The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture. It has been used globally since the 1970s as a proxy to monitor crop growth and correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions that might alter the crop’s real NDVI value. Synthetic Aperture Radar (SAR), on the other hand, can penetrate clouds and is hardly affected by atmospheric conditions, but it is sensitive to the physical structure of the crop and therefore does not give a direct indication of the NDVI. Several SAR indices and methods have been suggested to estimate NDVIs via SAR; however, they tend to work for local spatial and temporal conditions and do not work well globally. This is because they are not flexible enough to capture the changing NDVI–SAR relationship throughout the crop-growing season. This study suggests a new method for converting Sentinel-1 to NDVIs for Agricultural Fields (SNAF) by utilizing a hyperlocal machine learning approach. This method generates multiple on-the-fly disposal field- and time-specific models for every available Sentinel-1 image across 2021. Each model learns the field-specific NDVI (from Sentinel-2 and Landsat-8) –SAR (Sentinel-1) relationship based on recent NDVI and SAR time series and consequently estimates the optimal NDVI value from the current SAR image. The SNAF was tested on 548 commercial fields from 18 countries with 28 crop types and, based on 6880 paired NDVI–SAR images, achieved an RMSE, bias, and R2 of 0.06, 0.00, and 0.92, respectively. The outcome of this study aspires to a persistent seamless stream of NDVI values, regardless of the atmospheric conditions, illumination, or local conditions, which can assist in agricultural decision making.
Since the 1980s, thermal imagery has been used to assess crop water stress. The increase in the temporal resolution of optical satellite sensors (in the range of 400–2500 nm) and the better spatial resolution compared to the thermal imagery call for the definition of a new way for crop water stress monitoring. Hence, we are suggesting a new method utilizing spectral indices from three subsequent images to address this challenge. This method predicts the current water stress with the two past images and compares it to the current stress: if the existing conditions are better than the predicted stress, the crop is not under stress and has sufficient water for development. To evaluate the suggested method, we downloaded Sentinel-2 images and compared the stress found with that method to the leaf area index, leaf water potential, and yield from seven alfalfa growth cycles. The results outline the ability of the new optical stress index to depict spatial and temporal changes in the alfalfa water stress and especially illustrated the changes in the crop water stress over the growth cycle and after each irrigation. This new method needs to be validated with different crops and satellite sensors to verify its success.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.