF-based WUET against data from 28 eddy covariance (EC) flux sites representing eight different vegetation types. Results indicated that the model performed well in ecosystems with dense canopies, explaining 56 % of the daily variability in EC tower-based WUET. For the years 2019-2020, the global average WUET derived from SIF was 3.49 g C/kg H2O. Notably, this value exceeded 4 g C/kg H2O in tropical rainforest regions near the equator and went beyond 5 g C/kg H2O in the high-latitude regions of the Northern Hemisphere. We found that SIF-based WUET was primarily influenced by VPD rather than SWC in over 90 % of the global vegetated area. The model used in this study increased our ability to mechanistically estimate WUET with SIF at the global scale, thereby highlighting the significance of the global response of SIF-based WUET to water stress, and also enhancing our understanding of the water‑carbon cycle in terrestrial ecosystems.

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS

To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.

Site-specific apparent optimum air temperature for vegetation photosynthesis across the globe

The apparent optimum air temperature for vegetation photosynthesis (Topt) is a key temperature parameter in terrestrial ecosystem models estimating daily photosynthesis or gross primary production (GPP, g C/m2/day). To date, most models use biome-specific Topt (Topt-biome) parameter values. Given vegetation acclimation and adaptation to local climate, site-specific Topt (Topt-site) is needed to reduce uncertainties in estimating daily GPP across the scales from site to region and the globe. Previous studies have demonstrated using the Enhanced Vegetation Index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and daytime air temperature data to estimate the Topt-site at the eddy covariance tower sites. This study used MODIS-derived EVI and ERA5 climate data to estimate and generate global Topt-site data products from 2000 to 2019. The Topt-site of individual pixels within a biome has large variation, which clearly cannot be represented accurately by the widely used Topt-biome. Therefore, using this global dataset of Topt-site estimates might significantly affect GPP simulation in current ecosystem models.

Dynamic regulation of water potential in Juniperus osteosperma mediates ecosystem carbon fluxes

Some plants exhibit dynamic hydraulic regulation, in which the strictness of hydraulic regulation (i.e. iso/anisohydry) changes in response to environmental conditions. However, the environmental controls over iso/anisohydry and the implications of flexible hydraulic regulation for plant productivity remain unknown. In Juniperus osteosperma, a drought-resistant dryland conifer, we collected a 5-month growing season time series of in situ, high temporal-resolution plant water potential ( Ψ ) and stand gross primary productivity (GPP). We quantified the stringency of hydraulic regulation associated with environmental covariates and evaluated how predawn water potential contributes to empirically predicting carbon uptake. Juniperus osteosperma showed less stringent hydraulic regulation (more anisohydric) after monsoon precipitation pulses, when soil moisture and atmospheric demand were high, and corresponded with GPP pulses. Predawn water potential matched the timing of GPP fluxes and improved estimates of GPP more strongly than soil and/or atmospheric moisture, notably resolving GPP underestimation before vegetation green-up. Flexible hydraulic regulation appears to allow J. osteosperma to prolong soil water extraction and, therefore, the period of high carbon uptake following monsoon precipitation pulses. Water potential and its dynamic regulation may account for why process-based and empirical models commonly underestimate the magnitude and temporal variability of dryland GPP.

Increased crossing of thermal stress thresholds of vegetation under global warming

Temperature extremes exert a significant influence on terrestrial ecosystems, but the precise levels at which these extremes trigger adverse shifts in vegetation productivity have remained elusive. In this study, we have derived two critical thresholds, using standard deviations (SDs) of growing-season temperature and satellite-based vegetation productivity as key indicators. Our findings reveal that, on average, vegetation productivity experiences rapid suppression when confronted with temperature anomalies exceeding 1.45 SD above the mean temperature during 2001-2018. Furthermore, at temperatures exceeding 2.98 SD above the mean, we observe the maximum level of suppression, particularly in response to the most extreme high-temperature events. When Earth System Models are driven by a future medium emission scenario, they project that mean temperatures will routinely surpass both of these critical thresholds by approximately the years 2050 and 2070, respectively. However, it is important to note that the timing of these threshold crossings exhibits spatial variation and will appear much earlier in tropical regions. Our finding highlights that restricting global warming to just 1.5°C can increase safe areas for vegetation growth by 13% compared to allowing warming to reach 2°C above preindustrial levels. This mitigation strategy helps avoid exposure to detrimental extreme temperatures that breach these thresholds. Our study underscores the pivotal role of climate mitigation policies in fostering the sustainable development of terrestrial ecosystems in a warming world.

