High-Throughput Prediction of Whole Season Green Area Index in Winter Wheat With an Airborne Multispectral Sensor

INTRODUCTION

In recent decades, the interest has grown to quantify the green area index as one of the key characteristics of crop canopies (e.g. for modelling transpiration, light interception, growth). The approach of estimating green area index based on multispectral reflection data from unmanned airborne vehicles with lightweight sensors might have the potential to deliver data with sufficient accuracy and high throughput during the whole season.

MATERIALS AND METHODS

We therefore examined the applicability of a recently launched drone-based multispectral system (Sequoia, Parrot) for the prediction of whole season green area index in winter wheat, with data from field trials in Northern Germany (2017, 2018 and 2019). The explanatory power of different modeling approaches to predict green area index based on multispectral data was tested: linear and non-linear regression models, multivariate techniques, and machine learning algorithms. Further, different predictors were implemented in these models: multispectral data as raw bands and as ratios. Additionally, a new approach for the evaluation of green area index predictions during senescence is introduced. It is shown that a robust calibration during growth phase is applicable during senescence as well.

RESULTS AND DISCUSSION

A linear model which includes all four wavebands provided by the sensor in three ratios (VIQUO) and a Support Vector Machine (SVM) algorithm allow a reliable and sufficiently accurate whole season prediction. The VIQUO-model is recommended as the best model, as it is precise but still relatively simple, thus easier to communicate and to apply than the SVM. The integrated values of predicted green area indices in an independent trial are highly correlated with their final biomass (R²: VIQUO = 0.84, SVM = 0.85) which represents the process of radiation interception, one of the determining factors of growths. This is an indicator for both, a robust model calibration and a high potential of the tested multispectral system for agricultural research and crop management.

Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data

Accurate estimations of the vertical leaf nitrogen (N) distribution within a rice canopy is helpful for understanding the nutrient supply and demand of various functional leaf layers of rice and for improving the predictions of rice productivity. A two-year field experiment using different rice varieties, N rates, and planting densities was performed to investigate the vertical distribution of the leaf nitrogen concentration (LNC, %) within the rice canopy, the relationship between the LNC in different leaf layers (LNCLi, i = 1, 2, 3, 4), and the relationship between the LNCLi and the LNC at the canopy level (LNCCanopy). A vertical distribution model of the LNC was constructed based on the relative canopy height. Furthermore, the relationship between different vegetation indices (VIs) and the LNCCanopy, the LNCLi, and the LNC vertical distribution model parameters were studied. We also compared the following three methods for estimating the LNC in different leaf layers in rice canopy: (1) estimating the LNCCanopy by VIs and then estimating the LNCLi based on the relationship between the LNCLi and LNCCanopy; (2) estimating the LNC in any leaf layer of the rice canopy by VIs, inputting the result into the LNC vertical distribution model to obtain the parameters of the model, and then estimating the LNCLi using the LNC vertical distribution model; (3) estimating the model parameters by using VIs directly and then estimating the LNCLi by the LNC vertical distribution model. The results showed that the LNC in the bottom of rice canopy was more susceptible to different N rates, and changes in the LNC with the relative canopy height could be simulated by an exponential model. Vegetation indices could estimate the LNC at the top of rice canopy. R705/(R717+R491) (R2 = 0.763) and the renormalized difference vegetation index (RDVI) (1340, 730) (R2 = 0.747) were able to estimate the parameter "a" of the LNC vertical distribution model in indica rice and japonica rice, respectively. In addition, method (2) was the best choice for estimating the LNCLi (R2 = 0.768, 0.700, 0.623, and 0.549 for LNCL1, LNCL2, LNCL3, and LNCL4, respectively). These results provide technical support for the rapid, accurate, and non-destructive identification of the vertical distribution of nitrogen in rice canopies.

Effect of Cultivar on Chlorophyll Meter and Canopy Reflectance Measurements in Cucumber

Optical sensors can be used to assess crop N status to assist with N fertilizer management. Differences between cultivars may affect optical sensor measurement. Cultivar effects on measurements made with the SPAD-502 (Soil Plant Analysis Development) meter and the MC-100 (Chlorophyll Concentration Meter), and of several vegetation indices measured with the Crop Circle ACS470 canopy reflectance sensor, were assessed. A cucumber (Cucumis sativus L.) crop was grown in a greenhouse, with three cultivars. Each cultivar received three N treatments, of increasing N concentration, being deficient (N1), sufficient (N2) and excessive (N3). There were significant differences between cultivars in the measurements made with both chlorophyll meters, particularly when N supply was sufficient and excessive (N2 and N3 treatments, respectively). There were no consistent differences between cultivars in vegetation indices. Optical sensor measurements were strongly linearly related to leaf N content in each of the three cultivars. The lack of a consistent effect of cultivar on the relationship with leaf N content suggests that a unique equation to estimate leaf N content from vegetation indices can be applied to all three cultivars. Results of chlorophyll meter measurements suggest that care should be taken when using sufficiency values, determined for a particular cultivar

Evaluation of OptRx™ active optical sensor to monitor soybean response to nitrogen inputs

BACKGROUND

Active optical crop sensors have been gaining importance to determine in-season nitrogen (N) fertilization requirements for on-the-go variable rate applications. Although most of these active in-field crop sensors have been evaluated in maize (Zea mays L.) and wheat (Triticum aestivum L. emend. Thell.), these sensors have not been evaluated in soybean [Glycine max (L.) Merr.] production systems in North Dakota, USA. Recent research from both South Dakota and North Dakota, USA indicate that in-season N application in soybean can increase soybean yield under certain conditions.

RESULTS

The study revealed that OptRx™ sensor reading did not show any significant differences from early to midway through the growing season. The NDRE (normalized difference red edge) index data collected towards the end of the growing season showed significantly higher values for some of the N treatments as compared to others in both years. The NDRE values were strongly correlated to grain yield for both years under tiled (r = 0.923) and non-tiled (r = 0.901) drainage conditions. Certain soybean varieties displayed significantly higher NDRE values over both years. The three varieties tested across years, under both tiled and non-tiled conditions, showed a significant linear relationship between late August NDRE values and yield (R2  = 0.85 for tiled and R2  = 0.81 for non-tiled).

