Unlike traditional ground-based methodology, remote sensing allows for the rapid estimation of crop residue cover (f_R). While the Cellulose Absorption Index (CAI) is ideal for f_R estimation, a new index, the Shortwave Infrared Normalized Difference Residue Index (SINDRI), utilizing ASTER bands 6 and 7, is proposed for future multispectral sensors and would be less costly to implement. SINDRI performed almost as well as CAI and better than other indices at five locations in the USA on multiple dates. A minimal upgrade from one broad band to two narrow bands would provide f_R data for carbon cycle modeling and tillage verification.

Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during the decay process and their impact on estimates of fR were assessed. Proportions of cellulose and hemicellulose declined, while lignin increased. Spectral features associated with cellulose diminished during decomposition. Narrow-band spectral residue indices robustly estimated fR, while broad-band indices were inconsistent. Advanced multi-spectral sensors or hyperspectral sensors are required to assess fR reliably over diverse agricultural landscapes.

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R² = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R² = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R² = 0.44), with significantly increased interference from green vegetation.

Accurate, site-specific tillage information forms an important dimension for development of effective agricultural management practices and policies. Landsat Thematic Mapper (TM) imagery provides the opportunity for systematic mapping of tillage practices via crop residue (plant litter or senescent or nonphotosynthetic vegetation) cover (CRC) estimation at broad scales because of its repetitive coverage of the Earth's land areas over several decades. This study evaluated the effectiveness of a multitemporal approach using the minimum values of Normalized Difference Tillage Index (minNDTI) for assessing CRC at multiple locations over several years. Local models were generated for each dataset. In addition, we tested the feasibility of a regional model in mapping CRC. Results show that the minNDTI method was able to estimate CRC, and a regional model is possible. We found that in addition to the known impact of emergent green vegetation, soil moisture and organic carbon (C) can also confound the NDTI signal, thereby underestimating CRC for low-lying wet and dark areas. Accuracy of the minNDTI technique is comparable to the hyperspectral Cellulose Absorption Index (CAI) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Shortwave Infrared Normalized Difference Residue Index (SINDRI) for tillage classification. The minNDTI technique is currently the best for monitoring CRC and tillage practices from space, opening the door for generating field-level tillage maps at broad spatial and temporal scales.