Physiological dynamics dominate the relationship between solar-induced chlorophyll fluorescence and gross primary productivity along the nitrogen gradient in cropland

Solar-induced chlorophyll fluorescence (SIF) has been found to be robustly correlated with gross primary productivity (GPP) based on satellite datasets. However, it is unclear whether nitrogen affects the relationship between SIF and GPP at the canopy scale. Here, seasonal dynamics of SIF, GPP, vegetation physiology and canopy structure were measured synchronously throughout growing season along the nitrogen gradient in a rice paddy of China's subtropical region. Our results found that the slope of SIF against GPP was not constant, showing an increasing trend from low to high nitrogen levels. The sensitivity of SIF to nitrogen was larger than that of GPP. Nitrogen enrichment versus deficiency had asymmetrical effects on the SIF-GPP relationship. The steeper slope of SIF against GPP under high nitrogen level was mainly attributed to the promotion of canopy fluorescence efficiency (ΦF) rather than the variation of canopy fluorescence escape probability (Fesc). These results emphasize the vital role of nitrogen in exploring mechanisms underlying SIF dynamics and decoding GPP from SIF.

A satellite-derived dataset on vegetation phenology across Central Asia from 2001 to 2023

Satellite-observed land surface phenology (LSP) data have helped us better understand terrestrial ecosystem dynamics at large scales. However, uncertainties remain in comprehending LSP variations in Central Asian drylands. In this article, an LSP dataset covering Central Asia (45-100°E, 33-57°N) is introduced. This LSP dataset was produced based on Moderate Resolution Imaging Spectroradiometer (MODIS) 0.05-degree daily reflectance and land cover data. The phenological dynamics of drylands were tracked using the seasonal profiles of near-infrared reflectance of vegetation (NIRv). NIRv time series processing involved the following steps: identifying low-quality observations, smoothing the NIRv time series, and retrieving LSP metrics. In the smoothing step, a median filter was first applied to reduce spikes, after which the stationary wavelet transform (SWT) was used to smooth the NIRv time series. The SWT was performed using the Biorthogonal 1.1 wavelet at a decomposition level of 5. Seven LSP metrics were provided in this dataset, and they were categorized into the following three groups: (1) timing of key phenological events, (2) NIRv values essential for the detection of the phenological events throughout the growing season, and (3) NIRv value linked to vegetation growth state during the growing season. This LSP dataset is useful for investigating dryland ecosystem dynamics in response to climate variations and human activities across Central Asia.

A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China

Long-term, daily, and gap-free Normalized Difference Vegetation Index (NDVI) is of great significance for a better Earth system observation. However, gaps and contamination are quite severe in current daily NDVI datasets. This study developed a daily 0.05° gap-free NDVI dataset from 1981-2023 in China by combining valid data identification and spatiotemporal sequence gap-filling techniques based on the National Oceanic and Atmospheric Administration daily NDVI dataset. The generated NDVI in more than 99.91% of the study area showed an absolute percent bias (|PB|) smaller than 1% compared with the original valid data, with an overall R2 and root mean square error (RMSE) of 0.79 and 0.05, respectively. PB and RMSE between our dataset and the MODIS daily gap-filled NDVI dataset (MCD19A3CMG) during 2000 to 2023 are 7.54% and 0.1, respectively. PB between our dataset and three monthly NDVI datasets (i.e., GIMMS3g, MODIS MOD13C2, and SPOT/PROBA) are only -5.79%, 4.82%, and 2.66%, respectively. To the best of our knowledge, this is the first long-term daily gap-free NDVI in China by far.

GOES-R land surface products at Western Hemisphere eddy covariance tower locations

The terrestrial carbon cycle varies dynamically on hourly to weekly scales, making it difficult to observe. Geostationary ("weather") satellites like the Geostationary Environmental Operational Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence. The Advanced Baseline Imager (ABI) aboard GOES-R measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. However, GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature (LST), and downwelling shortwave radiation (DSR) at 314 ABI fixed grid pixels containing eddy covariance towers. We demonstrate how to best combine satellite and in-situ datasets and show how ABI attributes useful for ecosystem monitoring vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

Transition from positive to negative indirect CO2 effects on the vegetation carbon uptake

Although elevated atmospheric CO2 concentration (eCO2) has substantial indirect effects on vegetation carbon uptake via associated climate change, their dynamics remain unclear. Here we investigate how the impacts of eCO2-driven climate change on growing-season gross primary production have changed globally during 1982-2014, using satellite observations and Earth system models, and evaluate their evolution until the year 2100. We show that the initial positive effect of eCO2-induced climate change on vegetation carbon uptake has declined recently, shifting to negative in the early 21st century. Such emerging pattern appears prominent in high latitudes and occurs in combination with a decrease of direct CO2 physiological effect, ultimately resulting in a sharp reduction of the current growth benefits induced by climate warming and CO2 fertilization. Such weakening of the indirect CO2 effect can be partially attributed to the widespread land drying, and it is expected to be further exacerbated under global warming.

Stronger increases but greater variability in global mangrove productivity compared to that of adjacent terrestrial forests

Mangrove forests are a highly productive ecosystem with important potential to offset anthropogenic greenhouse gas emissions. Mangroves are expected to respond differently to climate change compared to terrestrial forests owing to their location in the tidal environment and unique ecophysiological characteristics, but the magnitude of difference remains uncertain at the global scale. Here we use satellite observations to examine mean trends and interannual variability in the productivity of global mangrove forests and nearby terrestrial evergreen broadleaf forests from 2001 to 2020. Although both types of ecosystem experienced significant recent increases in productivity, mangroves exhibited a stronger increasing trend and greater interannual variability in productivity than evergreen broadleaf forests on three-quarters of their co-occurring coasts. The difference in productivity trends is attributed to the stronger CO2 fertilization effect on mangrove photosynthesis, while the discrepancy in interannual variability is attributed to the higher sensitivities to variations in precipitation and sea level. Our results indicate that mangroves will have a faster increase in productivity than terrestrial forests in a CO2-rich future but may suffer more from deficits in water availability, highlighting a key difference between terrestrial and tidal ecosystems in their responses to climate change.