CONCLUSION

In this research, the study results show that the OptRx™ sensor has the potential to work for soybean as well, though later in the crop growing season. Further investigation is needed to confirm the use of OptRx™ sensor for variable rate in-season N applications in soybeans. © 2019 Society of Chemical Industry.

Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance

As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (Brassica chinensis L. var. Shanghai Qing), Chinese white cabbage (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee), and Romaine lettuce (Lactuca sativa var longifoliaf. Lam) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R2 = 0.809, RMSE = 62.44 mg m-2), Chinese white cabbage (R2 = 0.891, RMSE = 45.18 mg m-2) and Romaine lettuce (R2 = 0.834, RMSE = 38.58 mg m-2) individually as well as of the three vegetables combined (R2 = 0.811, RMSE = 55.59 mg m-2). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680-750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately.

Precision Agriculture Techniques and Practices: From Considerations to Applications

Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

[Effects of solar radiation on CH4 emission in paddy field]

Decrease in solar radiation is one of the main components of climate change. Studies aimed at examining the effects of decreased solar radiation on CH₄ emission and estimation of CH₄ emission based on hyperspectral data in paddy fields are still scarce. A field simulation experiment was conducted to investigate the effects of shading intensity on CH₄ emission in a paddy field and rice canopy hyperspectral properties. CH₄ emission flux was estimated with rice canopy hyperspectral data. The shading intensities were set at three levels, i.e. control (CK, no shading), light shading (S₁, 60% of shading rate), and heavy shading (S₂, 84% of shading rate). The results showed that shading significantly reduced CH₄ emission. However, CH₄ emission under heavy shading (S₂) was higher than that under light shading (S₁). The reflectance of the near-infrared spectrum on rice canopy from the jointing stage to grain filling stage was in the sequence of CK>S₂>S₁. The spectral reflectance on rice canopy was significantly and positively correlated with CH₄ flux in the near-infrared band (699-1349 nm), with a correlation coefficient of 0.64 (P<0.01). The six vegetation indices were significantly correlated with CH₄ flux. The correlation coefficient between Ratio Vegetation Index (RVI) and CH₄ flux was the largest, with R²=0.84 (P<0.01). The stepwise regression model with RVI, Normalized Difference Vegetation Index (NDVI), and 507 nm original reflectance (Ρ₅₀₇) parameters was the best one (fitting model R²=0.86, prediction model R²=0.85) for estimating CH₄ emission.

[Fraction of absorbed photosynthetically active radiation over summer maize canopy estimated by hyperspectral remote sensing under different drought conditions.]

Fraction of absorbed photosynthetically active radiation (fAPAR) is one of the important remote sensing model parameters of vegetation productivity. However, the crop canopy fAPAR estimation during growing season under different drought conditions has not been reported yet. In this study, the characteristics of summer maize canopy fAPAR and spectral reflectance during growing season under different drought stresses and the relationships of fAPAR with reflectance, the first derivative spectral reflectance and vegetation indices were examined based on the hyperspectral reflectance and fAPAR data from the summer maize drought manipulation experiment with five irrigation levels in 2015. Under mild water stress and sufficient water supply conditions, fAPAR was higher, with the maximum value of 0.7. Under severe water stress and severe persistent drought, fAPAR was lower, with the minimum value of 0.06. Reflectance of visible and shortwave bands increased and near infrared reflectance decreased with increasing drought. The fAPAR was negatively related with visible bands and shortwave bands, but positively correlated with near infrared. Visible and shortwave band reflectance had significant correlation with fAPAR, especially at 383, 680 and 1980 nm, with all the correlation coefficients being more than -0.87. The strong and stable relationship between the first derivative spectral reflectance and fAPAR appeared at 580, 720 and 1546 nm, with the correlation coefficients being -0.91, 0.89 and 0.88, respectively. There were linear or logarithm relationships between fAPAR with nine vegetation indices. Among the nine indices, the enhanced vegetation index (EVI), renormalized difference vegetation index (RDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) performed well with the correlation coefficient being higher than 0.88, and the average relative error (RMAE) 16.6%, 16.6%, 16.7% and 16.2%, respectively. Based on the logarithmic relationship between first derivative spectral reflectance and fAPAR, the simulation effect was best at the band of (720±5) nm, with a correlation coefficient of 0.86. The correlation coefficient of the relationship between fAPAR and reflectance was less than 0.81. The results could provide fAPAR simulation for remote sensing model of vegetation productivity and drought warning.

Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery

Contents Summary I. Introduction II. Evolving modes of phenological study III. The phenocam approach IV. Applications of the phenocam method V. Looking forward Acknowledgements References SUMMARY: Global change is shifting the seasonality of vegetation in ecosystems around the globe. High-frequency digital camera imagery, and vegetation indices derived from that imagery, is facilitating better tracking of phenological responses to environmental variation. This method, commonly referred to as the 'phenocam' approach, is well suited to several specific applications, including: close-up observation of individual organisms; long-term canopy-level monitoring at individual sites; automated phenological monitoring in regional-to-continental scale observatory networks; and tracking responses to experimental treatments. Several camera networks are already well established, and some camera records are a more than a decade long. These data can be used to identify the environmental controls on phenology in different ecosystems, which will contribute to the development of improved prognostic phenology models.

