1.1. Crop Residue and Conservation Tillage
Crop residue is plant litter (non-photosynthetic vegetation) that accumulates on the surface of agricultural fields, generally after harvest has occurred and crops have senesced. The residues cover the soil surface with a mulch layer that plays an important role in soil conservation by reducing both water-based and wind-based erosion [
1]. Additionally, crop residue can preserve soil moisture, reduce evaporation, inhibit the germination of weeds, increase soil organic carbon, and support healthy soil ecosystems [
2,
3]. The amount of crop residue cover is directly linked to crop system management, responding to tillage practices, crop rotations, and harvest methods.
Reducing tillage intensity, with corresponding increases in crop residue cover, can increase soil water retention and help to control soil erosion, mitigating nutrient losses in runoff [
4]. The diversity of tillage practices employed by farmers (e.g., plow-till, chisel plow, disking, zone till, vertical till, no-till) reflects the continuing evolution of agricultural mechanization, and results in a broad spectrum of crop residue cover outcomes [
5]. High-residue crop management practices are key components of conservation agriculture, and are crucial to promote sustainable cropping systems, complementing additional practices such as reduced chemical usage, improved nutrient management, and diversified crop rotations [
1,
6].
1.2. Chesapeake Bay Setting
Prevention of erosion and runoff, and retention of agricultural nutrients on farmland, are of great importance in the Chesapeake Bay watershed, which has been subject to extensive anthropogenic ecological stressors, including significant impacts from farming [
7]. Elevated nutrient and sediment levels have plagued the Chesapeake Bay for several decades, reducing the extent of submerged aquatic vegetation and creating algal blooms and hypoxic zones in the estuary [
8]. Conservation management practices, including the use of conservation tillage and winter cover crops, have played pivotal roles in reducing nutrient inputs to the estuary [
9]. Therefore, management practices that maintain crop residue on the field surface are promoted by Chesapeake Bay restoration efforts. To verify and promote the implementation of effective conservation tillage practices, there is a need to map and identify the amount and distribution of crop residue in the working farm landscape.
The Chesapeake Bay Program Partnership has defined four categories of tillage intensity for use in their watershed model [
10]: conventional tillage (<15% cover); low residue tillage (15–30% cover); conservation tillage (30–60% cover); and high residue tillage (>60% cover). These classes are similar to those used nationally by the Conservation Technology Information Center (CTIC:
https://www.ctic.org/resourcedisplay/322/), with the addition of the high residue tillage category. The classes with lower percent cover are associated with a higher risk of erosion and sediment transport, due to increased dislodgement of soil particles by rainfall and wind. Greater than 30% residue cover is expected to control roughly 65% of soil erosion, depending on soil type, slope, and local conditions, while >60% cover is expected to control >90% of erosion [
11,
12]. Residue cover outcomes resulting from agronomic management depend on many factors, such as crop residue type (species, size, durability, lignification), climate (moisture and temperature), and tillage intensity.
1.3. Ground-Based Measurement of Crop Residue Cover
Soil tillage intensity is typically characterized by the fraction of soil covered by crop residues in the late spring, shortly after tillage is complete and summer crops have been planted (
https://www.ctic.org/resourcedisplay/255/). At that time there is a brief window of opportunity for the measurement of crop residue cover before crop vegetation covers the soil surface. Various ground-based methods exist for assessing the amount of residue cover on agricultural fields [
13,
14], including roadside surveys, in-field photography, and line-point transects. Each of these methods has its own merits, but the challenges associated with assessing crop residue cover in many fields in a timely manner have been highlighted by various researchers [
15,
16,
17,
18,
19].
Roadside surveys of crop residue cover are made by driving a route through an agricultural area, stopping at intervals, and visually estimating the amount of crop residue cover in fields. The CTIC compiles annual county-level assessments of soil tillage intensity using roadside survey methods (
https://www.ctic.org/resourcedisplay/255/). Roadside surveys are time-effective but are often hampered by only observing field edges near the road, which may not be representative of the whole field, and by the oblique viewing angle. The roadside surveys are therefore somewhat subjective and sometimes tend to overestimate crop residue cover in fields with <30% residue cover and underestimate cover for fields with >30% residue cover [
16,
19].
Photographic methods consist of acquiring proximal vertical photographs or digital images of the field surface and estimating the fraction of the soil surface covered by crop residue. Methods include using software (e.g., SamplePoint [
20]) to overlay a digital grid of crosshairs on each image and visually determining the number of crosshairs that intersect green vegetation, crop residue, or soil. Accuracy depends on the spatial resolution of the images, the contrast between the soil and the crop residue in each image, the number of crosshairs used, and the skill of the observer. In-field photography can be acquired in a time-effective and accurate manner, but requires access to the fields, and the post-processing to determine percent ground cover can be labor intensive.
The line-point transect method uses a string with small equally spaced markers extended over the field surface, and the observer counts the number of markers that intercept crop residue, green vegetation, or soil. Accuracy depends on the length of the line, the number of markers, and the skill of the observer. The line-point transect method used by the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) typically has 100 1-cm markers evenly spaced along a 15-m string [
21]. In-field line-point transect surveys have been demonstrated as being very accurate but are also time consuming to conduct, and do not work well with standing residue.