Establishing a Gross Primary Productivity Model by SIF and PRI on the Rice Canopy

Solar-induced chlorophyll fluorescence (SIF) has shown remarkable results in estimating vegetation carbon cycles, and combining it with the photochemical reflectance index (PRI) has great potential for estimating gross primary productivity (GPP). However, few studies have used SIF combined with PRI to estimate crop canopy GPP. Large temporal and spatial variability between SIF, PRI, and GPP has also been found in remote sensing observations, and the observed PRI and SIF are influenced by the ratio of different observed information (e.g., background, direct sunlit, and shaded leaves) and the physiological state of the vegetation. In this study, the PRI and SIF from a multi-angle spectrometer and the GPP from an eddy covariance system were used to assess the ability of the PRI to enhance the SIF-GPP estimation model. A semi-empirical kernel-driven Bidirectional Reflectance Distribution Function (BRDF) model was used to describe the hotspot PRI/SIF (PRIhs/SIFhs), and a modified two-leaf model was used to calculate the total canopy PRI/SIF (PRItot/SIFtot). We compared the accuracies of PRIhs/SIFhs and PRItot/SIFtot in estimating GPP. The results indicated that the PRItot+SIFtot-GPP model performed the best, with a correlation coefficient (R2) of the validation dataset of 0.88, a root mean square error (RMSE) of 3.74, and relative prediction deviation (RPD) of 2.71. The leaf area index (LAI) had a linear effect on the PRI/SIF estimation of GPP, but the temperature and vapor pressure differences had nonlinear effects. Compared with hotspot PRIhs/SIFhs, PRItot/SIFtot exhibited better consistency with GPP across different time series. Our research demonstrates that PRI is effective in enhancing SIF and PRI for estimating GPP on the rice canopy and also suggests that the two-leaf model would contribute to the vegetation index tracking the real-time crop productivity.

Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7-10.8 %, 3.7-7.5 %, and 1.6-14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products.

Regulation of biophysical drivers on carbon and water fluxes over a warm-temperate plantation in northern China

Plantations have great potential for carbon sequestration and play a vital role in the water cycle. However, it is challenging to accurately estimate the carbon and water fluxes of plantations, and the impact of biophysical drivers on the coupling of carbon and water fluxes is not well understood. Thus, we modified the phenology module of the Biome-BGC model and optimized the parameters with the aim of simulating the gross primary productivity (GPP), evapotranspiration (ET) and water use efficiency (WUE) of a warm-temperate plantation in northern China from 2009 to 2020. Photosynthetically active radiation (PAR) showed significant positive correlations on GPP and WUE during the first stage of the growing season (S1: from early April to late July). Active accumulated temperature (Taa) mainly controlled the changes in GPP and ET during the second stage (S2: between the end of July and early November). Throughout the growing season, soil water content dominated daily GPP and WUE, whereas Taa regulated ET. The optimized Biome-BGC model performed better than the original model in simulating GPP and ET. Compared with the values simulated by the original model, root mean square error decreased by 7.89 % and 15.97 % for the simulated GPP and ET, respectively, while the determination coefficient increased from 0.77 to 0.81 for simulated GPP and from 0.51 to 0.62 for simulated ET. The results of this study demonstrated that the optimized model more accurately assessed carbon sequestration and water consumption in plantations.

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

Integrating field- and remote sensing data to perceive species heterogeneity across a climate gradient

Tropical forests exhibit significant diversity and heterogeneity in species distribution. Some tree species spread abundantly, impacting the functional aspects of communities. Understanding how these facets respond to climate change is crucial. Field data from four protected areas (PAs) were combined with high-resolution Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets to extract large-scale plot data of abundant species and their functional traits. A supervised component generalized linear regression (SCGLR) model was used to correlate climate components with the distribution of abundant species across PAs. The recorded rainfall gradient influenced the proportion of PA-specific species in the observed species assemblages. Community weighted means (CWMs) of biochemical traits showed better correlation values (0.85-0.87) between observed and predicted values compared to biophysical traits (0.52-0.79). The model-based projection revealed distinct distribution responses of each abundant species to the climate gradient. Functional diversity and functional traits maps highlighted the interplay between species heterogeneity and climate. The appearance dynamics of abundant species in dark diversity across PAs demonstrated their assortment strategy in response to the climate gradient. These observations can significantly aid in the ecological management of PAs exposed to climate dynamics.

Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean

Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

Water availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA-GPP ) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA-GPP relationship (RWA-GPP ) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near-infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance-light-use efficiency GPP. We found that the 3- and 7-year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17- and 40-year time scales (7.28% and 8.23%). The long-term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA-GPP and RWA-GPP with different source products: In high-latitude regions, RWA-GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid-low latitudes. Forests had weak RWA-GPP at all time scales, shrublands showed negative RWA-GPP at long time scales, and grassland (GL) showed a positive RWA-GPP at short time scales. Globally, the EWA-GPP , whether positive or negative, enhanced significantly at 3-, 7-, and 17-year time scales. For arid and humid zones, the semi-arid and sub-humid zones experienced a faster increase in the positive EWA-GPP , whereas the humid zones experienced a faster increase in the negative EWA-GPP . At the ecosystem types, the positive EWA-GPP at a 3-year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA-GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA-GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

An abrupt shift in gross primary productivity over Eastern China-Mongolia and its inter-model diversity in land surface models

The terrestrial ecosystem in East Asia mainly consists of semi-arid regions that are sensitive to climate change. Therefore, gross primary productivity (GPP) in East Asia could be highly variable and vulnerable to climate change, which can significantly affect the local carbon budget. Here, we examine the spatial and temporal characteristics of GPP variability in East Asia and its relationship with climate factors over the last three decades. We detect an abrupt decrease in GPP over Eastern China-Mongolia region around the year 2000. This is attributed to an abrupt decrease in precipitation associated with the phase shift of the Pacific decadal oscillation (PDO). We also evaluate the reproducibility of offline land surface models to simulate these abrupt changes. Of the twelve models, eight were able to simulate this abrupt response, while the others failed due to the combination of an exaggerated CO2 fertilization effect and an underrated climate impact. For accurate prediction, it is necessary to improve the sensitivity of the GPP to changes in CO2 concentrations and the climate system.

Lagged feedback of peak season photosynthetic activities on local surface temperature in Inner Mongolia, China

Increased vegetation peak growth and phenological shifts toward spring have been observed in response to climate warming in the temperate regions. Such changes have the potential to modify warming by perturbing land‒atmosphere energy exchanges; however, the signs and magnitudes of biophysical feedback on surface temperature in different biomes are largely unknown. Here, we synthesized information from vegetation growth proxies, land surface temperature (LST), and surface energy balance factors (surface evapotranspiration (ET), albedo, and broadband emissivity (BBE)) to investigate the variations in timing (PPT) and productivity (PPmax) of seasonal peak photosynthesis and their time-lagged biophysical feedbacks to the post-season LST in Inner Mongolia (IM) during 2001-2020. We found that increased PPmax, rather than advanced PPT, exhibited a significant impact on LST, with divergent signs and magnitudes across diurnal periods and among different biomes. In the grassland biome, increased PPmax cooled both LST during daytime (LSTday) and nighttime (LSTnight) throughout the post-season period, with a more pronounced response during daytime and diminishing gradually from July to September. This cooling effect on LST was primarily attributed to enhanced ET, as evidenced by the greater effect of ET cooling than that of albedo warming and BBE cooling based on a structural equation model (SEM). In the forest biome, increased PPmax led to a symmetrical warming effect on LSTday and LSTnight, and none of the surface energy balance factors were identified as significant intermediate explanatory factors for the observed warming effect. Moreover, the responses of average LST (LSTmean) and diurnal temperature range of LST (LSTDTR) to variations in PPmax were consistent with those of LSTday at two biomes. The observations above elucidate the divergent feedback mechanisms of vegetation peak growth on LST among different biomes and diurnal cycles, which could facilitate the improvement of the realistic parameterization of surface processes in global climate models.

Structural complexity biases vegetation greenness measures

Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

Temperature extremes of 2022 reduced carbon uptake by forests in Europe

The year 2022 saw record breaking temperatures in Europe during both summer and fall. Similar to the recent 2018 drought, close to 30% (3.0 million km2) of the European continent was under severe summer drought. In 2022, the drought was located in central and southeastern Europe, contrasting the Northern-centered 2018 drought. We show, using multiple sets of observations, a reduction of net biospheric carbon uptake in summer (56-62 TgC) over the drought area. Specific sites in France even showed a widespread summertime carbon release by forests, additional to wildfires. Partial compensation (32%) for the decreased carbon uptake due to drought was offered by a warm autumn with prolonged biospheric carbon uptake. The severity of this second drought event in 5 years suggests drought-induced reduced carbon uptake to no longer be exceptional, and important to factor into Europe's developing plans for net-zero greenhouse gas emissions that rely on carbon uptake by forests.

AmeriFlux BASE data pipeline to support network growth and data sharing

AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux's diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team's quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca)

Common-garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common-garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2  = .98-.99, root mean square error [RMSE] = 0.06-0.10 m) and diameter at breast height (DBH, R2  = .71-.97, RMSE = 2.57-3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture-limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground-measured height and DBH. This phenotyping framework contributes to the analysis of common-garden trials towards a mechanistic understanding of local adaptation to climate.

