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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #303215

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Using daily field-scale evapotranspiration (ET) derived with multi-sensor data fusion for monitoring crop condition and yield in central Iowa, United States

Author
item Semmens, Kathryn
item Anderson, Martha
item Gao, Feng
item Kustas, William - Bill
item Mladenova, Iliana
item HAIN, C. - University Of Maryland
item JOHNSON, D. - National Agricultural Statistical Service (NASS, USDA)
item OTKIN, J. - University Of Wisconsin
item Prueger, John

Submitted to: International Symposium on Recent Advances in Quantitative Remote Sensing
Publication Type: Abstract Only
Publication Acceptance Date: 5/5/2014
Publication Date: 9/22/2014
Citation: Semmens, K.A., Anderson, M.C., Gao, F.N., Kustas, W.P., Mladenova, I., Hain, C., Johnson, D., Otkin, J., Prueger, J.H. 2014. Using daily field-scale evapotranspiration (ET) derived with multi-sensor data fusion for monitoring crop condition and yield in central Iowa, United States [abstract]. 4th International Symposium on Recent Advances in Quantitative Remote Sensing, September 22-26, 2014, Torrent (Valencia), Spain.

Interpretive Summary:

Technical Abstract: Drought has significant impacts over broad spatial and temporal scales, and information about the timing and extent of such conditions is of critical importance to many end users in the agricultural and water resource management communities. The ability to accurately monitor effects on crops and provide early warning of developing vegetation stress provides valuable information for mitigating negative impacts of drought. This research employs a multi-sensor thermal band data fusion methodology (STARFM: Spatial and Temporal Adaptive Reflective Fusion Model) combined with multi-scale evapotranspiration (ET) modeling (ALEXI: Atmosphere Land Exchange Inverse Model) to compute daily field-scale (30 meter resolution) ET from 2000 to 2011 for Ames, Iowa, a corn and soybean production region in midwest United States. The ET timeseries is evaluated at flux tower sites located in corn and soybean fields, and compared to fused Normalized Difference Vegetation Index timeseries (reflective bands) as well as county level crop condition, crop progress (phenological stage) and soil moisture weekly reports from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) collected by trained observers on the ground. Spatial and temporal correlations between these datasets are explored over a variety of temporal and spatial scales, as well as for different corn and soybean phenological stages. The dataset is used to test the hypothesis that daily field-scale ET can improve yield estimates by providing independent, spatially continuous estimates of crop condition. It is hypothesized that combining ET with crop phenological stage (derived from NDVI) will provide critical information about crop stress during key development stages. In addition, stress-induced changes in thermal-based ET retrievals are hypothesized to precede declines in green crop cover fraction (typically observed using optical vegetation indices), providing earlier warning of changing crop conditions. This research, by comparing both multi-sensor remote sensing and ground observations, provides a unique and valuable perspective of evapotranspiration and drought estimation with implications for crop health and yield estimation.