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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Sustainable Agricultural Systems Laboratory » Research » Publications at this Location » Publication #419048
Research Project: Enhancing Sustainability of Mid-Atlantic Agricultural Systems Using Agroecological Principles and Practices

Location: Sustainable Agricultural Systems Laboratory

Title: Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave satellite imagery

Author
item LAMB, BRIAN - Us Geological Survey (USGS)
item HIVELY, W. DEAN - Us Geological Survey (USGS)
item Jennewein, Jyoti
item Thieme, Alison
item SOROKA, ALEX - Us Geological Survey (USGS)
item SANTOS, LETICIA - North Carolina State University
item JONES, DANIELA - North Carolina State University
item Mirsky, Steven

Submitted to: Soil and Tillage Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/26/2025
Publication Date: 4/3/2025
Citation: Lamb, B., Hively, W., Jennewein, J.S., Thieme, A.N., Soroka, A.M., Santos, L., Jones, D., Mirsky, S.B. 2025. Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave satellite imagery. Soil and Tillage Research. 251. Article e106524. https://doi.org/10.1016/j.still.2025.106524.
DOI: https://doi.org/10.1016/j.still.2025.106524

Interpretive Summary: High amounts of crop residue cover have many benefits including erosion reduction and soil moisture retention. However, the ability to accurately predict crop residue across large spatial regions has been limited due to the availability of specialized remote sensing instruments. This study produced annual maps of crop residue from 2015 to 2022 for farmland on the Delmarva Peninsula, Maryland, USA using a specialized remote sensing satellite paired with on-farm photograph sampling. Our findings suggest that the narrow-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) for predicting crop residue across all years with high accuracy, indicating a potential reduction for ground data collection to calibrate specialized satellite imagery in the future. Our findings also showed broad-band indices, such as the Shortwave Infrared Angle (SWIRA) index, in conjunction with wetness and greenness indices, showed great model improvements for satellites with more common spectral bands. This indicates a potential for future the combination of broadband spectral indices (common in publicly available satellites) to map crop residue cover regionally without the use of specialized and often commercial satellites. Such analyses could provide a more accurate record of crop residue cover globally dating back to 1982.

Technical Abstract: Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover (fR) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify fR at scale has been limited. In this study we produced annual maps of fR for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm photographs (n = 895) classified to fR using SamplePoint software. Univariate linear regressions were used to compare photograph fR to WV3 crop residue indices including: 1) Shortwave Infrared Normalized Difference Residue Index (SINDRI), 2) Shortwave Infrared Difference Residue Index (SIDRI), 3) Normalized Difference Tillage Index (NDTI), and 4) Shortwave Infrared Angle Index (SWIRA). SINDRI and SIDRI are based on narrow bands capable of measuring lignocellulose absorption features. NDTI and SWIRA are based on Landsat-comparable broad bands. Our findings demonstrated that SINDRI outperformed other indices in fR estimation in terms of coefficient of determination (R2 = 0.869) and root mean square error (RMSE = 0.111), when R2 and RMSE were averaged across six individual years. For a univariate analysis combining five years of high-quality WV3 imagery, SINDRI again exhibited the highest fR estimation performance (R2 = 0.795; RMSE = 0.141), suggesting that SINDRI can map fR accurately with a singular relationship, potentially reducing the need for labor-intensive ground data collection. For broad-band indices, a multiple linear regression analysis that included a Water Index (WI) and Normalized Difference Vegetation Index (NDVI) as additional predictors increased the accuracy of fR estimation significantly, particularly for SWIRA (R2 = 0.767; RMSE = 0.144), but also NDTI (R2 = 0.654; RMSE = 0.174). Our findings suggest that while indices computed from narrow-band imagery are most accurate for fR estimation, SWIRA has the potential to improve fR estimation compared to NDTI, especially when used in conjunction with WI and NDVI. An index suite of SWIRA, WI, and NDVI can be computed with Landsat 4–9 imagery, providing a more accurate record of global fR dating back to 1982.