1. Introduction
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]. Several organizations (e.g., Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), Agriculture and Agri-Food Canada (AAFC), and United States Department of Agriculture (USDA)) are promoting cover crops and retaining crop residue as beneficial management practices (BMPs) to address soil health and water quality and wish to track adoption of these practices [
4,
5,
6]. There is also the question of whether fall tillage/bare soil area may be increasing in the Canadian portion of the basin after years of increases in reduced tillage. Census reporting of no-tillage land preparation for seeding increased from 1991 until 2011, but in 2016 conventional tillage, which incorporates most of the crop residue into the soil, did show an increase [
7]. Therefore, improved datasets are needed for agricultural land in the Lake Erie region, that account for the dynamic land cover that changes from year-to-year as a result of adopted crop and tillage rotations. 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]).
Crop residue cover is currently measured in the field using one or more conventional methods such as line-transect, meter stick, photographic comparison, hand-held app, or photographic-grid [
11,
12,
13,
14]. However, most of these methods are time consuming, labor intensive, and cannot provide continuous data over large areas, as percent residue cover is estimated over spatially and temporally disconnected fields. In the United States, the Conservation Technology Information Center (CTIC) has been instrumental in tracking changes in land management through the Crop Residue Management (CRM) survey which involved county level road-side surveys from 1998–2004 and again in 2006 and 2008 [
15]. The CTIC, with Applied Geosolutions and The Nature Conservancy, have now implemented the Operational Tillage Information System (OpTIS) [
16,
17], a remote sensing-based survey of both tillage and cover crop acres for 645 counties in and around the U.S. Corn Belt Land Resources Region-M, including some in the Lake Erie Basin. To our knowledge, there are no current non-growing season soil cover data or maps (e.g., bare soil, residue, or living crop) for the Canadian Lake Erie Basin. Therefore, it is important to develop methodologies for on-going monitoring of soil cover (cover/winter crops and crop residue) at large scales to meet the needs of land management decision makers.
Many studies have found medium-resolution remote sensing imagery (e.g., Landsat products) to be appropriate for residue and cover crop mapping over large areas [
18,
19,
20,
21,
22,
23,
24,
25,
26,
27] given its high overall data quality (e.g., temporal, spatial, and radiometric resolutions). Most of these studies used spectral indices (e.g., the minimum normalized difference tillage index (NDTI) and the Normalized Difference Vegetated Index (NDVI)) to map crop residue cover [
28]. Pacheco and McNairn [
29] used linear spectral unmixing analysis with multispectral images (e.g., Landsat-5 TM and SPOT-4 and -5) to estimate the fractional abundance of a specific residue type (e.g., corn percentage (%) vs. soybean %) within a single pixel. The authors [
29], thus, demonstrated that the linear spectral unmixing analysis technique is an alternative approach to estimate percent crop residue cover. Unlike spectral indices, spectral unmixing analysis uses the information from all available spectral bands to establish the contribution of soil covers (e.g., crop residue(s), bare soil, green vegetative cover) to total reflectance and generates fraction maps as outputs, which provide the proportion (0 to 1) of each soil cover present in each pixel [
29].
In some studies, where only one type of soil cover is studied, pixels or fields have been screened in or out of the study based on a threshold of green cover [
30] to improve results. Spectral unmixing which includes both residue and green cover endmembers may be one way to overcome these interference issues and determine both pieces of soil cover information simultaneously [
31,
32,
33], and thereby determine a total soil cover (i.e., total cover = residue + green fraction). In addition, most of the previous medium-resolution remote sensing imagery approaches aimed to map crop residue cover using a single imagery-date; a few studies have used multitemporal remote sensing datasets to monitor crop residue cover changes through time [
28,
34,
35,
36,
37]. Other studies used multitemporal remote sensing datasets and then selected the minimum NDTI from over the time period [
28,
36].
The main objective of this study was to test and evaluate the performance of the linear spectral unmixing analysis technique for estimating soil cover (e.g., bare soil, crop residue, or living crop) using seasonal multitemporal Landsat-8 imagery and to validate against ground measurements over the Canadian Lake Erie Basin. We hypothesize that linear spectral unmixing can be an appropriate technique to map residue and green cover simultaneously, over large geographic regions. More specifically, this study aimed to (i) quantify soil cover fractions for the 2015–2016 non-growing season; (ii) compare the performance of the different possible unmixing endmember combinations; (iii) compare the performance of the best unmixing endmember combination to spectral indices results in relation to ground data; and (iv) evaluate the overall accuracy of residue, green and total cover estimates and classes using the unmixing results.
4. Conclusions
In this paper, accuracy assessments of spectral unmixing data derived from Landsat-8 imagery were made using field data collected during the 2015-2016 post-harvest, pre-planting, and post-planting time periods. The overall accuracies of green and residue covers varied from 77%–95% and 44%–85%, respectively. The analysis indicated that the crop residue type selected for 3 endmember unmixing was not critical; both soybean and corn residue endmembers provided similar and reasonable unmixing results for residue cover and green cover estimates, though which crop endmember had the better fit varied by image and date. It is reasonable to regularly use corn residue as an endmember to avoid having to adjust soybean spectral signatures because of the unlikelihood of finding a 100% pure soybean residue endmember. The results from this study could support the development of operational map products that could be used for performance metrics for the Canada-Ontario Lake Erie Action Plan and as inputs for sustainability indicators that both require knowledge about non-growing season land management over a large area. Soil cover maps of residue and green fractions that have this level of detail are not routinely produced for the Lake Erie Basin, and the spectral signatures generated from this study could be used and validated as baselines for the monitoring of soil cover for other time periods. A thorough study of the spatial patterns of change of soil cover in relation to error levels should be performed to fully assess the advantages and limitations of spectral unmixing for change detection both within and beyond one season.
Overall, spectral unmixing has proved to be cost and time effective. It produced reasonably accurate soil cover classification (e.g., corn and soybean residue cover, green cover and total cover) results of individual imagery and provided a quantitative assessment of the fraction of key selected soil covers in the Canadian Lake Erie basin. However, using satellite imagery to map crop residues showed some limitations possibly due to the inherent difficulties in separating the crop residue spectral signatures from that of soil and the insufficient coverage due to the presence of clouds. Given our relatively large data set of field observations available for model evaluation, further research into the application of nonlinear unmixing approaches should be undertaken [
51]. Future work should also benefit from the availability of Sentinel 2A and B satellite images with a revisit time of 5 days, for improving soil cover monitoring. Sentinel 2A and B improved temporal resolution will allow for more frequently available data than Landsat-8 and, therefore, will increase the opportunity for the capture of more optically useful images with minimal cloud cover. This in turn will help in achieving sufficient progress in soil cover mapping, measuring, and estimating spatial variability in crop residue and cover crops over large areas.