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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 #294391

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

Location: Hydrology and Remote Sensing Laboratory

Title: Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion

Author
item Cammalleri, C. - European Commission-Joint Research Centre (JRC)
item Anderson, Martha
item Gao, Feng
item Hain, C. - University Of Maryland
item Kustas, William - Bill

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/16/2013
Publication Date: 3/15/2014
Publication URL: http://handle.nal.usda.gov/10113/59964
Citation: Anderson, M.C., Gao, F.N., Hain, C., Kustas, W.P. 2013. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agricultural and Forest Meteorology. 186:1-11.

Interpretive Summary: Estimation of water used by crops (evapotranspiration) can be conducted over large agricultural areas using satellites observations. Unfortunately, with the suite of satellites currently available, we can estimate crop water use at scales of individual fields (100m pixels) only infrequently (bi-weekly or once per month). Coarser resolution satellites provide daily observations, but they do not isolate water use on a field-to-field basis – estimates are averaged over several fields within a pixel 1-km in dimension. In this work, a method to combine these two sources of data (known as data fusion) is tested using evapotranspiration observations collected over rainfed and irrigated cotton fields in Bushland, Texas, and corn and soybean fields near Mead, Nebraska. The method facilitates field scale estimates of crop water use at daily timesteps. The proposed method worked well at estimating seasonal cumulative water use in all fields investigated. At daily timesteps, the method worked better in Nebraska where irrigation is more spatially continuous, resulting in evapotranspiration that is more uniform at the 1-km pixel scale. Application of this technique with satellite observation will significantly improve the monitoring of crop water use at field scale and enhance water conservation of agricultural systems.

Technical Abstract: A continuous monitoring of daily evapotranspiration (ET) at field scale can be achieved by combining thermal infrared remote sensing data information from multiple satellite platforms. Here, an integrated approach to field scale ET mapping is described, combining multi-scale surface energy balance evaluations and data fusion methodologies to optimally exploit spatiotemporal characteristics of image datasets collected by the Landsat and Moderate resolution Imaging Spectroradiometer (MODIS) sensors, as well as geostationary platforms. Performance of this methodology is evaluated over adjacent irrigated and rainfed fields, since mixed conditions are the most challenging for data fusion procedures, and in two different climatic regions: a semi-arid site in Bushland, TX, and a temperate site in Mead, NE,. Daytime-total ET estimates obtained on the Landsat overpass dates suggest that the intrinsic model accuracy is consistent across the different test sites when provided contemporaneous Landsat imagery at 30-m resolution, and on the order of 0.5 mm d-1. Comparisons between tower observations and daily ET datastreams, reconstructed between overpasses by fusing Landsat and MODIS estimates, provide a means for assessing the strengths and limitations of the fused product. At the Mead site, the model performed similarly for both irrigated and rainfed fields, with an accuracy of about 0.9 mm d-1. This similarity in performance is likely due to the relatively large size of the fields (˜ 50 ha), such that the moisture dynamics of the irrigated fields are reasonably well captured at the 1-km MODIS thermal pixel scale. On the other hand, the accuracy of daily retrievals for irrigated fields at the Bushland site was somewhat lower than that for the rainfed field (1.5 and 1.0 mm d-1, respectively). In this case, the irrigated fields were small (˜ 5 ha) compared to the MODIS pixel scale, and sparsely distributed over the landscape, so sporadic contributes to ET from soil evaporation due to irrigation were not captured by the 1-km MODIS ET retrievals. Better performance is expected over agricultural areas where irrigation is more spatially continuous, resulting in moisture fluxes that are more uniform at the MODIS pixel scale. Overall, the model accurately reproduces the ET temporal dynamics for all the experimental sites, and is able to capture the main differences that were observed between irrigated and rainfed fields at both daily and seasonal time scales.