A Novel Illumination Compensation Technique for Multi-Spectral Imaging in NDVI Detection

To overcome the dependence on sunlight of multi-spectral cameras, an active light source multi-spectral imaging system was designed and a preliminary experimental study was conducted at night without solar interference. The system includes an active light source and a multi-spectral camera. The active light source consists of four integrated LED (Light Emitting Diode) arrays and adjustable constant current power supplies. The red LED arrays and the near-infrared LED arrays are each driven by an independently adjustable constant current power supply. The center wavelengths of the light source are 668 nm and 840 nm, which are consistent with that of filter lens of the Rededge-M multi-spectral camera. This paper shows that the radiation intensity measured is proportional to the drive current and is inversely proportional to the radiation distance, which is in accordance with the inverse square law of light. Taking the inverse square law of light into account, a radiation attenuation model was established based on the principle of image system and spatial geometry theory. After a verification test of the radiation attenuation model, it can be concluded that the average error between the radiation intensity obtained using this model and the actual measured value using a spectrometer is less than 0.0003 w/m². In addition, the fitting curve of the multi-spectral image grayscale digital number (DN) and reflected radiation intensity at the 668 nm (Red light) is y = −3484230x² + 721083x + 5558, with a determination coefficient of R² = 0.998. The fitting curve with the 840 nm (near-infrared light) is y = 491469.88x + 3204, with a determination coefficient of R² = 0.995, so the reflected radiation intensity on the plant canopy can be calculated according to the grayscale DN. Finally, the reflectance of red light and near-infrared light can be calculated, as well as the Normalized Difference Vegetation Index (NDVI) index. Based on the above model, four plants were placed at 2.85 m away from the active light source multi-spectral imaging system for testing. Meanwhile, NDVI index of each plant was measured by a Greenseeker hand-held crop sensor. The results show that the data from the two systems were linearly related and correlated with a coefficient of 0.995, indicating that the system in this article can effectively detect the vegetation NDVI index. If we want to use this technology for remote sensing in UAV, the radiation intensity attenuation and working distance of the light source are issues that need to be considered carefully.

Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013⁻2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68⁻70%, 10⁻12%, and 67⁻73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77⁻85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.

Assessing regional drought impacts on vegetation and evapotranspiration: a case study in Guanacaste, Costa Rica

This research investigates ecological responses to drought by developing a conceptual framework of vegetation response and investigating how multiple measures of drought can improve regional drought monitoring. We apply this approach to a case study of a recent drought in Guanacaste, Costa Rica. First, we assess drought severity with the Standard Precipitation Index (SPI) based on a 64-yr precipitation record derived from a combination of Global Precipitation Climatology Center data and satellite observations from Tropical Rainfall Measuring Mission and Global Precipitation Measurement. Then, we examine spatial patterns of precipitation, vegetation greenness, evapotranspiration (ET), potential evapotranspiration (PET), and evaporative stress index (ESI) during the drought years of 2013, 2014, and 2015 relative to a baseline period (2002-2012). We compute wet season (May-October) anomalies for precipitation at 0.25° spatial resolution, normalized difference vegetation index (NDVI) at 30-m spatial resolution, and ET, PET and ESI derived with the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model at 1-km spatial resolution. We assess patterns of landscape response across years and land cover types including three kinds of forest (deciduous, old growth, and secondary), grassland, and cropland. Results show that rainfall in Guanacaste reached an all-time low in 2015 over a 64-yr record (wet season SPI = -3.46), resulting in NDVI declines. However, ET and ESI did not show significant anomalies relative to a baseline, drought-free period. Forests in the region exhibited lower water stress compared to grasslands and had smaller declines, and even some increases, in NDVI and ET during the drought period. This work highlights the value of using multiple measures to assess ecosystem responses to drought. It also suggests that agricultural land management has an opportunity to integrate these findings by emulating some of the characteristics of drought-resilient ecosystems in managed systems.

High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery

Bioethanol production obtained from cereal straw has aroused great interest in recent years, which has led to the development of breeding programs to improve the quality of lignocellulosic material in terms of the biomass and sugar content. This process requires the analysis of genotype-phenotype relationships, and although genotyping tools are very advanced, phenotypic tools are not usually capable of satisfying the massive evaluation that is required to identify potential characters for bioethanol production in field trials. However, unmanned aerial vehicle (UAV) platforms have demonstrated their capacity for efficient and non-destructive acquisition of crop data with an application in high-throughput phenotyping. This work shows the first evaluation of UAV-based multi-spectral images for estimating bioethanol-related variables (total biomass dry weight, sugar release, and theoretical ethanol yield) of several accessions of wheat, barley, and triticale (234 cereal plots). The full procedure involved several stages: (1) the acquisition of multi-temporal UAV images by a six-band camera along different crop phenology stages (94, 104, 119, 130, 143, 161, and 175 days after sowing), (2) the generation of ortho-mosaicked images of the full field experiment, (3) the image analysis with an object-based (OBIA) algorithm and the calculation of vegetation indices (VIs), (4) the statistical analysis of spectral data and bioethanol-related variables to predict a UAV-based ranking of cereal accessions in terms of theoretical ethanol yield. The UAV-based system captured the high variability observed in the field trials over time. Three VIs created with visible wavebands and four VIs that incorporated the near-infrared (NIR) waveband were studied, obtaining that the NIR-based VIs were the best at estimating the crop biomass, while the visible-based VIs were suitable for estimating crop sugar release. The temporal factor was very helpful in achieving better estimations. The results that were obtained from single dates [i.e., temporal scenario 1 (TS-1)] were always less accurate for estimating the sugar release than those obtained in TS-2 (i.e., averaging the values of each VI obtained during plant anthesis) and less accurate for estimating the crop biomass and theoretical ethanol yield than those obtained in TS-3 (i.e., averaging the values of each VI obtained during full crop development). The highest correlation to theoretical ethanol yield was obtained with the normalized difference vegetation index (R ² = 0.66), which allowed to rank the cereal accessions in terms of potential for bioethanol production.

Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

Rapid, non-destructive and accurate detection of crop N status is beneficial for optimized fertilizer applications and grain quality prediction in the context of precision crop management. Previous research on the remote estimation of crop N nutrition status was mostly conducted with ground-based spectral data from nadir or oblique angles. Few studies investigated the performance of unmanned aerial vehicle (UAV) based multispectral imagery in regular nadir views for such a purpose, not to mention the feasibility of oblique or multi-angular images for improved estimation. This study employed a UAV-based five-band camera to acquire multispectral images at seven view zenith angles (VZAs) (0°, ± 20°, ± 40° and ±60°) for three critical growth stages of winter wheat. Four representative vegetation indices encompassing the Visible Atmospherically Resistant Index (VARI), Red edge Chlorophyll Index (CI_red-edge), Green band Chlorophyll Index (CI_green), Modified Normalized Difference Vegetation Index with a blue band (mND_blue) were derived from the multi-angular images. They were used to estimate the N nutrition status in leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) of wheat canopies for a combination of treatments in N rate, variety and planting density. The results demonstrated that the highest accuracy for single-angle images was obtained with CI_green for LNC from a VZA of -60° (R² = 0.71, RMSE = 0.34%) and PNC from a VZA of -40° (R² = 0.36, RMSE = 0.29%). When combining an off-nadir image (-40°) and the 0° image, the accuracy of PNC estimation was substantially improved (CI_red-edge: R² = 0.52, RMSE = 0.28%). However, the use of dual-angle images did not significantly increase the estimation accuracy for LNA and PNA compared to the use of single-angle images. Our findings suggest that it is important and practical to use oblique images from a UAV-based multispectral camera for better estimation of nitrogen concentration in wheat leaves or plants. The oblique images acquired from additional flights could be used alone or combined with the nadir-view images for improved crop N status monitoring.

Enhanced gross primary production and evapotranspiration in juniper-encroached grasslands

Woody plant encroachment (WPE) into grasslands has been occurring globally and may be accelerated by climate change in the future. This land cover change is expected to alter the carbon and water cycles, but it remains uncertain how and to what extent the carbon and water cycles may change with WPE into grasslands under current climate. In this study, we examined the difference of vegetation indices (VIs), evapotranspiration (ET), gross primary production (GPP), and solar-induced chlorophyll fluorescence (SIF) during 2000-2010 between grasslands and juniper-encroached grasslands. We also quantitatively assessed the changes of GPP and ET for grasslands with different proportions of juniper encroachment (JWPE). Our results suggested that JWPE increased the GPP, ET, greenness-related VIs, and SIF of grasslands. Mean annual GPP and ET were, respectively, ~55% and ~45% higher when grasslands were completely converted into juniper forests under contemporary climate during 2000-2010. The enhancement of annual GPP and ET for grasslands with JWPE varied over years ranging from about +20% GPP (~+30% for ET) in the wettest year (2007) to about twice as much GPP (~+55% for ET) in the severe drought year (2006) relative to grasslands without encroachment. Additionally, the differences in GPP and ET showed significant seasonal dynamics. During the peak growing season (May-August), GPP and ET for grasslands with JWPE were ~30% and ~40% higher on average. This analysis provided insights into how and to what degree carbon and water cycles were impacted by JWPE, which is vital to understanding how JWPE and ecological succession will affect the regional and global carbon and water budgets in the future.

Retrieving the Diurnal FPAR of a Maize Canopy from the Jointing Stage to the Tasseling Stage with Vegetation Indices under Different Water Stresses and Light Conditions

The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. However, it has not yet been verified whether continuous VIs can be used to accurately estimate the diurnal dynamics of a vegetation canopy FPAR, which may fluctuate dramatically within a day. In this study, we measured the high temporal resolution spectral data (480 to 850 nm) and FPAR data of a maize canopy from the jointing stage to the tasseling stage under different irrigation and illumination conditions using two automatic observation systems. To estimate the FPAR, we developed regression models based on a quadratic function using 13 kinds of VIs. The results show the following: (1) Under nondrought conditions, although the illumination condition (sunny or cloudy) influenced the trend of the canopy diurnal FPAR, it had only a slight effect on the model accuracies of the FPAR-VIs. The maximum coefficients of determination (R²) of the FPAR-VIs models generated for the sunny nondrought data, the cloudy nondrought data, and all of the nondrought data were 0.895, 0.88, and 0.828, respectively. The VIs—including normalized difference vegetation index (NDVI), green NDVI (GNDVI), red-edge simple ratio (SR₇₀₅), modified simple ratio 2 (mSR2), red-edge normalized difference vegetation index (NDVI₇₀₅), and enhanced vegetation index (EVI)—that were related to the canopy structure had higher estimation accuracies (R² > 0.8) than the other VIs that were related to the soil adjustment, chlorophyll, and physiology. The estimation accuracies of the GNDVI and some red-edge VIs (including NDVI₇₀₅, SR₇₀₅, and mSR2) were higher than the estimation accuracy of the NDVI. (2) Under drought stress, the FPAR decreased significantly because of leaf wilting and the effective leaf area index decrease around noon. When we included drought data in the model, accuracies were reduced dramatically and the R² value of the best model was only 0.59. When we built the regression models based only on drought data, the EVI, which can weaken the influence of soil, had the best estimate accuracy (R² = 0.68).