Widespread and complex drought effects on vegetation physiology inferred from space

The response of vegetation physiology to drought at large spatial scales is poorly understood due to a lack of direct observations. Here, we study vegetation drought responses related to photosynthesis, evaporation, and vegetation water content using remotely sensed data, and we isolate physiological responses using a machine learning technique. We find that vegetation functional decreases are largely driven by the downregulation of vegetation physiology such as stomatal conductance and light use efficiency, with the strongest downregulation in water-limited regions. Vegetation physiological decreases in wet regions also result in a discrepancy between functional and structural changes under severe drought. We find similar patterns of physiological drought response using simulations from a soil-plant-atmosphere continuum model coupled with a radiative transfer model. Observation-derived vegetation physiological responses to drought across space are mainly controlled by aridity and additionally modulated by abnormal hydro-meteorological conditions and vegetation types. Hence, isolating and quantifying vegetation physiological responses to drought enables a better understanding of ecosystem biogeochemical and biophysical feedback in modulating climate change.

Biome-scale temperature sensitivity of ecosystem respiration revealed by atmospheric CO2 observations

The temperature sensitivity of ecosystem respiration regulates how the terrestrial carbon sink responds to a warming climate but has been difficult to constrain observationally beyond the plot scale. Here we use observations of atmospheric CO2 concentrations from a network of towers together with carbon flux estimates from state-of-the-art terrestrial biosphere models to characterize the temperature sensitivity of ecosystem respiration, as represented by the Arrhenius activation energy, over various North American biomes. We infer activation energies of 0.43 eV for North America and 0.38 eV to 0.53 eV for major biomes therein, which are substantially below those reported for plot-scale studies (approximately 0.65 eV). This discrepancy suggests that sparse plot-scale observations do not capture the spatial-scale dependence and biome specificity of the temperature sensitivity. We further show that adjusting the apparent temperature sensitivity in model estimates markedly improves their ability to represent observed atmospheric CO2 variability. This study provides observationally constrained estimates of the temperature sensitivity of ecosystem respiration directly at the biome scale and reveals that temperature sensitivities at this scale are lower than those based on earlier plot-scale studies. These findings call for additional work to assess the resilience of large-scale carbon sinks to warming.

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

Satellite solar-induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.

Improved gross primary production estimation in rice fields through integrated multi-scale methodologies

Understanding productivity in agricultural ecosystems is important, as it plays a significant role in modifying regional carbon balances and capturing carbon in the form of agricultural yield. This study in particular combines information from flux determinations using the eddy covariance (EC) methodology, process-based modeling of carbon gain, remotely (satellite) sensed vegetation indices (VIs), and field surveys to assess the gross primary production (GPP) of rice, which is a primary food crop worldwide. This study relates two major variables determining GPP. The first is leaf area index (LAI) and carboxylation capacity of the rice canopy (Vc_uptake), and the second being MODIS remotely sensed vegetation indices (VIs). Success in applying such derived relationships has allowed GPP to be remotely determined over the seasonal course of rice development. The relationship to VIs of both LAI and Vc_uptake was analyzed first by using the regression approaches commonly applied in remote sensing studies. However, the resultant GPP estimations derived from these generic models were not consistently accurate and led to a large proportion of underestimations. The new, alternative approach developed to estimate LAI and Vc_uptake uses consistent development curves for rice (i.e., relies on consistent biological regulations of plant development). The modeled GPP based on this consistent development curve for both LAI and Vc_uptake agreed with R ² from 0.76 to 0.92 (within the 95% confidence interval). The results of this study demonstrate that improved linkages between ground-based survey data, eddy flux measurements, process-based models, and remote sensing data can be constructed to estimate GPP in rice paddies. This study suggests further that the conceptual application of the consistent development curve, such as the combining of different scale measurements, has the potential to predict GPP better than the common practice of utilizing simple linear models, when seeking to estimate the critical parameters that influence carbon gain and agricultural yields.

Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems

Vegetation phenology can profoundly modulate the climate-biosphere interactions and thus plays a crucial role in regulating the terrestrial carbon cycle and the climate. However, most previous phenology studies rely on traditional vegetation indices, which are inadequate to characterize the seasonal activity of photosynthesis. Here, we generated an annual vegetation photosynthetic phenology dataset with a spatial resolution of 0.05 degrees from 2001 to 2020, using the latest gross primary productivity product based on solar-induced chlorophyll fluorescence (GOSIF-GPP). We combined smoothing splines with multiple change-point detection to retrieve the phenology metrics: start of the growing season (SOS), end of the growing season (EOS), and length of growing season (LOS) for terrestrial ecosystems above 30° N latitude (Northern Biomes). Our phenology product can be used to validate and develop phenology or carbon cycle models and monitor the climate change impacts on terrestrial ecosystems.

