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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #344254

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

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

Author
item Quemada, M. - University Of Madrid
item Hively, W.d. - Institute Of Geographic Sciences And Natural Resources
item Daughtry, Craig
item Lamb, B.t. - Institute Of Geographic Sciences And Natural Resources
item Shermeyer, J. - Institute Of Geographic Sciences And Natural Resources

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/8/2017
Publication Date: 3/1/2018
Citation: Quemada, M., Hively, W., Daughtry, C.S., Lamb, B., Shermeyer, J. 2018. Improved crop residue cover estimates by coupling spectral indices for residue and moisture. Remote Sensing of Environment. 206:33-44. https://doi.org/10.1016/j.rse.2017.12.012.

Interpretive Summary: Maintaining crop residues on the soil surface is a key component of conservation agriculture for sustainable cropping systems. The soil surface is often completely covered by crop residues after harvest, but residue cover decreases as the soil is tilled or residues are removed for fuel or feed. Crop residue cover reduces soil erosion, runoff, and amount of nutrients and agrochemicals that reach surface waters. Tillage intensity is the primary management practice that controls crop residue cover. Only remote sensing has the potential for monitoring crop residue cover over large areas in a timely and cost effective manner. However, variations in moisture conditions across fields and landscapes cause errors in remotely sensed estimates crop residue cover. This research developed and tested a robust method using a pair of spectral indices, one for moisture and one for crop residue cover, to mitigate the uncertainty caused by variable moisture conditions. This protocol can provide reliable assessments of crop residue cover and soil tillage intensity at regional scales which will improve our predictions of the impact of agricultural practices across landscapes.

Technical Abstract: Remote sensing assessment of soil residue cover (fR) and tillage intensity will improve our predictions of the impact of agricultural practices and promote sustainable management. Spectral indices for estimating fR are sensitive to soil and residue water content, therefore, the uncertainty of estimates increased with variable moisture conditions. Our goals were to evaluate the robustness of spectral residue indices based on the shortwave infrared region (SWIR) for estimating fR, and to develop a method to mitigate the uncertainty caused by variable moisture conditions on fR estimates. Eight fields partially irrigated and two fields with uniform water distribution were identified in WorldView3 satellite images from the Eastern Shore (MD, USA). Fields were subdivided in wedges and the SWIR bands were extracted from each pixel and averaged for each wedge. Based on these bands, two residue indexes (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 new NDTI and SINDRI were calculated. Finally, the probability density distributions of fR estimated from the residue indices were calculated for each field. SINDRI was more robust than NDTI for estimating fR. Moisture correction of spectral bands reduced the root mean square error of NDTI fR estimates from 23.7 to 4.4%, and SINDRI fR estimates from 6.6 to 2.2%. The mean and variance of the probability density distribution of fR estimated from residue indices, before and after moisture correction, was greatly reduced in the fields partially irrigated, whereas only slightly in fields with uniform water distribution. The estimation of fR should be based on SINDRI if bands are available, but can be reliable estimated combining NDTI with a water content index to mitigate the uncertainty caused by variable moisture conditions.