Vulnerability of Amazonian forests to repeated droughts

Extreme droughts have been recurrent in the Amazon over the past decades, causing socio-economic and environmental impacts. Here, we investigate the vulnerability of Amazonian forests, both undisturbed and human-modified, to repeated droughts. We defined vulnerability as a measure of (i) exposure, which is the degree to which these ecosystems were exposed to droughts, and (ii) its sensitivity, measured as the degree to which the drought has affected remote sensing-derived forest greenness. The exposure was calculated by assessing the meteorological drought, using the standardized precipitation index (SPI) and the maximum cumulative water deficit (MCWD), which is related to vegetation water stress, from 1981 to 2016. The sensitivity was assessed based on the enhanced vegetation index anomalies (AEVI), derived from the newly available Moderate Resolution Imaging Spectroradiometer (MODIS)/Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) product, from 2003 to 2016, which is indicative of forest's photosynthetic capacity. We estimated that 46% of the Brazilian Amazon biome was under severe to extreme drought in 2015/2016 as measured by the SPI, compared with 16% and 8% for the 2009/2010 and 2004/2005 droughts, respectively. The most recent drought (2015/2016) affected the largest area since the drought of 1981. Droughts tend to increase the variance of the photosynthetic capacity of Amazonian forests as based on the minimum and maximum AEVI analysis. However, the area showing a reduction in photosynthetic capacity prevails in the signal, reaching more than 400 000 km2 of forests, four orders of magnitude larger than areas with AEVI enhancement. Moreover, the intensity of the negative AEVI steadily increased from 2005 to 2016. These results indicate that during the analysed period drought impacts were being exacerbated through time. Forests in the twenty-first century are becoming more vulnerable to droughts, with larger areas intensively and negatively responding to water shortage in the region.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

A Simulation Study Using Terrestrial LiDAR Point Cloud Data to Quantify Spectral Variability of a Broad-Leaved Forest Canopy

In this analysis, a method for construction of forest canopy three-dimensional (3D) models from terrestrial LiDAR was used for assessing the influence of structural changes on reflectance for an even-aged forest in Belgium. The necessary data were extracted by the developed method, as well as it was registered the adjacent point-clouds, and the canopy elements were classified. Based on a voxelized approach, leaf area index (LAI) and the vertical distribution of leaf area density (LAD) of the forest canopy were derived. Canopy–radiation interactions were simulated in a ray tracing environment, giving suitable illumination properties and optical attributes of the different canopy elements. Canopy structure was modified in terms of LAI and LAD for hyperspectral measurements. It was found that the effect of a 10% increase in LAI on NIR reflectance can be equal to change caused by translating 50% of leaf area from top to lower layers. As presented, changes in structure did affect vegetation indices associated with LAI and chlorophyll content. Overall, the work demonstrated the ability of terrestrial LiDAR for detailed canopy assessments and revealed the high complexity of the relationship between vertical LAD and reflectance.

Utilization of single-image normalized difference vegetation index (SI-NDVI) for early plant stress detection

PREMISE OF THE STUDY

An imaging system was refined to monitor the health of vegetation grown in controlled conditions using spectral reflectance patterns. To measure plant health, the single-image normalized difference vegetation index (SI-NDVI) compares leaf reflectance in visible and near-infrared light spectrums.

METHODS AND RESULTS

The SI-NDVI imaging system was characterized to assess plant responses to stress before visual detection during controlled stress assays. Images were analyzed using Fiji image processing software and Microsoft Excel to create qualitative false color images and quantitative graphs to detect plant stress.

CONCLUSIONS

Stress was detected in Arabidopsis thaliana seedlings within 15 min of salinity application using SI-NDVI analysis, before stress was visible. Stress was also observed during ammonium nitrate treatment of Eruca sativa plants before visual detection. Early detection of plant stress is possible using SI-NDVI imaging, which is both simpler to use and more cost efficient than traditional dual-image NDVI or hyper-spectral imaging.

Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery

The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools.

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.

[Application of near-surface remote sensing in monitoring the dynamics of forest canopy phenology.]

Near-surface remote sensing is an important technique for in-situ monitoring of forest phenology and a robust tool for scaling of the phenology with a high temporal resolution and mode-rate spatial coverage. Here, we first reviewed the methods of near-surface remote sensing with three major optical sensors (i.e., radiometer, spectrometer, and digital camera) for monitoring forest phenology. Second, we analyzed sources of uncertainties from distinguishing the phenophases by using the data obtained at the Maoershan flux site in the temperate forest. We found that the error was mainly attributed to the extracting method. Third, we analyzed the linkage of near-surface remote sensing with other methods and its intrinsic problems. Finally, we proposed four priorities in the research of this field: 1) linking optical (or canopy structural) phenology with functional phenology (physiological and ecological processes); 2) integrating the regional networks of canopy phenology for global networking observation and data sharing of canopy phenology; 3) integrating multi-source and multi-scale phenological data with the help of near-surface remote sensing; 4) developing phenology models based on near-surface remote sensing in order to improve the phenology simulation in the dynamic global vegetation models.

Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R² = 0.52-0.70), and photochemical reflectance index and blue N index from ground (PRI_g and BNI_g) performed consistently well across all growth stages (R² = 0.48-0.65 and 0.39-0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNI_g and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring.

[Retrieval of leaf area index of Phyllostachys praecox forest based on MODIS reflectance time series data.]

Based on the MODIS surface reflectance data, five vegetation indices, including norma-lized difference vegetation index (NDVI), simple ratio index (SR), Gitelson green index (GI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI) were constructed as remote sensing variables, coupled with the seven original spectral reflectance bands of MODIS. Stepwise regression and correlation analysis were used to select the variables, and the stepwise regression and Back Propagation (BP) neural network models were constructed based on the measured LAI to retrieve the LAI time series data of Phyllostachys praecox (Lei bamboo) forest during the period from January 2014 to March 2017. The retrieval results were compared with MOD15A2 LAI products during the same period. The results showed that SR was the single variable selected for the stepwise regression model. The correlations of LAI with bands b₁, b₂, b₃, b₇ and five vegetation indices were significant, which could be used as input variables of BP neural network model. There was a significant correlation between the LAI estimated from BP neural network and measured LAI, with the R² of 0.71, RMSE of 0.34, and RMSE_r of 13.6%. R² was increased by 10.9%, RMSE decreased by 5.6%, and RMSE_r decreased by 12.3% compared with LAI estimated from stepwise regression method. R² was increased by 54.5%, RMSE decreased by 79.3%, and RMSE_r decreased by 79.1% compared with MODIS LAI. The LAI of Lei bamboo forest could be accurately retrieved using BP neural network method based on MODIS reflectance time series data, which would be a feasible method for rapid monitoring of LAI in Lei bamboo forest.

