Estimating fractional coverage of crop, crop residue, and bare soil using shortwave infrared angle index and Sentinel-2 MSI

Accurate estimations of soil cover parameters (fractional coverage of crop residue [f (cr)], crop [f (c)], and bare soil [f (bs)]) in croplands can assist in decision-making for agricultural management organisations and are important for understanding the interrelations between global changes and terrestrial ecosystems. In recent decades, optical remote sensing has been widely used for mapping f (c), f (cr), and f (bs). The crop residue and soil spectra decrease rapidly with increasing cropland moisture primarily because of water absorption in the near-infrared and shortwave infrared bands. This leads to a decrease in the denominator of current normalized crop residue spectral indices (shortwave infrared [SWIR] band 1 + band 2), thereby causing them to increase with increasing cropland moisture. This study revealed that the slopes of the soil and crop residue spectra between SWIR1 and SWIR2 were less affected by cropland moisture changes. Based on this feature, we propose a shortwave infrared angle (SWIRA) index and jointly use it with normalized difference vegetation index (NDVI) to estimate and produce multi-temporal maps of f (c), f (cr), and f (bs) using broadband Sentinel-2 MSI images. The proposed SWIRA is the slope of the spectrum between SWIR1 and SWIR2. This study (i) uses laboratory-based spectral reflectance to evaluate the performance of the SWIRA and (ii) further evaluates the performance of the SWIRA-NDVI method using multi-temporal Sentinel-2 MSI images. Our results indicate that (i) cropland moisture has a considerably smaller effect on SWIRA than it does on current broadband normalized crop residue spectral indices, and (ii) maps of the estimated f (c), f (cr), and f (bs) values can be used to improve crop growth and decision-making, for example, by providing harvest maps and soil tillage intensity maps.

Automated summary

This study proposes a method using SWIRA and NDVI with Sentinel-2 MSI images to estimate and produce multi-temporal maps of crop residue, crop, and bare soil. Results show that SWIRA is less affected by cropland moisture compared to existing spectral indices, and the estimated values can be used for improving crop growth and decision-making in agriculture.