In contrast to these ground-based methods for measuring crop residue cover, remote sensing from airborne or satellite-based platforms has the potential to offer a rapid and accurate assessment of crop residue cover over large areas in a timely and cost-effective manner, if current limitations can be overcome [
22]. Remote sensing, under optimal circumstances, can provide synoptic observations of field residue conditions that are unparalleled by ground-based surveys in terms of both spatial extent and detail of observations.
1.4. Remote Sensing of Crop Residue
Crop residues have unique spectral absorption features that allow them to be discriminated from soils and vegetation based on their reflectance spectra. They are largely composed of cellulose and lignin, which exhibit absorption features near 2100 nm and 2300 nm [
23,
24,
25,
26]. Additionally, crop residues tend to have a general decrease in spectral reflectance from 1600 nm to 2300 nm, in contrast to soils for which spectral reflectance remains relatively constant from 1600 to 2300 nm. These differences in spectral reflectance can be used to generate spectral indices that distinguish soil from crop residue [
27,
28].
Daughtry et al. [
24,
29] showed that the Cellulose Absorption Index (CAI), which measures the 2100 nm cellulose absorption feature, was very accurate in determining percent residue cover and for distinguishing crop residue from other types of ground cover. However, this index relies on changes in reflectance within relatively narrow spectral bands (<100 nm) that can only be computed using reflectance data from hyperspectral instruments. The Hyperion sensor aboard the Earth Observing−1 satellite provided space-borne hyperspectral imagery that was used by Bannari et al. [
30] to map crop residue, but that instrument was deactivated in 2017 and hyperspectral imagery is currently only available on airborne or proximal platforms. While space-borne hyperspectral sensors are limited in availability and operation, various indices have been developed for mapping crop residue using multispectral satellite imagery [
22,
27,
31], relying on advanced multispectral imagers (i.e., Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), WorldView-3 [
32]) as well as broadband multispectral imagers (i.e., Landsat family, Sentinel-2)
The broadband multispectral satellites provide global coverage, and previous studies have shown success using indices relying on a few relatively broad spectral bands for mapping residue cover at regional scales [
18,
19,
33,
34,
35,
36]. In most cases, the Normalized Difference Tillage Index (NDTI) [
34] was demonstrated to be the best of the Landsat-based tillage indices for estimating residue cover, exploiting the difference in reflectance between the two Landsat shortwave infra-red (SWIR) bands centered near 1600 nm and 2300 nm. However, the Landsat-compatible crop residue indices are dependent on spectrally broad contrasts, which can be strongly influenced by crop residue age and condition, moisture content, soil brightness, and the presence of green vegetation [
22,
37], which limits their effectiveness.
The WorldView-3 (WV3) satellite, launched on August 2014, is now providing advanced multispectral imagery in 16 bands ranging from 400–2500 nm [
38]. This includes eight comparatively narrow SWIR bands, and eight bands in the visible near infrared (VNIR). Four of the SWIR bands are spectrally similar to the SWIR bands employed by the ASTER satellite (
https://asterweb.jpl.nasa.gov/data.asp). The ASTER bands have been used with success to compute advanced multispectral residue indices such as the Lignin Cellulose Absorption Index (LCA) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) [
39,
40]. These indices, calculated from the spectrally narrow SWIR bands of ASTER/WV3, have been shown to be more effective at measuring residue cover than indices derived from the spectrally broad SWIR bands employed by Landsat or Sentinel-2 due to the more precise measurement of cellulose and lignin absorption features [
37,
41]. Although the SWIR instrument of ASTER failed in 2008, WV3 has built upon the ASTER heritage by including additional SWIR bands and higher spatial resolution imagery, enabling the calculation of additional SWIR-based indices suitable for the detection of crop residue cover. The WV3 satellite shows the potential of advanced multispectral imagers to overcome current limitations of remote sensing of crop residue cover and tillage intensity, and to achieve reliable residue cover assessment at the landscape scale [
22,
42].
Residue decomposition stage, water content, and the presence of green vegetation can impact the accuracy of crop residue characterization using spectral indices. Soil and residue water content can have a strong impact on residue cover estimation [
37,
43,
44]. Increasing moisture content reduces reflectance from the visible to the shortwave infrared and attenuates reflectance features, sometimes resulting in the overestimation of residue cover by spectral residue indices [
44]. All residue indices are sensitive to variable moisture conditions, but SINDRI proved more robust than NDTI when no correction for moisture was applied [
37]. Similarly, residue decomposition was shown to affect all SWIR residue indices [
45], but the narrow band SWIR indices (SINDRI, LCA) were less affected than the broadband indices (NDTI). The presence of green vegetation can physically obscure crop residues, and can also attenuate residue absorption features due to the high water content of green vegetation [
22]. Previous studies have dealt with this issue by limiting residue measurement to areas with minimal green vegetation, as indicated by Normalized Difference Vegetation Index (NDVI) values <0.3 [
24,
40].
Our objective was to estimate crop residue cover and classify soil tillage intensity using remotely sensed data from the recently launched advanced multispectral satellite, WorldView-3. Various SWIR spectral indices were compared with in situ measurements of crop residue cover, and maps of residue cover on non-vegetated agricultural fields were produced, with a focus on evaluating the potential for using satellite remote sensing technology to quantify the extent and distribution of crop residue cover on agricultural fields of eastern Maryland, USA.