Simple regression models to estimate light interception in wheat crops with Sentinel‐2 and a handheld sensor
Pedro Pellegrini, C. Mariano Cossani, Carlos M. Di Bella, Gervasio Piñeiro, Víctor O. Sadras, Martı́n Oesterheld
Abstract
Abstract Capture of radiation by crop canopies drives growth rate, grain set, and yield. Since the fraction of photosynthetically active radiation absorbed by green area (fAPAR g ) correlates with normalized difference vegetation index (NDVI), remote sensors have been used to monitor vegetation. With a 10‐m spatial resolution and 5‐d revisiting time, the recently launched Sentinel‐2 satellite is a promising tool for fAPAR g monitoring. However, the available algorithm to estimate fAPAR g is based on simulations of canopy interception of several vegetation types and was never tested in field crops. Handheld sensors, such as GreenSeeker, are another alternative to estimate fAPAR g . Our objectives were (a) to test the ability of indices derived from Sentinel‐2 and GreenSeeker NDVI to capture fAPAR g of wheat ( Triticum aestivum L.) crops, (b) to compare these sensors’ performance against the moderate resolution imaging spectroradiometer (MODIS), and (c) to compare our Sentinel‐2 model estimations with the available algorithm. In wheat fields in the southwest Argentinean Pampas, on several sampling dates, we measured fAPAR g with a quantum light sensor and NDVI with a GreenSeeker. We regressed fAPAR g measurements with vegetation indices from the different sources and selected the best models. Sentinel‐2 and GreenSeeker NDVI precisely estimated fAPAR g , with a performance similar to MODIS ( p < .05; RMSD = 0.09, 0.11, and 0.08; R 2 = .89, .88, and .95, respectively). The available algorithm to estimate fAPAR g with Sentinel‐2 yielded biased estimations, mainly in the lower range of fAPAR g . These results suggest that simple models may provide fAPAR g estimations with Sentinel‐2 and GreenSeeker in wheat crops with an accuracy suitable for agricultural applications.