Estimation of transpiration and canopy cover of winter wheat under different fertilization levels using thermal infrared and visible imagery

This abstract is for SUPPORT APPLICATION.Drought is the most significant stress that reduces crop yield, hence, agricultural irrigation is the major consumer of freshwater worldwide. There is everlasting need to improve irrigation applications in order to increase water use efficiency and save water. Conventional methods to estimate crop water status and within-field variability are precise, yet, highly demanding for time and manpower. Remote sensing in the reflective and the emissive spectrum with unmanned aerial vehicle (UAV) holds potential to detect drought stress by observing canopy status over a larger area. A common method to detect drought stress using UAV thermal imagery is the Crop Water Stress Index (CWSI), which does needs improvement and parametrization for cereal crops such as winter wheat. Field experiment with winter wheat was performed in 24 plots (30 m x 30 m) under three different irrigation regimes in 2018 (drought year) and 2019 (normal year) in Denmark. Thermal and multispectral data on UAV scale were collected during the growth period. Plant physiology, i.e., stomatal conductance, leaf water potential and canopy cover was measured, in addition to soil water content. Crop water deficit was estimated through comparison of the variability of canopy temperature and plant physiological changes. The resulting correlation pointed on clear possibility to quantify crop water status using thermal data, which is useful to develop a site-specific application of irrigation. Further work involves parameterization of CWSI and calculation of and comparison with other indices to test for improvements.

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An improved combined vegetation difference index and burn scar index approach for mapping cropland burned areas using combined data from Landsat 8 multispectral and thermal infrared bands

International Journal of Wildland Fire ◽

10.1071/wf18146 ◽

2020 ◽

Vol 29 (6) ◽

pp. 499

Author(s):

Shufu Liu ◽

Shudong Wang ◽

Tianhe Chi ◽

Congcong Wen ◽

Taixia Wu ◽

...

Keyword(s):

Winter Wheat ◽

Land Surface ◽

Vegetation Index ◽

Agricultural Land ◽

Thermal Infrared ◽

Haihe River Basin ◽

Burned Area ◽

Landsat 8 ◽

Burn Scar ◽

Burned Areas

The accurate extraction of agricultural burned area is essential for fire-induced air quality models and assessments of agricultural grain loss and wildfire disasters. The present study provides an improved approach for mapping uncontrolled cropland burned areas, which involves pre-classification using a difference vegetation index model for various agricultural land scenarios. Land surface temperature was analysed in burned and unburned areas and integrated into a previous burn scar index (BSI) model, and multispectral and thermal infrared information were combined to create a new temperature BSI (TBSI) to remove background noise. The TBSI model was applied to a winter wheat agricultural region in the Haihe River Basin in northern China. The extracted burned areas were validated using Gaofen-1 satellite data and compared with those produced by the previous BSI model. The producer and user accuracy of the new TBSI model were measured at 92.42 and 95.31% respectively, with an overall kappa value of 0.92, whereas those of the previous BSI model were 83.33, 87.30% and 0.86. The results indicate that the new method is more appropriate for mapping uncontrolled winter wheat burned area. Potential applications of this research include trace gas emission models, agricultural fire management and agricultural wildfire disaster assessment.

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Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)

International Journal of Agronomy ◽

10.1155/2021/1443191 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Vaughn Reed ◽

Daryl B. Arnall ◽

Bronc Finch ◽

Joao Luis Bigatao Souza

Keyword(s):

Winter Wheat ◽

Prediction Model ◽

Optical Sensors ◽

Prediction Models ◽

Canopy Cover ◽

Yield Prediction ◽

State University ◽

Normalized Difference Vegetative Index ◽

Highly Correlated ◽

N Rates

Optical sensors have grown in popularity for estimating plant health, and they form the basis of midseason yield estimations and nitrogen (N) fertilizer recommendations, such as the Oklahoma State University (OSU) nitrogen fertilization optimization algorithm (NFOA). That algorithm uses measurements of normalized difference vegetative index (NDVI), yet not all producers have access to the sensors required to make these measurements. In contrast, most producers have access to smartphones, which can measure fractional green canopy cover (FGCC) using the Canopeo app, but the usefulness of these measurements for midseason yield estimations remains untested. Our objectives were to (1) quantify the relationship between NDVI and FGCC, (2) assess the potential for using FGCC values in place of NDVI values in the current OSU Yield Prediction Model, and (3) compare the performance of NDVI and FGCC-based yield prediction models from the collected dataset. This project, implemented on 13 winter wheat sites over the 2019-2020 growing season, used a range of nitrogen (N) rates (0, 34, 67, 101, and 134 kg N ha−1) to provide different levels of yield. Our results indicated that while NDVI and FGCC are highly correlated (r2 = 0.76), FGCC is not suitable for direct insertion into the current yield prediction model. However, a yield prediction model derived from FGCC provided similar estimates of yield compared to NDVI (Nash Sutcliffe Efficiency = −3.3). This new FGCC-based model will give more producers access to sensor-based yield prediction and N rate recommendations.

