Improved crop residue cover estimates obtained by coupling spectral indices for residue and moisture

Abstract

Remote sensing assessment of crop residue cover ( f R ) and tillage intensity can improve predictions of the environmental impact of agricultural practices and promote sustainable management. Spectral indices for estimating f R are sensitive to soil and crop residue water contents, therefore the uncertainty of f R estimates increases when moisture conditions vary. Our goals were to evaluate the robustness of spectral residue indices based on the shortwave infrared region (SWIR) for estimating f R and to mitigate the uncertainty caused by variable moisture conditions on f R estimates. Ten fields with center pivot irrigation systems (eight partially irrigated and two uniformly dry fields) were identified in Worldview-3 satellite imagery acquired for a study site in Maryland (USA). The fields were mid-irrigation at the time of imagery acquisition, allowing comparison of residue cover under dry and wet conditions. Fields were subdivided into approximately equal-size wedges within the dry and wet portions of each field, and the SWIR bands were extracted for each pixel. Two crop residue indices (Normalized Difference Tillage Index (NDTI); Shortwave Infrared Normalized Difference Residue Index (SINDRI) and a water index (WI) were calculated. Reflectance in each band was moisture-adjusted based on the WI difference between wet and dry wedges, and updated NDTI and SINDRI were calculated. Finally, the probability density distributions of f R estimated from the residue indices were calculated for each field. SINDRI was more robust than NDTI for estimating f R . Moisture corrections of spectral bands reduced the root mean square error of NDTI f R estimates from 22.7% to 4.7%, and SINDRI f R estimates from 6.0% to 2.2%. The mean and variance of the probability density distribution of f R estimated from residue indices, before and after moisture correction, were greatly reduced in the partially irrigated fields, but only slightly in fields with uniform water distribution. The estimation of f R should be based on SINDRI if appropriate bands are available, but f R can be reliably estimated by combining NDTI with a water content index to mitigate the uncertainty caused by variable moisture conditions.