The magnitude and pace of photosynthetic recovery after wildfire in California ecosystems

Wildfire modifies the short- and long-term exchange of carbon between terrestrial ecosystems and the atmosphere, with impacts on ecosystem services such as carbon uptake. Dry western US forests historically experienced low-intensity, frequent fires, with patches across the landscape occupying different points in the fire-recovery trajectory. Contemporary perturbations, such as recent severe fires in California, could shift the historic stand-age distribution and impact the legacy of carbon uptake on the landscape. Here, we combine flux measurements of gross primary production (GPP) and chronosequence analysis using satellite remote sensing to investigate how the last century of fires in California impacted the dynamics of ecosystem carbon uptake on the fire-affected landscape. A GPP recovery trajectory curve of more than five thousand fires in forest ecosystems since 1919 indicated that fire reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text]([Formula: see text]) in the first year after fire, with average recovery to prefire conditions after [Formula: see text] y. The largest fires in forested ecosystems reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text] (n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly [Formula: see text] MMT CO[Formula: see text] (3-y rolling mean) in cumulative forgone carbon uptake due to the legacy of fires on the landscape, complicating the challenge of maintaining California's natural and working lands as a net carbon sink. Understanding these changes is paramount to weighing the costs and benefits associated with fuels management and ecosystem management for climate change mitigation.

Light scattering in stacked mesophyll cells results in similarity characteristic of solar spectral reflectance and transmittance of natural leaves

Solar spectral reflectance and transmittance of natural leaves exhibit dramatic similarity. To elucidate the formation mechanism and physiological significance, a radiative transfer model was constructed, and the effects of stacked mesophyll cells, chlorophyll content and leaf thickness on the visible light absorptance of the natural leaves were analyzed. Results indicated that light scattering caused by the stacked mesophyll cells is responsible for the similarity. The optical path of visible light in the natural leaves is increased with the scattering process, resulting in that the visible light transmittance is significantly reduced meanwhile the visible light reflectance is at a low level, thus the visible light absorptance tends to a maximum and the absorption of photosynthetically active radiation (PAR) by the natural leaves is significantly enhanced. Interestingly, as two key leaf functional traits affecting the absorption process of PAR, chlorophyll content and leaf thickness of the natural leaves in a certain environment show a convergent behavior, resulting in the high visible light absorptance of the natural leaves, which demonstrates the PAR utilizing strategies of the natural leaves. This work provides a new perspective for revealing the evolutionary processes and ecological strategies of natural leaves, and can be adopted to guide the improvement directions of crop photosynthesis.

Integrating multiple plant functional traits to predict ecosystem productivity

Quantifying and predicting variation in gross primary productivity (GPP) is important for accurate assessment of the ecosystem carbon budget under global change. Scaling traits to community scales for predicting ecosystem functions (i.e., GPP) remain challenging, while it is promising and well appreciated with the rapid development of trait-based ecology. In this study, we aim to integrate multiple plant traits with the recently developed trait-based productivity (TBP) theory, verify it via Bayesian structural equation modeling (SEM) and complementary independent effect analysis. We further distinguish the relative importance of different traits in explaining the variation in GPP. We apply the TBP theory based on plant community traits to a multi-trait dataset containing more than 13,000 measurements of approximately 2,500 species in Chinese forest and grassland systems. Remarkably, our SEM accurately predicts variation in annual and monthly GPP across China (R² values of 0.87 and 0.73, respectively). Plant community traits play a key role. This study shows that integrating multiple plant functional traits into the TBP theory strengthens the quantification of ecosystem primary productivity variability and further advances understanding of the trait-productivity relationship. Our findings facilitate integration of the growing plant trait data into future ecological models.

Advancing understanding of land-atmosphere interactions by breaking discipline and scale barriers

Vegetation and atmosphere processes are coupled through a myriad of interactions linking plant transpiration, carbon dioxide assimilation, turbulent transport of moisture, heat and atmospheric constituents, aerosol formation, moist convection, and precipitation. Advances in our understanding are hampered by discipline barriers and challenges in understanding the role of small spatiotemporal scales. In this perspective, we propose to study the atmosphere-ecosystem interaction as a continuum by integrating leaf to regional scales (multiscale) and integrating biochemical and physical processes (multiprocesses). The challenges ahead are (1) How do clouds and canopies affect the transferring and in-canopy penetration of radiation, thereby impacting photosynthesis and biogenic chemical transformations? (2) How is the radiative energy spatially distributed and converted into turbulent fluxes of heat, moisture, carbon, and reactive compounds? (3) How do local (leaf-canopy-clouds, 1 m to kilometers) biochemical and physical processes interact with regional meteorology and atmospheric composition (kilometers to 100 km)? (4) How can we integrate the feedbacks between cloud radiative effects and plant physiology to reduce uncertainties in our climate projections driven by regional warming and enhanced carbon dioxide levels? Our methodology integrates fine-scale explicit simulations with new observational techniques to determine the role of unresolved small-scale spatiotemporal processes in weather and climate models.

