Southern great plains 1997 hydrological experiment: vegetation sampling and data documentation

"Prepared for the United States Department of Agriculture, Agricultural Research Service"--Cover

Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices

Fuel moisture content (FMC) is an important variable for predicting the occurrence and spread of wildfire. Because FMC is calculated from the ratio of canopy water content to dry-matter content, we hypothesized that FMC may be estimated by remote sensing with a ratio of a vegetation water index to a vegetation dry-matter index. Four vegetation water indices, six dry-matter indices, and the resulting water/dry-matter index ratios were calculated using simulated leaf reflectances from the PROSPECT model. Two water indices, the Normalized Difference Infrared Index (NDII) and the Normalized Difference Water Index (NDWI), were more correlated with leaf water content than with FMC, and were not correlated with leaf dry-matter content. Two dry-matter indices, the Normalized Dry Matter Index (NDMI) and a recent index (unnamed) were correlated to leaf dry matter content, were inversely correlated with FMC, and were not correlated with water content. Ratios of these water indices and these dry-matter indices were highly and consistently correlated with FMC. Ratios of other water indices with other dry-matter indices were not consistently correlated with FMC. The ratio of NDII with NDMI was strongly related to FMC by a quadratic polynomial equation with an R2 of 0.947. Spectral reflectance data were acquired for single leaves and leaf stacks of Quercus alba, Acer rubrum, and Zea mays; the relationship between FMC and NDII/NDMI had an R2 of 0.853 and was almost identical to the equation from the PROSPECT model simulations. For the SAIL model simulations, the relationship between NDII/NDMI and FMC at the canopy scale had an R2 of 0.900, but the quadratic polynomial equation differed from the equations determined from the PROSPECT simulations and spectral reflectance data. NDMI requires narrow-band sensors to measure the effect of dry matter on reflectance at 1722 nm whereas NDII may be determined with many different sensors. Therefore, monitoring FMC with NDII/NDMI requires either a new sensor or a combination of two sensors, one with high temporal resolution for monitoring water content and one with high spectral resolution for estimating dry-matter content

Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions

Towards estimation of canopy foliar biomass with spectral reflectance measurements

Canopy foliar biomass, defined as the product of leaf dry matter content and leaf area index, is an important measurement for global biogeochemical cycles. This study explores the potential for retrieving foliar biomass in green canopies using a spectral index, the Normalized Dry Matter Index (NDMI). This narrow-band index is based on absorption at the C–H bond stretch overtone and is correlated with leaf dry matter content in fresh green leaves. PROSPECT and SAIL model simulations suggest that the NDMI at the canopy scale is able to minimize the effects of leaf thickness and leaf water content and to maximize sensitivity to variation in canopy foliar biomass. The simulation outputs were analyzed with an ANOVA, and 87% of the variation in the NDMI is explained by leaf dry matter content. The NDMI was linearly related to foliar biomass (g cm−2) from model simulations (R2=0.97). The NDMI calculated from spectral reflectances for one to four stacked leaves was also correlated with total leaf biomass (R2=0.59). These results suggest that it may be possible to determine foliar biomass from airborne and satellite-borne imaging spectrometers, such as NASA\u27s HyspIRI mission

Spectral detection of crop residues for soil conservation from conventional and large biomass soybean

A spectrally derived cellulose absorption index (CAI) was tested to determine its value as a remote sensing method for detecting crop residue ground coverage for soil erosion control in soybean (Glycine max (L.) Merr.). Soybean produces inadequate crop residue for soil conservation purposes in many production years. Crop residues left on the soil surface after harvest slow soil erosion rates. The CAI remote sensing technique was tested over field plots of conventional and large biomass soybean (LBS) with known above ground crop residue biomass and surface coverage. New LBS types are being bred and tested at the Beltsville Agricultural Research Center (BARC), Beltsville, Maryland, US, and can grow to heights of 1.8 m and produce increased amounts of crop residue compared to conventional cultivars. The highest performing LBS line for these traits provided 2963 kg/ha more crop residue biomass and provided a maximum increase of 42% more crop residue cover than the poorest performing conventional soybean. The comparison of LBS versus conventional soybean provided a wide range of soybean residue coverage for testing the CAI remote sensing algorithm. Spectrally derived CAI measures of crop residue were significantly associated with physical ground measurements of crop biomass at harvest and % cover after over wintering. Significant correlations were found between, the CAI and at harvest biomass (r2 = 0.66), between, the CAI and the line point transect measurement (r2 = 0.74), and between, CAI and the analysis of red-green-blue digital imagery (r2 = 0.74) for measuring crop residue cover. These findings indicate that LBS can increase crop residue biomass and crop residue soil coverage by soybean litter and these factors can be detected by remote sensing methods in the field

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll with points at (670 nm, R670), (550 nm, R550), and (480 nm, R480), where Rλ is the spectral reflectance at wavelengths of 670, 550 and 480, respectively. The equation is TGI = −0.5 [(670 − 480) (R670 − R550) − (670 − 550) (R670 − R480)]. In 1999, investigators funded by NASA’s Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated maize in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper (TM) data were acquired along with leaf chlorophyll meter and other data on three dates in July during late vegetative growth and early reproductive growth. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Landsat TM, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms

Multispectral satellite mapping of crop residue cover and tillage intensity in Iowa

Quantifying crop residue cover is crucial for identifying tillage intensity and evaluating effectiveness of conservation management practices across large geographic areas. Current assessment protocols are labor intensive, time consuming, and costly. Our objective was to assess crop residue cover and soil tillage intensity in a watershed in central Iowa for three years (2009 to 2011) using multispectral satellite images. The watershed is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.), which are grown on glacial-till derived soils across 85% of the land area. For each year, crop residue cover was measured for a few fields using the line-point transect method or visually estimating surface cover through roadside surveys. Conservation tillage fields had ≥30% residue cover, while more intensively tilled fields ha

Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions