1. Introduction
Best management practices for croplands often include maintaining crop residues on the soil surface [
1]. Beneficial effects of crop residue cover include decreased soil erosion, increased soil organic matter, improved soil quality and reduced amounts of nutrients that reach streams [
2]. Crop residues often completely cover the soil surface after harvest, but residue cover decreases as the soil is tilled or the residues are harvested for fuel or feed. Simulation models, such as the Environmental Policy Integrated Climate (EPIC) [
3] and Soil and Water Assessment Tool (SWAT) [
4], can predict the overall impact of crop and soil management practices on soil organic carbon, greenhouse gas emissions and water quality. These models also require geospatial information on landscape topography, soil properties, weather and climate, crop type, crop management practices and soil tillage intensity. Appropriate databases exist for all, except for soil tillage intensity.
Soil tillage intensity may be characterized by the fraction of the soil surface covered by crop residues (
fR) shortly after planting: intensive tillage has <15% cover; reduced tillage has 15%–30% cover; and conservation tillage >30% cover [
5]. The line-point transect is the standard technique used by the USDA Natural Resources Conservation Service (NRCS) to quantify crop residue cover [
6], but is impractical for monitoring crop residue cover in many fields in a timely manner. The challenges associated with various methods of assessing crop residue cover were also highlighted by other researchers [
7,
8,
9].
Synoptic remote sensing imagery offers a rapid means for estimating
fR and determining soil tillage intensity if current limitations are overcome [
10,
11]. Reflectance spectra of crop residues and soils are spectrally similar throughout most of the 400–1500 nm wavelength region [
10]. As crop residues weather after harvest, they may be either brighter or darker than the soils depending on soil type, crop type, water content of soil and crop residue and the degree of decomposition of the crop residue, which makes discrimination challenging [
12,
13,
14,
15,
16]. In the shortwave infrared region, the spectra of dry crop residues have absorption features in the 2100–2350 nm wavelength region associated with cellulose and lignin [
17] that are absent in the spectra of soils and green vegetation [
10,
18]. Water reduced reflectance of crop residues and soils at all wavelengths, attenuated the cellulose and lignin absorption features [
19] and increased the uncertainty of
fR estimates [
16,
20]. Thus, any robust method to monitor the spatial variability of soil tillage intensity over large areas must also account for the spatial variability in scene water content and its impact on estimates of crop residue cover.
Remote sensing systems for assessing crop residue cover and water content of soil and crop residues can be sorted into three overlapping classes based on the spectral resolution of the sensors. First, hyperspectral imaging sensors have many contiguous narrow (≤10 nm) spectral bands, provide the flexibility to use spectrum analysis techniques [
16,
17] and develop spectral indices for specific targets of interest. Satellite hyperspectral sensors include the Hyperion Imaging Spectrometer [
21] and the Environmental Mapping and Analysis Program (EnMAP) [
22]. The Cellulose Absorption Index (CAI) [
23], which estimated the depth of the cellulose absorption feature near 2100 nm, is calculated as:
where R
2.0, R
2.1 and R
2.2 refer to reflectance values in 10-nm bands centered at 2030 nm, 2100 nm and 2210 nm, respectively. Although CAI was linearly related to
fR for a wide range of soils and crop residues, the slopes and intercepts of
fR vs. CAI relationships were significantly altered by the water contents of soils and crop residues [
20].
Second, advanced multispectral imagers typically have multiple discrete and relatively narrow (≥30 nm) spectral bands in the 1500–2500-nm wavelength region that are strategically located to identify targets of interest [
24]. These sensors include the WorldView-3 [
25] and Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) [
26]. Both WorldView-3 and ASTER include multiple bands in the 2100–2500-nm wavelength region that have been used to assess crop residue cover [
10,
20,
27]. Probably, the most robust crop residue index for these advanced multispectral sensors is the Shortwave Infrared Normalized Difference Residue Index (SINDRI) [
10], which is calculated as:
where SWIR6 and SWIR7 refer to WorldView-3 SWIR Bands 6 (2185−2225 nm) and 7 (2235−2285 nm), respectively. These WorldView-3 bands also correspond to ASTER Bands A6 and A7. The effects of water on SINDRI are unclear and need to be examined [
10,
28].