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Coupling Remote Sensing Data and AquaCrop Model for Simulation of Winter Wheat Growth under Rainfed and Irrigated Conditions in a Mediterranean Environment

Agronomy ◽

10.3390/agronomy11112265 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2265

Author(s):

Marie Therese Abi Saab ◽

Razane El Alam ◽

Ihab Jomaa ◽

Sleiman Skaf ◽

Salim Fahed ◽

...

Keyword(s):

Remote Sensing ◽

Winter Wheat ◽

Vegetation Index ◽

Vegetation Indices ◽

Canopy Cover ◽

Mediterranean Environment ◽

Wheat Growth ◽

Aquacrop Model ◽

Rainfed Conditions ◽

Sentinel 2

The coupling of remote sensing technology and crop growth models represents a promising approach to support crop yield prediction and irrigation management. In this study, five vegetation indices were derived from the Copernicus-Sentinel 2 satellite to investigate their performance monitoring winter wheat growth in a Mediterranean environment in Lebanon’s Bekaa Valley. Among those indices, the fraction of canopy cover was integrated into the AquaCrop model to simulate biomass and yield of wheat grown under rainfed conditions and fully irrigated regimes. The experiment was conducted during three consecutive growing seasons (from 2017 to 2019), characterized by different precipitation patterns. The AquaCrop model was calibrated and validated for different water regimes, and its performance was tested when coupled with remote sensing canopy cover. The results showed a good fit between measured canopy cover and Leaf Area Index (LAI) data and those derived from Sentinel 2 images. The R2 coefficient was 0.79 for canopy cover and 0.77 for LAI. Moreover, the regressions were fitted to relate biomass with Sentinel 2 vegetation indices. In descending order of R2, the indices were ranked: Fractional Vegetation Cover (FVC), LAI, the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI). Notably, FVC and LAI were highly correlated with biomass. The results of the AquaCrop calibration showed that the modeling efficiency values, NSE, were 0.99 for well-watered treatments and 0.95 for rainfed conditions, confirming the goodness of fit between measured and simulated values. The validation results confirmed that the simulated yield varied from 2.59 to 5.36 t ha−1, while the measured yield varied from 3.08 to 5.63 t ha−1 for full irrigation and rainfed treatments. After integrating the canopy cover into AquaCrop, the % of deviation of simulated and measured variables was reduced. The Root Mean Square Error (RMSE) for yield ranged between 0.08 and 0.69 t ha−1 before coupling and between 0.04 and 0.42 t ha−1 after integration. This result confirmed that the presented integration framework represents a promising method to improve the prediction of wheat crop growth in Mediterranean areas. Further studies are needed before being applied on a larger scale.

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Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery

Remote Sensing ◽

10.3390/rs12223696 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3696

Author(s):

Ramin Heidarian Dehkordi ◽

Moussa El Jarroudi ◽

Louis Kouadio ◽

Jeroen Meersmans ◽

Marco Beyer

Keyword(s):

Winter Wheat ◽

Leaf Rust ◽

Stripe Rust ◽

Canopy Cover ◽

Correlation Coefficients ◽

Plant Diseases ◽

Diseased Plant ◽

Wheat Leaf Rust ◽

Spectral Bands ◽

Wheat Leaf

During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs), thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention for discriminating healthy from diseased plants and monitoring the progress of such plant diseases in fields. Despite the well-documented usage of UAV-based hyperspectral remote sensing for discriminating healthy and diseased plant areas, employing red-green-blue (RGB) imagery for a similar purpose has yet to be fully investigated. This study aims at evaluating UAV-based RGB imagery to discriminate healthy plants from those infected by stripe and wheat leaf rusts in winter wheat (Triticum aestivum L.), with a focus on implementing an expert system to assist growers in improved disease management. RGB images were acquired at four representative wheat-producing sites in the Grand Duchy of Luxembourg. Diseased leaf areas were determined based on the digital numbers (DNs) of green and red spectral bands for wheat stripe rust (WSR), and the combination of DNs of green, red, and blue spectral bands for wheat leaf rust (WLR). WSR and WLR caused alterations in the typical reflectance spectra of wheat plants between the green and red spectral channels. Overall, good agreements between UAV-based estimates and observations were found for canopy cover, WSR, and WLR severities, with statistically significant correlations (p-value (Kendall) < 0.0001). Correlation coefficients were 0.92, 0.96, and 0.86 for WSR severity, WLR severity, and canopy cover, respectively. While the estimation of canopy cover was most often less accurate (correlation coefficients < 0.20), WSR and WLR infected leaf areas were identified satisfactorily using the RGB imagery-derived indices during the critical period (i.e., stem elongation and booting stages) for efficacious fungicide application, while disease severities were also quantified accurately over the same period. Using such a UAV-based RGB imagery method for monitoring fungal foliar diseases throughout the cropping season can help to identify any new disease outbreak and efficaciously control its spread.

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