[Hyper spectral characteristics and estimation model of leaf chlorophyll content in cotton under waterlogging stress.]

In order to rapidly monitor chlorophyll content in cotton functional leaf, and establish the quantitative relationship between chlorophyll content and spectral characteristic parameter of single cotton leaf, cotton was pot cultivated in a rain shelter and subjected to waterlogging at squaring stage. Cotton leaf samples were taken and measured every 3 days after waterlogging. The correlation between chlorophyll content and spectral characteristic parameter was synthetically analyzed, and then the estimation model of chlorophyll content was established and verified. The results showed that the chlorophyll content decreased with increasing waterlogging stress. The original spectral reflectance and first order differential spectral reflectance was negatively correlated with the chlorophyll content in the band near 580 and 697 nm. The estimation model established by difference vegetation index and normalized difference vegetation index performed better than that established by linear model of single band. Furthermore, the estimation model with (DR₆₉₇-DR₇₃₈)/(DR₆₉₇+DR₇₃₈) as the independent variable fitted the best with the correlation coefficient of 0.814, which could be utilized to estimate chlorophyll content of single leaf under waterlogging stress.

Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat

Hyperspectral remote sensing is a rapid non-destructive method for diagnosing nitrogen status in wheat crops. In this study, a quantitative correlation was associated with following parameters: leaf nitrogen accumulation (LNA), raw hyperspectral reflectance, first-order differential hyperspectra, and hyperspectral characteristics of wheat. In this study, integrated linear regression of LNA was obtained with raw hyperspectral reflectance (measurement wavelength = 790.4 nm). Furthermore, an exponential regression of LNA was obtained with first-order differential hyperspectra (measurement wavelength = 831.7 nm). Coefficients (R²) were 0.813 and 0.847; root mean squared errors (RMSE) were 2.02 g·m^-2 and 1.72 g·m^-2; and relative errors (RE) were 25.97% and 20.85%, respectively. Both the techniques were considered as optimal in the diagnoses of wheat LNA. Nevertheless, the better one was the new normalized variable (SD_r - SD_b)/(SD_r + SD_b), which was based on vegetation indices of R² = 0.935, RMSE = 0.98, and RE = 11.25%. In addition, (SD_r - SD_b)/(SD_r + SD_b) was reliable in the application of a different cultivar or even wheat grown elsewhere. This indicated a superior fit and better performance for (SD_r - SD_b)/(SD_r + SD_b). For diagnosing LNA in wheat, the newly normalized variable (SD_r - SD_b)/(SD_r + SD_b) was more effective than the previously reported data of raw hyperspectral reflectance, first-order differential hyperspectra, and red-edge parameters.

[Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices.]

Vegetation phenology is a comprehensive indictor for the responses of terrestrial ecosystem to climatic and environmental changes. Remote sensing spectrum has been widely used in the extraction of vegetation phenology information. However, there are many differences between phenology extracted by remote sensing and site observations, with their physical meaning remaining unclear. We selected one tile of MODIS data in northeastern China (2000-2014) to examine the SOS and EOS differences derived from the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (SR) based on both the red and near-infrared bands. The results showed that there were significant differences between NDVI-phenology and SR-phenology. SOS derived from NDVI averaged 18.9 days earlier than that from SR. EOS derived from NDVI averaged 19.0 days later than from SR. NDVI-phenology had a longer growing season. There were significant differences in the inter-annual variation of phenology from NDVI and SR. More than 20% of the pixel SOS and EOS derived from NDVI and SR showed the opposite temporal trend. These results caused by the seasonal curve characteristics and noise resistance differences of NDVI and SR. The observed data source of NDVI and SR were completely consistent, only the mathematical expressions were different, but phenology results were significantly different. Our results indicated that vegetation phenology monitoring by remote sensing is highly dependent on the mathematical expression of vegetation index. How to establish a reliable method for extracting vegetation phenology by remote sensing needs further research.

[The inversion of nitrogen balance index in typical growth period of soybean based on high definition digital image and hyperspectral data on unmanned aerial vehicles]

Nitrogen balance index (NBI) is one of the important indicators for crop growth. The high and low status of nitrogen can be quickly monitored by measuring NBI, which can provide accurate information of agricultural production and management. The relationship between NBI and original spectrum and derivative spectrum of infrared and near infrared wavelength from flowering to maturity stage was analyzed based on high definition digital image and hyperspectral data on unmanned aerial vehicles. Then, the sensitive bands were selected and the vegetation indexes were calculated. The inversion models of NBI were constructed by empirical model method. The optimal inversion model was obtained by analysing the determination coefficient (R2) and the root mean square error (RMSE) of validating model. The results showed that the correlation between NBI and derivative spectral reflectance was more stronger than that between it and original spectral reflectance. All the 14 vegetation indices selected in this study, except the derivative spectral photochemical reflectance index, had significant correlation with NBI. The NBI inversion models were constructed based on those 13 vegetation indices and the accuracy was analyzed. The inversion model constructed by derivative spectral difference vegetation index had the highest accuracy, with the R2 and RMSE being 0.771 and 3.077 respectively. The soybean NBI distribution maps of the whole growing stages generated by this model could reflect the soybean growth state. Estimation of NBI using the high definition digital image and hyperspectral data obtained by unmanned aerial vehicle, as shown by our results, could be a real-time, dynamic, non-destructive and effective way to monitor the nitrogen status of soybean. It's a simple and practical method for precise management of nitrogen in soybean.

Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively.

