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

IF 6.1 1区农林科学 Q1 SOIL SCIENCE

Soil & Tillage Research Pub Date : 2025-04-03 DOI:10.1016/j.still.2025.106524

Brian T. Lamb , W. Dean Hively , Jyoti Jennewein , Alison Thieme , Alexander M. Soroka , Leticia Santos , Daniela Jones , Steven Mirsky

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引用次数: 0

Abstract

Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover (f_R) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify f_R at scale has been limited. In this study we produced annual maps of f_R for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm photographs (n = 895) classified to f_R using SamplePoint software. Univariate linear regressions were used to compare photograph f_R 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 f_R 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 f_R estimation performance (R² = 0.795; RMSE = 0.141), suggesting that SINDRI can map f_R 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 f_R 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 f_R estimation, SWIRA has the potential to improve f_R 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 f_R dating back to 1982.

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来源期刊

Soil & Tillage Research 农林科学-土壤科学

CiteScore

13.00

自引率

6.20%

发文量

266

审稿时长

5 months

期刊介绍： Soil & Tillage Research examines the physical, chemical and biological changes in the soil caused by tillage and field traffic. Manuscripts will be considered on aspects of soil science, physics, technology, mechanization and applied engineering for a sustainable balance among productivity, environmental quality and profitability. The following are examples of suitable topics within the scope of the journal of Soil and Tillage Research: The agricultural and biosystems engineering associated with tillage (including no-tillage, reduced-tillage and direct drilling), irrigation and drainage, crops and crop rotations, fertilization, rehabilitation of mine spoils and processes used to modify soils. Soil change effects on establishment and yield of crops, growth of plants and roots, structure and erosion of soil, cycling of carbon and nutrients, greenhouse gas emissions, leaching, runoff and other processes that affect environmental quality. Characterization or modeling of tillage and field traffic responses, soil, climate, or topographic effects, soil deformation processes, tillage tools, traction devices, energy requirements, economics, surface and subsurface water quality effects, tillage effects on weed, pest and disease control, and their interactions.