E.R. Hunt

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.

Using canopy reflectance models and spectral angles to assess potential of remote sensing to detect invasive weeds.

One of the goals of applied remote sensing is to map locations of invasive weeds. However, differences in plant cover and leaf area index (LAI) alter canopy reflectance, making detection of a single species difficult. Variation in canopy reflectance may be simulated using the Scattering by Arbitrarily Inclined Leaves (SAIL) model. Simulated reflectances are used to calculate spectral angles to determine the separability of an invasive weed from co-occurring vegetation. Leafy spurge is a noxious invasive weed with yellow-green flower-bracts. Spectral angles from SAIL model simulations show that flowering leafy spurge may be detected when LAI is greater than 1.0 and flower- bract cover is greater than 10%. A threshold of 3.5 deg (0.061 radians) was determined to provide the best separation between leafy spurge and co-occurring vegetation. To test this prediction, the Spectral Angle Mapper was used to classify leafy spurge using AVIRIS, Landsat ETM+ and SPOT data. Classification accuracy was inversely related to simulated spectral angles from the SAIL model analyses. Using canopy reflectance models and spectral angles may help identify those invasive species that are potentially detectable by remote sensing, and may indicate the conditions where detection will be problematic based on variation of LAI, cover and other variables.

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.

Assessing the extent of conservation tillage in agricultural landscapes

Crop residue (or plant litter) on the soil surface can decrease soil erosion and runoff and improve soil quality. Quantification of crop residue cover is required to evaluate the effectiveness of conservation tillage practices as well as the extent of biofuel harvesting. Remote sensing techniques can provide reliable assessment od crop residue cover over large fields. With Landsat Thematic Mapper bands, crop residues can be brighter or darker than soils depending on soil type, crop type, moisture content, and residue age. With hyperspectral reflectance data, relatively narrow absorption features, centered near 2100 and 2300 nm, can be detected that are associated with cellulose and lignin concentrations. These features are evident in reflectance spectra of crop residues, but not in reflectance spectra of soils. Our objectives were to: (1) estimate crop residue cover using remotely sensed data over an agricultural site in central Iowa, and (2) evaluate alternative, less labor-intensive sampling schemes for acquiring crop residue cover surface reference data. We acquired EO-1 Hyperion imaging spectrometer data over agricultural fields in central Iowa shortly after planting in May 2004 and 2005. Crop residue cover was also measured in corn and soybean fields using line-point transects. The cellulose absorption index (CAI), which measured the relative intensity of the absorption feature near 2100 nm, was calculated using three relatively narrow bands centered at 2030, 2100, and 2210 nm. Results showed that crop residue cover was linearly related to CAI. Changes in the slopes of the regression line from year to year were related to scene moisture conditions. Tillage intensity classes corresponding to conventional tillage (≤ 30% cover) and conservation tillage (> 30% cover) were correctly identified in 75-82% of the fields. In addition, by combining information from previous season’s crop classification with crop residue cover after planting, an inventory of soil tillage intensity by previous crop was generated for the whole Hyperion scene for each year. Inventories and maps of tillage intensity are required for field- and watershed scale models to evaluate management practices that maximize production and minimize environmental impact.

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.

Remote Sensing of Canopy Water Content During SMEX'04 and SMEX'05 Using Shortwave-Infrared Reflectances

The Soil Moisture Experiments in 2004 and 2005 were conducted to validate algorithms for soil moisture retrievals. One of the key parameters for determination of soil moisture from microwave sensors is the vegetation water content of canopy and stems. We tested if canopy water content could be determined from reflectances in the shortwave-infrared and if the amount of canopy water content was related to the total vegetation water content by allometric equations. The normalized difference infrared index (NDII) was linearly related to canopy water content for all plants up to an equivalent water thickness of 1.0 mm. The biggest factor affecting the estimation of canopy water content was the soil background reflectance. For corn and soybean canopy equivalent water thickness were linearly related to total vegetation water content. However, there may be a separate allometric equation required for each vegetation type.

Scaling Vegetation Water Content from Thematic Mapper to MODIS During SMEX04

Vegetation water content (VWC) is important for accurate retrievals of soil moisture using microwave sensors and may be important for determining water stress and forest fire potential. The MODerate resolution Imaging Spectroradiometer (MODIS) and future operational sensors have bands in the shortwave infrared region which can be used for monitoring VWC. The Soil Moisture Experiments 2004 (SMEX04) were conducted during the summer-monsoon season in Arizona, USA, and Sonora, Mexico, as part of the North American Monsoon Experiment. Plots from different vegetation types were sampled for leaf area index and leaf equivalent water thickness. Landsat 5 Thematic Mapper (TM) and MODIS imagery were acquired for three dates, before, during and after the SMEX04 experiment. The Normalized Difference Infrared Index [NDII = (R850 - R1650)/(R850 + R1650)] was linearly related to canopy equivalent water thickness for the Landsat 5 TM data. The TM-estimated canopy equivalent water thickness were aggregated and linearly related to canopy equivalent water thickness from MODIS, showing that MODIS and future sensors would be useful in estimating vegetation water content.