Estimation of Crop Residue Cover Utilizing Multiple Ground Truth Survey Techniques and Multi-Satellite Regression Models

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-11, Vol.16 (22), p.4185
• Main Authors: Williams, Forrest, Gelder, Brian, Presley, DeAnn, Pape, Bryce, Einck, Andrea
• Format: Article
• Language: English
• Subjects: Agricultural land; Conservation; Cover crops; crop residue cover; crop residue indexes; Crop residues; Data analysis; Data collection; Datasets; Economic impact; Estimates; Google Earth Engine; Information management; Kansas; Methods; Natural resources; Nebraska; normalized difference tillage index (NDTI); Polls & surveys; Precipitation; Regression analysis; Regression models; Remote sensing; residue survey; Residues; Soil erosion; Surveys; Tillage; tillage intensity; United States; Watersheds; Windshields
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16224185

Soil erosion within agricultural landscapes has significant environmental and economic impacts and is strongly driven by reduced residue cover in agricultural fields. Large-area soil erosion models such as the Daily Erosion Project are important tools for understanding the patterns of soil erosion, but they rely on the accurate estimation of crop residue cover over large regions to infer the tillage practices, an erosion model input. Remote sensing analyses are becoming accepted as a reliable way to estimate crop residue cover, but most use localized training datasets that may not scale well outside small study areas. An alternative source of training data may be commonly conducted tillage surveys that capture information via rapid “windshield” surveys. In this study, we utilized the Google Earth Engine to assess the utility of three crop residue survey types (windshield tillage surveys, windshield binned residue surveys, and photo analysis surveys) and one synthetic survey (retroactively binned photo analysis data) as sources of training data for crop residue cover regressions. We found that neither windshield-based survey method was able to produce reliable regressions but that they can produce reasonable distinctions between low-residue and high-residue fields. On the other hand, both photo analysis and retroactively binned photo analysis survey data were able to produce reliable regressions with r2 values of 0.57 and 0.56, respectively. Overall, this study demonstrates that photo analysis surveys are the most reliable dataset to use when creating crop residue cover models, but we also acknowledge that these surveys are expensive to conduct and suggest some ways these surveys could be made more efficient in the future.