Simple regression models to estimate light interception in wheat crops with Sentinel‐2 and a handheld sensor

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.Abbreviations: fAPAR g , fraction of photosynthetically active radiation absorbed by green area; MODIS, moderate resolution imaging spectroradiometer; mSR, modified simple ratio; mSR2, modified simple ratio 2; ND705, red-edge normalized difference; NDVI, normalized difference vegetation index; NIR, near-infrared; PAR, photosynthetically active radiation; WDRVI, wide dynamic range vegetation index.

Symmetric response to competition in binary mixtures of cultivars associates with genetic gain in wheat yield

“…We measured NDVI (Greenseeker® Trimble) as a proxy for intercepted radiation (Pellegrini et al, 2020; Randall et al, 1996), the nitrogen nutrition index NNI to quantify crop nitrogen status (Gastal et al, 2015) and carbon isotope composition δ 13 C to quantify crop water status (Condon et al, 2002; Kohn, 2010; Stewart et al, 1995).…”

“…From mid‐tillering to maturity, we measured NDVI weekly, fitted polynomials to describe the time‐trajectory of NDVI and calculated the area under the curve to integrate NDVI over the season. We calculated the fraction of absorbed photosynthetically active radiation f APAR in pure stands using calibrations with NDVI for wheat canopies (Pellegrini et al, 2020 ). Cubic polynomials were fitted to describe the dynamics of f APAR with ontogeny; daily APAR was derived from daily f APAR from fitted curves and total solar radiation assuming a PAR: solar radiation ratio of 0.5 (Trápani et al, 1992 ).…”

Influence of weather and nutrients (FYM, N and P) on RGR, LAD, NAR and CGR to determine the productivity of maize, wheat and green gram in a cropping system

“…Daily incidental solar radiation (SRD) was derived from the daily sun shine hour data recorded at the IARI weather station. To get the fraction of intercepted Photosynthetically Active Radiation (fPAR), the values of NDVI taken at each phenological stage were used in the formula fPAR = −0.29 + 1.50 × NDVI (Pellegrini et al, 2020). The NDVI is the green seeker (Trimble, USA) value at a given stage calculated using the formula NDVI = (𝑅774 − 𝑅656) / (𝑅774 + 𝑅656).…”

Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage

“…Within remote sensing there are, at least, two main mechanisms for calibrations. The first approach considers sampling within the specific area of a pixel [51,52]. The second considers sampling within different areas of a paddock and, then, through data fusion, combines different satellites with contrasting spatial resolutions [20].…”