Guy Serbin

An Improved ASTER Index for Remote Sensing of Crop Residue

Unlike traditional ground-based methodology, remote sensing allows for the rapid estimation of crop residue cover (fR). While the Cellulose Absorption Index (CAI) is ideal for fR 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 fR data for carbon cycle modeling and tillage verification.

Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover

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.

Multitemporal remote sensing of crop residue cover and tillage practices: A validation of the minNDTI strategy in the United States

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 Earths 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.

Assessment of spectral indices for cover estimation of senescent vegetation

Quantification of dry plant matter (crop residue, senesced foliage, non-photosynthetic vegetation, or plant litter) surface cover (f R) is important for assessing agricultural tillage practices, carbon sequestration, rangeland health, or brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two spectral indices that can remotely estimate f R. CAI and SINDRI utilize three and two spectral bands, respectively, so SINDRI is expected to be less expensive to implement in future satellite sensors. We assessed the contrast of CAI and SINDRI with respect to soil reflectance spectra. Estimating f R with CAI is possible for all soils. However, a number of soil samples had positive SINDRI values due to various soil minerals, such as gibbsite and antigorite, which would be interpreted as high f R, and could limit its usefulness in some areas. Therefore, SINDRI is less applicable for estimating f R, even with reduced implementation costs.

Improved Remotely-Sensed Estimates of Crop Residue Cover by Incorporating Soils Information

Remote sensing allows for the rapid determination of crop residue cover. The Cellulose Absorption Index (CAI) has been shown to more accurately estimate residue cover and non-photosynthetic vegetation than other indices. CAI is useful as values are linear areal mixtures of soil and residue spectral properties. Our research shows that spatial soil property data can be used for calibration to improve residue cover estimates. Furthermore, residue cover estimations are affected by rainfall and live green vegetation, and these need to be accounted for in analyses. This work supports the concept that future remote sensing platforms should include CAI bands to allow for better estimation of residue cover.

Remote Sensing of Soil Carbon and Greenhouse Gas Dynamics across Agricultural Landscapes

Overall impact of GRACEnet management strategies for enhancing soil C sequestration and reducing greenhouse gases emissions requires extending results from small plot or field experiments to regional and national scales. This spatial scaling task is not trivial because the mechanisms controlling carbon, water, and energy exchanges are nonlinear and interact with each other. Remote sensing offers the only practical method to account for the spatial and temporal variability inherent across agricultural landscapes. In this chapter, the fundamental spectral properties of vegetation and soils are reviewed and potential synergies of in situ and remotely sensed measurements for providing frequent, spatially explicit information about agricultural landscapes are examined. Data fusion and assimilation techniques for merging data acquired at different spatial and temporal resolutions and techniques for creating synthetic datasets with high spatial and temporal resolutions are discussed. The next step for verifying GRACEnet practices will be to link process models with these enhanced datasets to reliably describe ecosystem functions at various scales.

Assessment of spectral indices for crop residue cover estimation

The quantification of surficial crop residue (nonphotosynthetic vegetation) cover is important for assessing agricultural tillage practices, rangeland health, and brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two spectral indices that have shown promise for remote estimation of crop residue cover. CAI and SINDRI utilize three and two spectral bands, respectively, rendering the latter less expensive to implement in future satellite sensors. This study shows that while CAI always contrasts well among soils, crop residues, and live vegetation, this is not always the case for SINDRI. A small number of surficial soil samples had positive SINDRI values that have reduced contrasts among crop residues. Some of these soils were biased by SINDRI-positive component minerals. As such, SINDRI is less applicable for remote crop residue cover estimation, even with reduced implementation costs.

Wheat straw composition and spectral reflectance changes during decomposition

Quantification of crop residue cover is required to assess the extent of conservation tillage. Our objectives were to measure the changes in wheat straw composition and spectral reflectance during decomposition and to assess impact of these changes on remotely sensed estimates of residue cover. Mesh bags filled with wheat straw were placed on the soil surface and removed at intervals over 22 months. The relative proportions of cellulose and hemicellulose in the straw declined while lignin increased. Reflectance spectra of wheat straw and two soils were measured over 350-2500 nm region. Absorption features in the reflectance spectra associated with cellulose diminished as the straw decomposed. The Cellulose Absorption Index (CAI) was a robust estimator of crop residue cover. Advanced multi-spectral sensors with multiple relatively narrow shortwave infrared bands or hyperspectral sensors are needed to assess crop residue cover reliably over diverse agricultural landscapes.