Third, broadband multispectral imagers typically have a few relatively broad (≥100 nm) spectral bands including in the 1500–2500-nm wavelength region. These bands are too wide and not properly located to capture the cellulose absorption feature near 2100 nm. These sensors include three versions of Landsat, i.e., Thematic Mapper (TM), Enhanced Thematic Mapper (ETM) and Operational Land Imager (OLI) [
29], as well as the European Space Agency Sentinel-2 [
30]. Several broadband spectral indices have been proposed to assess crop residue cover and tillage intensity [
9,
31]. In most cases, the Normalized Difference Tillage Index (NDTI) [
32] was the best of the Landsat-based tillage indices for estimating
fR [
10] and is calculated as:
where OLI6 and OLI7 correspond to reflectance in Landsat OLI Band 6 (1570–1650 nm) and Band 7 (2110–2290 nm), respectively. Reflectance in the corresponding Landsat TM/ETM+ and Sentinel-2 bands may also be used. The effects of moisture conditions on NDTI are significant [
10,
20], but, to our knowledge, no viable corrections have been reported for this broadband multispectral index.
Water in the crop residues and soils strongly attenuated the reflectance signal across all wavelengths and generally reduced the contrast between soils and crop residues [
16,
20]. Spectral indices using narrow near-infrared and shortwave infrared bands have been correlated with the water content of leaves [
33,
34], soils [
35], plant canopies [
36] and crop residues and soils [
21]. Spectral indices using various combinations of Landsat bands, particularly the shortwave infrared (TM5) together with the near-infrared band (TM4), have provided good estimates of vegetation water content [
37,
38]; however, these indices have not been used to assess water content of crop residues and soils. Wang et al. [
16] simulated reflectance spectra of scenes with varying proportions of crop residue and soils and significantly minimized the effects of moisture using an external parameter orthogonalization (EPO) procedure. However, the EPO protocol for estimating crop residue cover and the scene water content has not been tested with reflectance spectra measured in fields.
Surveys of crop residue cover are typically conducted in the spring shortly after planting, which is often the wettest season of the year. Water contents of soils and crop residues often vary spatially and temporally across fields, even with minor changes in topographic relief. Thus, accurate estimates of fR require concomitant assessments of the water contents of soils and crop residues, preferably using the suite of spectral bands that are available on each remote sensing system.
Our objectives for each sensor class were to: (1) assess the impact of water on the spectral indices for estimating crop residue cover (fR); (2) evaluate spectral water indices for estimating relative water content (RWC) of mixtures of crop residues and soils; and (3) propose methods that mitigate the uncertainty caused by variable moisture conditions on estimates of fR. The broadband multispectral indices demonstrated the capabilities of current remote sensing systems for monitoring fR and scene water content over large areas, while the hyperspectral and advanced multispectral indices showed what may be possible with future remote sensing sensors.
4. General Discussion
In summary, when conditions were dry, crop residue cover was linearly related to each of the three spectral residue indices. However, when relative water content of crop residues and soils varied, estimates of crop residue cover were adversely affected. Spectral water indices were developed to estimate scene relative water content. Pairs of spectral indices were used, one for relative water content and another for crop residue cover; the overall accuracy of crop residue cover estimates when moisture conditions varied.
Both slope and intercept of the
fR vs. CAI were altered by changes in water content under laboratory conditions [
16,
20]. In this study, we showed that the slopes and the intercepts of the
fR vs. CAI followed similar trends for both laboratory and field conditions, confirming that corrections based on moisture conditions significantly improved the ability of CAI to predict crop residue cover. However, the use of CAI as an index for
fR at regional scales is limited by the lack of suitable hyperspectral remote sensing satellite systems. Hyperion data have been used to calculate CAI and to estimate crop residue cover for test sites in Iowa and Indiana [
46,
47]. However, the narrow swath width of Hyperion images cannot provide the wall-to-wall coverage needed for regional scale monitoring.
Advanced multispectral sensors with relatively narrow shortwave infrared bands, such as WorldView-3, are alternatives to hyperspectral imaging spectrometers. Our results showed that fR estimated using SINDRI is less sensitive to variations in scene moisture conditions than fR estimated with CAI or NDTI. Corrections based on scene moisture conditions slightly improved the ability of SINDRI to predict crop residue cover.
Variations in scene moisture conditions adversely affected
fR estimated using NDTI. Both the slope and intercept of the
fR vs. NDTI regression were significantly altered by variations in scene moisture conditions. Nevertheless, NDTI has been used successfully to distinguish a few broad tillage classes [
32,
48]. These studies typically evaluated test sites within a single Landsat image. Thus, variations in moisture conditions were probably small and did not significantly alter classification accuracy. Other studies [
28,
49] have used multi-temporal Landsat images to classify tillage intensity. Gelder et al. [
49] selected Landsat images acquired more than two days after precipitation events, which allowed the surface layer to dry for most soils. In contrast, we created broad ranges of scene relative water contents to test the robustness of NDTI for estimating crop residue cover. Under relatively dry (RWC < 0.25) conditions and possibly very wet (RWC > 0.70) conditions, the regressions of
fR vs. NDTI were significant. For these very wet conditions, reflectance in the shortwave infrared bands of Landsat is significantly attenuated by water, and the spectral indices must be used with caution. However, NDTI was not a suitable predictor of
fR for intermediate values of RWC. Thus, assessments of the scene water contents are particularly crucial for estimating
fR using NDTI.
In the field experiment, some of the unexplained variability was probably associated with the experimental protocol for determining residue cover and RWC. The accuracy of SamplePoint measurements to determine ground-cover under field conditions ranges from 92%–98% depending on the quality of the image analyzed [
41]. Determining the water content of soil and residue samples is associated with some uncertainty, as the moisture distribution is not homogenous and is affected by soil texture and litter composition [
50,
51]. Considering this unexplained variability inherent to the field dataset, the accuracy attained (RMSE = 0.09–0.12) when using the equations based on CAI and SINDRI to predict
fR was very high.
In our field experiment, the soil, maize residues and water contents were quite uniform. Within any real agricultural scene, variations in topography, soil roughness, soil texture, green vegetation cover and precipitation affect moisture conditions and spectral reflectance. Therefore, these correlations to estimate RWC in a scene should be used with caution. Additional information about the local soil reflectance and crop type may be useful for estimating scene moisture conditions.
Baird and Baret [
52] proposed the CRIM (Crop Residue Index Multiband) method for estimating crop residue cover, which is based on the distance between the soil line and the residue line. In our experiment (data not shown), the contrasts between the soil and residue lines for pairs of visible (OLI4), near infrared (OLI5) and SWIR (OLI6 and OLI7) were very low, which limited the accuracy of the relationship between CRIM and the crop residue cover.
Current hyperspectral sensors (e.g., Hyperion and EnMAP) and advanced multispectral sensors (e.g., WorldView-3) have narrow swaths and are well suited for studying episodic events, but do not have the capacity to map large areas in a timely manner [
25]. Therefore, the challenge is how to best use a few hyperspectral and/or advanced multispectral images and many multispectral images (e.g., Landsat, Sentinel-2) to produce regional surveys and maps of crop residue cover and tillage intensity. For example, a simple robust method for quantitatively mapping the fractions of photosynthetic vegetation, non-photosynthetic vegetation and bare soil was developed using Hyperion and MODIS data [
52]. The three fractions were successfully mapped over large areas of Australian savannas with daily MODIS data. However, the relatively coarse spatial resolution of MODIS data (i.e., 250–1000 m) would be a limitation for assessing crop residue cover and tillage intensity in agricultural regions with many fields and diverse crops. Another promising example is the Spatial Temporal Adaptive Reflectance Fusion Model (STARFM [
53,
54]), which combines Landsat and MODIS reflectance data to produce composite images with the spatial resolution of Landsat and the temporal resolution of MODIS. These fused Landsat-MODIS images potentially could provide reliable temporal profiles of NDTI for estimating crop residue cover and soil tillage intensity using the minNDTI approach [
28].