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Cheng T.,University of California at Davis | Riano D.,University of California at Davis | Riano D.,Institute Economia y Geografia | Koltunov A.,University of California at Davis | And 3 more authors.
Remote Sensing of Environment | Year: 2013

Retrievals of vegetation canopy water content (CWC) from remotely sensed imagery can improve our understanding of the water cycle and help manage irrigation of agricultural crops. Optical remote sensing data can be used to detect seasonal CWC variation but whether they are sensitive enough for detecting diurnal CWC variation remains unknown. This paper investigates whether MODIS/ASTER airborne simulator (MASTER) data can be used to detect diurnal variation in CWC over well irrigated almond and pistachio orchards in the southern San Joaquin Valley of California, USA. MASTER images were first corrected for the Bi-directional Reflectance Distribution Function (BRDF) effect to remove cross-track variation in reflectance amplitude. Two spectral indexes, the Normalized Difference Infrared Index (NDII) and the Normalized Difference Vegetation Index (NDVI), were derived from corrected morning and afternoon MASTER imagery and related to the field-measured CWC. At the ground level, a significant decrease (~9%) in CWC occurred from morning to afternoon (p<0.0001). The field-measured CWC was positively correlated with MASTER-derived NDII and NDVI for both morning (NDII: r2=0.67, NDVI: r2=0.56, p<0.0001) and afternoon (NDII: r2=0.42, NDVI: r2=0.39, p<0.001) data. The diurnal change in CWC also led to a statistically significant spectral change that was observed as a 4% decline in NDII (p<0.005) or 2% decline in NDVI (p<0.0005). Our results show that the diurnal variation in CWC can be detected for the irrigated orchards using simple spectral indexes derived from MASTER data, with higher sensitivity for NDII than for NDVI as expected. The results also demonstrate the potential for remote sensing to improve crop management and better understand plant physiological changes at field to regional scales. © 2013 Elsevier Inc. Source


Moreno Ruiz J.A.,University of California at Davis | Moreno Ruiz J.A.,University of Almeria | Riano D.,University of California at Davis | Riano D.,Institute Economia y Geografia | And 4 more authors.
Remote Sensing of Environment | Year: 2012

A new algorithm for mapping burned areas in boreal forest using AVHRR archival data Long Term Data Record (LTDR) (0.05°, ca. 5km, version 3) was developed in Canada using burn records for the period between 1984 and 1999 and evaluated against AVHRR 1km and AVHRR-PAL 8km burned area map products. The algorithm combined 1) absolute and relative radiometric thresholds, 2) a Bayesian network classifier, and 3) neighborhood analysis for spatial fire coherence. Fire event records from Canadian Forest Service National Fire Database (CFSNFD) for western Canada were used to train the algorithm. LTDR and AVHRR 1km burned area mapping were similar for the same area, and correlated well to CFSNFD annual fire event records for western Canada, r 2=0.72 and 0.77, respectively. In addition, the LTDR mapping correlated well with fires for all of Canada in the CFSNFD database (r 2=0.65). This mapping product was a significant improvement over an 8km AVHRR-PAL burned area map product. For mapping boreal forests burned areas globally, this study demonstrates the potential accuracy for where LTDR represents the highest spatial and temporal resolution of daily images available since the 1980s. © 2011 Elsevier Inc. Source


Kasischke E.S.,University of Maryland University College | Loboda T.,University of Maryland University College | Giglio L.,University of Maryland University College | French N.H.F.,Michigan Technological University | And 4 more authors.
Journal of Geophysical Research: Biogeosciences | Year: 2011

A synthesis was carried out to analyze information available to quantify fire activity and burned area across North America, including a comparison of different data sources and an assessment of how variations in burned area estimate impact carbon emissions from fires. Data sets maintained by fire management agencies provide the longest record of burned area information. Canada and Alaska have the most well developed data sets consisting of the perimeters of large fires (>200 ha) going back to 1959 and 1950, respectively. A similar data set back to 1980 exists for the Conterminous U.S., but contains data only from federal land management agencies. During the early half of the 20th century, average burned area across North America ranged between 10 and 20 × 106 ha yr-1, largely because of frequent surface fires in the southeastern U.S. Over the past two decades, an average of 5 × 106 ha yr-1 has burned. Moderate-resolution (500-1000 m) satellite burned area products information products appear to either underestimate burned area (GFED3 and MCD45A1) or significantly overestimate burned area (L3JRC and GLOBCARBON). Of all the satellite data products, the GFED3 data set provides the most consistent source of burned area when compared to fire management data. Because they do not suitably reflect actual fire activity, the L3JRC and GLOBCARBON burned area data sets are not suitable for use in carbon cycle studies in North America. The MCD45A1 data set appears to map a higher fraction of burned area in low biomass areas compared to the GFED3 data set. © 2011 by the American Geophysical Union. Source


Qi Y.,University of Utah | Dennison P.E.,University of Utah | Spencer J.,University of Utah | Riano D.,University of California at Davis | Riano D.,Institute Economia y Geografia
Fire Ecology | Year: 2012

Live fuel moisture (LFM) is an important fuel property controlling fuel ignition and fire propagation. LFM varies seasonally, and is controlled by precipitation, soil moisture, evapotranspiration, and plant physiology. LFM is typically sampled manually in the field, which leads to sparse measurements in space and time. Use of LFM proxies could reduce the need for field sampling while potentially improving spatial and temporal sampling density. This study compares soil moisture and remote sensing data to field-sampled LFM for Gambel oak (Quercus gambelii Nutt) and big sagebrush (Artemisia tridentata Nutt) in northern Utah. Bivariate linear regression models were constructed between LFM and four independent variables. Soil moisture was more strongly correlated with LFM than remote sensing measurements, and produced the lowest mean absolute error (MAE) in predicted LFM values at most of the sites. When sites were pooled, canopy water content (CWC) had stronger correlations with LFM than normalized difference vegetation index (NDVI) or normalized difference water index (NDWI). MAE values for all proxies were frequently above 20 % LFM at individual sites. Despite this relatively large error, remote sensing and soil moisture data may still be useful for improving understanding of spatial and temporal trends in LFM. Source


Rodriguez J.M.,University of California at Davis | Ustin S.L.,University of California at Davis | Riano D.,University of California at Davis | Riano D.,Institute Economia y Geografia
Hydrological Processes | Year: 2011

Improved estimates of evapotranspiration (ET) are needed for water resource management and irrigation scheduling. We review the use of imaging spectroscopy to capture estimates of water vapour flux and biophysical components of ET. Remote sensing has long attempted to quantify and predict ET, with most applications relying only on green vegetation indexes from multispectral imagers combined with thermal radiance and weather data. In contrast, imaging spectrometry is an advanced remote sensing technology that measures hundreds of spectral bands in the solar spectrum. Plant pigments, water, and dry matter have unique spectral signatures that can advance estimates of ET and detection of drought stress. This allows analyses based on the physics of spectroscopy and avoids a requirement for continual empirical calibration. These spectral components provide unprecedented information about plant physiological processes, which improve understanding of the regulation of water fluxes and the energy budget. Laboratory, field, and airborne studies of spectral properties in the near- and shortwave-infrared region show strong relationships with plant water relations like water content, relative water content, and water potential. Because water absorption features are spectrally independent of pigment absorptions in the visible region, they provide a new source of information about environmental conditions. These new observations from imaging spectroscopy will lead to better understanding of ecological and hydrological processes. Copyright © 2011 John Wiley & Sons, Ltd. Source

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