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

Accurate and timely estimation of cotton yield over large areas is essential for precision agriculture, facilitating the operation of commodity markets and guiding agronomic management practices. Remote sensing (RS) and crop models are effective means to predict cotton yield in the field. The satellite vegetation indices (VIs) can describe crop yield variations over large areas but can't take the exact environmental impact into consideration. Climate variables (CVs), the result of the influence of spatial heterogeneity in large regions, can provide environmental information for better estimation of cotton yield. In this study, the most important VIs and CVs for estimating county-level cotton yield across Xinjiang Province were screened out. We found that the VIs of canopy structure and chlorophyll contents, and the CVs of moisture, were the most significant factors for cotton growth. For yield estimation, we utilized four approaches: least absolute shrinkage and selection operator regression (LASSO), support vector regression (SVR), random forest regression (RFR) and long short-term memory (LSTM). Due to its ability to capture temporal features over the long term, LSTM performed best, with an R² of 0.76, root mean square error (RMSE) of 150 kg/ha and relative RMSE (rRMSE) of 8.67%; moreover, an additional 10% of the variance could be explained by adding CVs to the VIs. For the within-season yield estimation using LSTM, predictions made 2 months before harvest were the most accurate (R² = 0.65, RMSE = 220 kg/ha, rRMSE = 15.97%). Our study demonstrated the feasibility of yield estimation and early prediction at the county level over large cotton cultivation areas by integrating satellite and environmental data.

Optimality principles explaining divergent responses of alpine vegetation to environmental change

Recent increases in vegetation greenness over much of the world reflect increasing CO₂ globally and warming in cold areas. However, the strength of the response to both CO₂ and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite-observed peak season absorbed photosynthetically active radiation (F_max ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year^-1 in drier regions and a browning of 0.12 ± 0.08% year^-1 in wetter regions). Using an eco-evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in F_max with a correlation coefficient (r) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year^-1 ) and browning (0.07 ± 0.06% year^-1 ). We also predicted the observed reduced sensitivities of F_max to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces F_max in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO₂ stimulates vegetation growth by enhancing water-use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.

An Advanced Data Fusion Method to Improve Wetland Classification Using Multi-Source Remotely Sensed Data

The goal of this research was to improve wetland classification by fully exploiting multi-source remotely sensed data. Three distinct classifiers were designed to distinguish individual or compound wetland categories using random forest (RF) classification. They were determined, in part, to best use the available remotely sensed features in order to maximize that information and to maximize classification accuracy. The results from these classifiers were integrated according to Dempster−Shafer theory (D−S theory). The developed method was tested on data collected from a study area in Northern Alberta, Canada. The data utilized were Landsat-8 and Sentinel-2 (multi-spectral), Sentinel-1 (synthetic aperture radar—SAR), and digital elevation model (DEM). Classification of fen, bog, marsh, swamps, and upland resulted in an overall accuracy of 0.93 using the proposed methodology, an improvement of 5% when compared to a traditional classification method based on the aggregated features from these data sources. It was noted that, with the traditional method, some pixels were misclassified with a high level of confidence (>85%). Such misclassification was significantly reduced (by ~10%) by the proposed method. Results also showed that some features important in separating compound wetland classes were not considered important using the traditional method based on the RF feature selection mechanism. When used in the proposed method, these features increased the classification accuracy, which demonstrated that the proposed method provided an effective means to fully employ available data to improve wetland classification.

Estimation of the net primary productivity of winter wheat based on the near-infrared radiance of vegetation

Quantifying net primary productivity (NPP) is important for understanding the global carbon cycle and for assessing ecosystem carbon dynamics. However, uncertainties remain in NPP estimation. Using winter wheat data obtained from an experimental station in 2019, this study evaluated the ability of the near-infrared radiance of vegetation (NIR_V,Rad) to estimate NPP at different time scales and established an estimation model based on NIR_V,Rad, where NIR_V,Rad was defined as the product of the normalized difference vegetation index (NDVI) and the near-infrared radiance. The results showed that the linear relationship between NIR_V,Rad and NPP was superior to the relationship between NPP and NDVI, enhanced vegetation index-2 (EVI2), and near-infrared reflectance of vegetation (NIR_V,Ref) at each time scale (hourly, daily, and growth period). The advantage of NIR_V,Rad was more evident on the hourly scale, in which the R² of NIR_V,Rad and NPP reached 0.77, whereas the R² values of the correlation of NDVI, EVI2, and NIR_V,Ref with NPP were 0.30, 0.16, and 0.14, respectively. There existed a strong linear relationship between absorbed photosynthetically active radiation, net photosynthetic rate, leaf area index, and NIR_V,Rad, which explained the good relationship between NIR_V,Rad and NPP. Through a comparative analysis of the various models, the NIR_V,Rad model was found to have the strongest ability to estimate NPP and the R², with the measured NPP reaching 0.81. The accuracy of NIR_V,Rad provides a new method for estimating NPP and a scientific basis for estimating NPP using high-resolution satellite remote sensing data on a regional scale.

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R²) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.