[A hyperspectral assessment model for leaf chlorophyll content of Pinus massoniana based on neural network]

The relationships between the leaf chlorophyll content (LCC) of Pinus massoniana at different growth stages and their chlorophyll content were analyzed. 7 of 36 red edge-based parameters were finally selected as the typical spectral response parameters which held the most significant statistical relationship with LCC, and then the hyperspectral assessment model for retrieving the LCC was built based on stepwise regression analysis method and B-P neural network, respectively. In the same way, four different vegetation indices (VIs) were selected as typical spectral parameters, in the meantime, the first four components of the principal component analysis (PCA) transformed from original spectral measurements were inputted into the B-P neural network, and then the hyperspectral assessment model for retrieving the LCC was built based on stepwise regression analysis method and B-P neural network, respectively. The results showed that R² of the red edge-based stepwise regression model and the red edge-based B-P neural network model were 0.5205 and 0.7253, RMSE were 0.1004 and 0.0848, and relative errors were 6.3% and 5.7%, respectively. R² of the VIs-based stepwise regression model and the VIs-based B-P neural network model were 0.5392 and 0.7064, RMSE were 0.0978 and 0.0871, and relative errors were at 6.2% and 6.0%, respectively. The prediction effect of PCA-based B-P neural network model was the best, R² was 0.7475, RMSE was 0.0540, and the relative error was 4.8%.

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research.

Assessment of soybean injury from glyphosate using airborne multispectral remote sensing

BACKGROUND

Glyphosate drift onto off-target sensitive crops can reduce growth and yield and is of great concern to growers and pesticide applicators. Detection of herbicide injury using biological responses is tedious, so more convenient and rapid detection methods are needed. The objective of this research was to determine the effects of glyphosate on biological responses of non-glyphosate-resistant (non-GR) soybean and to correlate vegetation indices (VIs) derived from aerial multispectral imagery.

RESULTS

Plant height, shoot dry weight and chlorophyll (CHL) content decreased gradually with increasing glyphosate rate, regardless of weeks after application (WAA). Accordingly, soybean yield decreased by 25% with increased rate from 0 to 0.866 kg AI ha(-1) . Similarly to biological responses, the VIs derived from aerial imagery - normalized difference vegetation index, soil adjusted vegetation index, ratio vegetation index and green NDVI - also decreased gradually with increasing glyphosate rate, regardless of WAA.

CONCLUSION

The VIs were highly correlated with plant height and yield but poorly correlated with CHL, regardless of WAA. This indicated that indices could be used to determine soybean injury from glyphosate, as indicated by the difference in plant height, and to predict the yield reduction due to crop injury from glyphosate.

Stress indicators based on airborne thermal imagery for field phenotyping a heterogeneous tree population for response to water constraints

As field phenotyping of plant response to water constraints constitutes a bottleneck for breeding programmes, airborne thermal imagery can contribute to assessing the water status of a wide range of individuals simultaneously. However, the presence of mixed soil-plant pixels in heterogeneous plant cover complicates the interpretation of canopy temperature. Moran's Water Deficit Index (WDI = 1-ETact/ETmax), which was designed to overcome this difficulty, was compared with surface minus air temperature (T s-T a) as a water stress indicator. As parameterization of the theoretical equations for WDI computation is difficult, particularly when applied to genotypes with large architectural variability, a simplified procedure based on quantile regression was proposed to delineate the Vegetation Index-Temperature (VIT) scatterplot. The sensitivity of WDI to variations in wet and dry references was assessed by applying more or less stringent quantile levels. The different stress indicators tested on a series of airborne multispectral images (RGB, near-infrared, and thermal infrared) of a population of 122 apple hybrids, under two irrigation regimes, significantly discriminated the tree water statuses. For each acquisition date, the statistical method efficiently delineated the VIT scatterplot, while the limits obtained using the theoretical approach overlapped it, leading to inconsistent WDI values. Once water constraint was established, the different stress indicators were linearly correlated to the stem water potential among a tree subset. T s-T a showed a strong sensitivity to evaporative demand, which limited its relevancy for temporal comparisons. Finally, the statistical approach of WDI appeared the most suitable for high-throughput phenotyping.

Stress indicators based on airborne thermal imagery for field phenotyping a heterogeneous tree population for response to water constraints

As field phenotyping of plant response to water constraints constitutes a bottleneck for breeding programmes, airborne thermal imagery can contribute to assessing the water status of a wide range of individuals simultaneously. However, the presence of mixed soil-plant pixels in heterogeneous plant cover complicates the interpretation of canopy temperature. Moran's Water Deficit Index (WDI = 1-ETact/ETmax), which was designed to overcome this difficulty, was compared with surface minus air temperature (T s-T a) as a water stress indicator. As parameterization of the theoretical equations for WDI computation is difficult, particularly when applied to genotypes with large architectural variability, a simplified procedure based on quantile regression was proposed to delineate the Vegetation Index-Temperature (VIT) scatterplot. The sensitivity of WDI to variations in wet and dry references was assessed by applying more or less stringent quantile levels. The different stress indicators tested on a series of airborne multispectral images (RGB, near-infrared, and thermal infrared) of a population of 122 apple hybrids, under two irrigation regimes, significantly discriminated the tree water statuses. For each acquisition date, the statistical method efficiently delineated the VIT scatterplot, while the limits obtained using the theoretical approach overlapped it, leading to inconsistent WDI values. Once water constraint was established, the different stress indicators were linearly correlated to the stem water potential among a tree subset. T s-T a showed a strong sensitivity to evaporative demand, which limited its relevancy for temporal comparisons. Finally, the statistical approach of WDI appeared the most suitable for high-throughput phenotyping.

Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding

Affordable and easy-to-use methods for assessing biomass and leaf area index (LAI) would be of interest in most breeding programs. Here, we describe the evaluation of a protocol for photographic sampling and image analysis aimed at providing low-labor yet robust indicators of biomass and LAI. In this trial, two genotypes of triticale, two of bread wheat, and four of tritordeum were studied. At six dates during the growing cycle, biomass and LAI were measured destructively, and digital photography was taken on the same dates. Several vegetation indices were calculated from each image. The results showed that repeatable and consistent values of the indices were obtained in consecutive photographic samplings on the same plots. The photographic indices were highly correlated with the destructive measurements, though the magnitude of the correlation was lower after anthesis. This work shows that photographic assessment of biomass and LAI can be fast, affordable, have good repeatability, and can be used under bright and overcast skies. A practical vegetation index derived from pictures is the fraction of green pixels over the total pixels of the image, and as it shows good correlations with all biomass variables, is the most robust to lighting conditions and has easy interpretation.

