Geology topics

Michael T. Marshall

Publications and source records attributed to Michael T. Marshall.

3 recordsLinked to original sources

USGS2016

Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)

Evapotranspiration (ET) is an important component of micro- and macro-scale climatic processes. In agriculture, estimates of ET are frequently used to monitor droughts, schedule irrigation, and assess crop water productivity over large areas. Currently, in situ measurements of ET are difficult to scale up for regional applications, so remote sensing technology has been increasingly used to estimate crop ET. Ratio-based vegetation indices retrieved from optical remote sensing, like the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index, and Enhanced Vegetation Index are critical components of these models, particularly for the partitioning of ET into transpiration and soil evaporation. These indices have their limitations, however, and can induce large model bias and error. In this study, micrometeorological and spectroradiometric data collected over two growing seasons in cotton, maize, and rice fields in the Central Valley of California were used to identify spectral wavelengths from 428 to 2295 nm that produced the highest correlation to and lowest error with ET, transpiration, and soil evaporation. The analysis was performed with hyperspectral narrowbands (HNBs) at 10 nm intervals and multispectral broadbands (MSBBs) commonly retrieved by Earth observation platforms. The study revealed that (1) HNB indices consistently explained more variability in ET (Δ R 2 = 0.12), transpiration (Δ R 2 = 0.17), and soil evaporation (Δ R 2 = 0.14) than MSBB indices; (2) the relationship between transpiration using the ratio-based index most commonly used for ET modeling, NDVI, was strong ( R 2 = 0.51), but the hyperspectral equivalent was superior ( R 2 = 0.68); and (3) soil evaporation was not estimated well using ratio-based indices from the literature (highest R 2 = 0.37), but could be after further evaluation, using ratio-based indices centered on 743 and 953 nm ( R 2 = 0.72) or 428 and 1518 nm ( R 2 = 0.69).

California↗

USGS2015

Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation

Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5–31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3–33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the “red-edge” spectral range (700–740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400–2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice ( R 2 = 0.91); 925 and 1104 nm for alfalfa ( R 2 = 0.81); 722 and 732 nm for cotton ( R 2 = 0.97); and 529 and 895 nm for maize ( R 2 = 0.94). The higher spectral resolution of the EO-1 Hyperion hyperspectral sensor and the ability of users to choose distinct HNBs for improved crop biomass estimation outweigh the benefits that come with higher spatial resolution of MSBBs.

California↗

USGS2014

Biomass modeling of four water intensiveleading world crops using hyperspectral narrowbands in support of HyspIRI Mission

New satellite missions are expected to record high spectral resolution information globally and consistently for the first time, so it is important to identify modeling techniques that take advantage of these new data. In this paper, we estimate biomass for four major crops using ground-based hyperspectral narrowbands. The spectra and their derivatives are evaluated using three modeling techniques: two-band hyperspectral vegetation indices (HVIs), multiple band-HVIs (MB-HVIs) developed from Sequential Search Methods (SSM), and MB-HVIs developed from Principal Component Regression. Overall, the two-band HVIs and MB-HVIs developed from SSMs using first derivative transformed spectra in the visible blue and green and NIR explained more biomass variability and had lower error than the other approaches or transformations; however a better search criterion needs to be developed in order to reflect the true ability of the two-band HVI approach. Short-Wave Infrared 1 (1000 to 1700 nm) proved less effective, but still important in the final models.

Photogrammetric Engineering and Remote Sensing↗