Effects of Low Temperature on the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity across Different Plant Function Types

Solar-induced chlorophyll fluorescence (SIF) has been recognized as a proxy of gross primary production (GPP) across various terrestrial biomes. However, the effects of low temperature on SIF and GPP among different plant function types (PFTs) have not yet been well-explored. To gain a better understanding of the relationship between SIF and GPP, we investigated the variation in the GPP/SIF ratio in response to low-temperature conditions using satellite and tower-based datasets. Based on the TROPOMI SIF product and FLUXCOM GPP data, we found that the SIF and GPP exhibited consistent seasonal and spatial patterns, while the GPP/SIF ratio differed for different PFTs. The GPP/SIF ratio for forest types was generally higher than 10 gC·d−1·mw−1·nm·sr, whereas the GPP/SIF ratio for grass and crop types was generally lower than 10 gC·d−1·mw−1·nm·sr. In addition, there were noticeable differences in the seasonal pattern of the GPP/SIF ratio between the selected samples that experienced low-temperature stress (below 10 °C, defined as group A) and those that grew under relatively warm conditions (above 10 °C throughout the year, defined as group B). The GPP/SIF ratio for group A generally exhibited a “hump-shaped” seasonal pattern, and that for group B showed a slightly “bowl-shaped” seasonal pattern, which means it is important to consider the effects of temperature on the SIF-GPP relationship. Through linear regression and correlation analysis, we demonstrate that there was a positive correlation between the GPP/SIF ratio and temperature for group A, with a wide temperature range including low-temperature conditions, indicating that, in this case, temperature affected the SIF–GPP relationship; however, for group B—with a temperature higher than 10 °C throughout the year—the GPP/SIF ratio was not consistently affected by temperature. The response of GPP/SIF to low temperature stress was confirmed by tower-based observations at a C3 cropland (C3CRO) site and a boreal evergreen needleleaf forest (BoENF) site. Although the relationship between the GPP/SIF ratio and temperature differed among PFTs, the GPP/SIF ratio decreased under low-temperature conditions for PFTs. Therefore, the GPP/SIF ratio was not constant and was largely influenced by low temperature for different PFTs, thus highlighting the importance of incorporating temperature into SIF-based GPP estimation.

Disentangling Soil, Shade, and Tree Canopy Contributions to Mixed Satellite Vegetation Indices in a Sparse Dry Forest

Remote sensing (RS) for vegetation monitoring can involve mixed pixels with contributions from vegetation and background surfaces, causing biases in signals and their interpretations, especially in low-density forests. In a case study in the semi-arid Yatir forest in Israel, we observed a mismatch between satellite (Landsat 8 surface product) and tower-based (Skye sensor) multispectral data and contrasting seasonal cycles in near-infrared (NIR) reflectance. We tested the hypothesis that this mismatch was due to the different fractional contributions of the various surface components and their unique reflectance. Employing an unmanned aerial vehicle (UAV), we obtained high-resolution multispectral images over selected forest plots and estimated the fraction, reflectance, and seasonal cycle of the three main surface components (canopy, shade, and sunlit soil). We determined that the Landsat 8 data were dominated by soil signals (70%), while the tower-based data were dominated by canopy signals (95%). We then developed a procedure to resolve the canopy (i.e., tree foliage) normalized difference vegetation index (NDVI) from the mixed satellite data. The retrieved and corrected canopy-only data resolved the original mismatch and indicated that the spatial variations in Landsat 8 NDVI were due to differences in stand density, while the canopy-only NDVI was spatially uniform, providing confidence in the local flux tower measurements.

The Response of Vegetation to Regional Climate Change on the Tibetan Plateau Based on Remote Sensing Products and the Dynamic Global Vegetation Model

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation.

Combining NDVI, PRI and the quantum yield of solar-induced fluorescence improves estimations of carbon fluxes in deciduous and evergreen forests

We used automated spectroradiometers to continuously monitor changes in the optical parameters of phenological and photosynthetic traits in beech and spruce forests. We examined seasonal variations in the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and solar-induced fluorescence in the oxygen A band (SIFA) that was estimated using a 3-FLD discrimination method from radiance data. The optical parameters tracked the activation and cessation of photosynthesis in spring and autumn. Data at photon fluxes >1200 μmol m^-2 s^-1 during extended noon hours were used to link the seasonal PRI and SIFA variations to the dynamics of photosynthesis. Seasonal PRI was significantly correlated with photosynthetic light-use efficiency (LUE) with R² values of 0.66 and 0.48 for the measurements in beech and spruce forests, respectively. SIFA emissions were significantly correlated with the gross primary production (GPP) of the evergreen spruce forest (R² = 0.47), but R² was only 0.13 when measured in the beech forest. The correlations between the optical parameters and GPP or LUE, however, tended to be lower when using a dataset with constant NDVI. Introducing an equation combining NDVI, PRI, and the quantum yield of SIFA emission increased R² for LUE estimation to 0.77 in the spruce forest and 0.63 in the beech forest. GPP was estimated from the parametric equation with improved accuracy reaching R² = 0.53 and RMSE = 5.95 μmol CO₂ m^-2 s^-1 in spruce forest and R² = 0.58 and RMSE = 5.23 μmol CO₂ m^-2 s^-1 in beech forest. Parametric equations were more efficient in estimating photosynthesis in datasets that consisted of an entire season's data. By combining NDVI, PRI and the quantum yield of SIFA, we could thus substantially improve estimations of carbon fluxes in diverse deciduous and evergreen canopies.