Abstract
Growing cover or winter crops and retaining crop residue on agricultural lands are considered beneficial management practices to address soil health and water quality. Remote sensing is a valuable tool to assess and map crop residue cover and cover crops. The objective of this study is to evaluate the performance of linear spectral unmixing for estimating soil cover in the non-growing season (November–May) over the Canadian Lake Erie Basin using seasonal multitemporal satellite imagery. Soil cover ground measurements and multispectral Landsat-8 imagery were acquired for two areas throughout the 2015–2016 non-growing season. Vertical soil cover photos were collected from up to 40 residue and 30 cover crop fields for each area (e.g., Elgin and Essex sites) when harvest, cloud, and snow conditions permitted. Images and data were reviewed and compiled to represent a complete coverage of the basin for three time periods (post-harvest, pre-planting, and post-planting). The correlations between field measured and satellite imagery estimated soil covers (e.g., residue and green) were evaluated by coefficient of determination (R2) and root mean square error (RMSE). Overall, spectral unmixing of satellite imagery is well suited for estimating soil cover in the non-growing season. Spectral unmixing using three-endmembers (i.e., corn residue-soil-green cover; soybean residue-soil-green cover) showed higher correlations with field measured soil cover than spectral unmixing using two- or four-endmembers. For the nine non-growing season images analyzed, the residue and green cover fractions derived from linear spectral unmixing using corn residue-soil-green cover endmembers were highly correlated with the field-measured data (mean R2 of 0.70 and 0.86, respectively). The results of this study support the use of remote sensing and spectral unmixing techniques for monitoring performance metrics for government initiatives, such as the Canada-Ontario Lake Erie Action Plan, and as input for sustainability indicators that both require knowledge about non-growing season land management over a large area.
Highlights
Information about soil cover at a regional scale is important to support modeling and monitoring of agricultural activities, as well as policy and program implementation
Unmixing using 4 endmembers had a significant correlation between measured residue and 4EndSB but not 4EndCR; both had a very low R2 when compared to measured residue cover
We were not able identify the exact mechanism for the differences observed in this study; it should be noted that there are several differences between these studies as different software was used for the spectral unmixing, no sites with green cover were included in [29], soil types were more uniform in the study area used by [29], and the level and degradation of soybean residues may have been different across our regions within eastern (Ottawa region) and southwestern (Elgin/Essex region) Ontario
Summary
Information about soil cover at a regional scale is important to support modeling and monitoring of agricultural activities, as well as policy and program implementation. There is particular interest in the management of agricultural land in the Lake Erie basin during the non-growing season as this is when most of the non-point source nutrient run-off and loadings to the lake occur. Both crop residues (dead or non-photosynthetic vegetation) and cover/winter crops (living) are considered beneficial for facilitating infiltration and reducing soil erosion and nutrient loss [1,2,3]. At the Lake Erie Basin scale, these changes in rotations and tillage practices (e.g., no-till or reduced tillage) can collectively have an effect on Great Lakes water quality [8,9,10])
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