Geology topics

A.R. Huete

Publications and source records attributed to A.R. Huete.

3 recordsLinked to original sources

USGS2007

Terrestrial remote sensing science and algorithms planned for EOS/MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.

International Journal of Remote Sensing↗

USGS2007

Relationship between evapotranspiration and precipitation pulses in a semiarid rangeland estimated by moisture flux towers and MODIS vegetation indices

We used moisture Bowen ratio flux tower data and the enhanced vegetation index (EVI) from the moderate resolution imaging spectrometer (MODIS) on the Terra satellite to measure and scale evapotranspiration (ET) over sparsely vegetated grassland and shrubland sites in a semiarid watershed in southeastern Arizona from 2000 to 2004. The grassland tower site had higher mean annual ET (336 mm yr-1) than the shrubland tower site (266 mm yr-1) (P<0.001). ET measured at the individual tower sites was strongly correlated with EVI (r=0.80-0.94). ET was moderately correlated with precipitation (P), and only weakly correlated with net radiation or air temperature. The strong correlation between ET and EVI, as opposed to the moderate correlation with rainfall, suggests that transpiration (T) is the dominant process controlling ET at these sites. ET could be adequately predicted from EVI and P across seasons and tower sites (r2 = 0.74) by a single multiple regression equation. The regression equation relating ET to EVI and P was used to scale ET over 25 km2 areas of grassland and shrubland around each tower site. Over the study, ratios of T to ET ranged from 0.75 to 1.0. Winter rains stimulated spring ET, and a large rain event in fall, 2000, stimulated ET above T through the following year, indicating that winter rain stored in the soil profile can be an important component of the plants' water budget during the warm season in this ecosystem. We conclude that remotely sensed vegetation indices can be used to scale ground measurements of ET over larger landscape units in semiarid ranglelands, and that the vegetation communities in this landscape effectively harvest the available precipitation over a period of years, even though precipitation patterns are variably seasonally and interannually. ?? 2007 Elsevier Ltd. All rights reserved.

Journal of Arid Environments↗

USGS1993

Compositing multitemporal remote sensing data sets

To eliminate cloud and atmosphere-affected pixels, the compositing of multi temporal remote sensing data sets is done by selecting the maximum vale of the normalized different vegetation index (NDVI) within a compositing period. The NDVI classifier, however, is strongly affected by surface type and anisotropic properties, sensor viewing geometries, and atmospheric conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external effects. To improve the accuracy of compositing products, two key approaches can be taken: one is to refine the compositing classifier (NDVI) and the other is to improve existing compositing algorithms. In this project, an alternative classifier was developed and an alternative pixel selection criterion was proposed for compositing. The new classifier and the alternative compositing algorithm were applied to an advanced very high resolution radiometer data set of different biome types in the United States. The results were compared with the maximum value compositing and the best index slope extraction algorithms. The new approaches greatly reduced the high frequency noises related to the external factors and repainted more reliable data. The results suggest that the geometric-optical canopy properties of specific biomes may be needed in compositing. Limitations of the new approaches include the dependency of pixel selection on the length of the composite period and data discontinuity.

Pecora 12 Symposium↗