A New Method to Define the VI-Ts Diagram Using Subpixel Vegetation and Soil Information: A Case Study over a Semiarid Agricultural Region in the North China Plain

The VI-Ts diagram determined by the scatter points of the vegetation index (VI) and surface temperature (Ts) has been widely applied in land surface studies. In the VI-Ts diagram, dry point is defined as a pixel with maximum Ts and minimum VI, while wet point is defined as a pixel with minimum Ts and maximum VI. If both dry and wet points can be obtained simultaneously, a triangular VI-Ts diagram can be readily defined. However, traditional methods cannot define an ideal VI-Ts diagram if there are no full ranges of land surface moisture and VI, such as during rainy season or in a period with a narrow VI range. In this study, a new method was proposed to define the VI-Ts diagram based on the subpixel vegetation and soil information, which was independent of the full ranges of land surface moisture and VI. In this method, a simple approach was firstly proposed to decompose Ts of a given pixel into two components, the surface temperatures of soil (T_soil) and vegetation (T_veg), by means of Ts and VI information of neighboring pixels. The minimum T_veg and maximum T_soil were then used to determine the wet and dry points respectively within a given sampling window. This method was tested over a 30 km × 30 km semiarid agricultural area in the North China Plain through 2003 using Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and MODerate-resolution Imaging Spectroradiometer (MODIS) data. The wet and dry points obtained from our proposed method and from a traditional method were compared with those obtained from ground data within the sampling window with the 30 km × 30 km size. Results show that T_soil and T_veg can be obtained with acceptable accuracies, and that our proposed method can define reasonable VI-Ts diagrams over a semiarid agricultural region throughout the whole year, even for both cases of rainy season and narrow range of VI.

Inter-Comparison of ASTER and MODIS Surface Reflectance and Vegetation Index Products for Synergistic Applications to Natural Resource Monitoring

Synergistic applications of multi-resolution satellite data have been of a great interest among user communities for the development of an improved and more effective operational monitoring system of natural resources, including vegetation and soil. In this study, we conducted an inter-comparison of two remote sensing products, namely, visible/near-infrared surface reflectances and spectral vegetation indices (VIs), from the high resolution Advanced Thermal Emission and Reflection Radiometer (ASTER) (15 m) and lower resolution Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m - 500 m) sensors onboard the Terra platform. Our analysis was aimed at understanding the degree of radiometric compatibility between the two sensors' products due to sensor spectral bandpasses and product generation algorithms. Multiple pairs of ASTER and MODIS standard surface reflectance products were obtained at randomly-selected, globally-distributed locations, from which two types of VIs were computed: the normalized difference vegetation index and the enhanced vegetation indices with and without a blue band. Our results showed that these surface reflectance products and the derived VIs compared well between the two sensors at a global scale, but subject to systematic differences, of which magnitudes varied among scene pairs. An independent assessment of the accuracy of ASTER and MODIS standard products, in which "in-house" surface reflectances were obtained using in situ Aeronet atmospheric data for comparison, suggested that the performance of the ASTER atmospheric correction algorithm may be variable, reducing overall quality of its standard reflectance product. Atmospheric aerosols, which were not corrected for in the ASTER algorithm, were found not to impact the quality of the derived reflectances. Further investigation is needed to identify the sources of inconsistent atmospheric correction results associated with the ASTER algorithm, including additional quality assessments of the ASTER and MODIS products with other atmospheric radiative transfer codes.

Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest

Vegetation indices play an important role in monitoring variations in vegetation. The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Group and the Normalized Difference Vegetation Index (NDVI) are both global-based vegetation indices aimed at providing consistent spatial and temporal information regarding global vegetation. However, many environmental factors such as atmospheric conditions and soil background may produce errors in these indices. The topographic effect is another very important factor, especially when the indices are used in areas of rough terrain. In this paper, we theoretically analyzed differences in the topographic effect on the EVI and the NDVI based on a non-Lambertian model and two airborne-based images acquired from a mountainous area covered by high-density Japanese cypress plantation were used as a case study. The results indicate that the soil adjustment factor “L” in the EVI makes it more sensitive to topographic conditions than is the NDVI. Based on these results, we strongly recommend that the topographic effect should be removed in the reflectance data before the EVI was calculated—as well as from other vegetation indices that similarly include a term without a band ratio format (e.g., the PVI and SAVI)—when these indices are used in the area of rough terrain, where the topographic effect on the vegetation indices having only a band ratio format (e.g., the NDVI) can usually be ignored.

Light interception and dry matter conversion efficiency of miscanthus genotypes estimated from spectral reflectance measurements

• Relationships between crop reflectance in the visible and the near infrared wavelengths are closely correlated with the amount of photosynthetically active tissue in the crop. Reflectance measurements were used to quantify genotypic differences in light interception, dry matter (DM) conversion efficiency and senescence pattern within the genus Miscanthus. The aim was to verify this method as a selection tool in plant breeding programmes. • Spectral reflectance of nine genotypes was measured weekly throughout their second and third growing seasons in a field experiment conducted in Denmark. Leaf greenness was assessed by visual scoring. • Significant differences between genotypes in the calculated fraction of PAR intercepted in green tissue (f _ipar ) occurred mainly early and late in the growing season. The f _ipar values correlated well with visual estimates of leaf greenness. Within genotypes accumulated intercepted PAR ranged from 632 to 737 MJ m ^-2 in the third year, while the DM : radiation quotient, ɛ, ranged from 1.06 to 2.53 g MJ ^-1 . • Yield variation between genotypes was mainly caused by differences in ɛ. Measuring spectral reflectance was less time consuming than visual leaf scoring. The significant physiological variation within the genus Miscanthus gives good prospects for future breeding.