A data fusion approach for mapping daily evapotranspiration at field scale. Issue 8 (5th August 2013)

• Record Type: Journal Article
• Authors: Cammalleri, C.; Anderson, M. C.; Gao, F.; Hain, C. R.; Kustas, W. P.
• Is Part Of: Water resources research. Volume 49:Issue 8(2013:Aug.)
• Page Start: 4672
• Page End: 4686
• Publication Date: 2013-08-05
• Subjects: thermal remote sensing -- Landsat -- MODIS -- surface energy balance; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/wrcr.20349
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract : [1] Thermal remote sensing methods for mapping evapotranspiration (ET) exploit the physical interconnection that exists between land‐surface temperature (LST) and evaporative cooling, employing principles of surface energy balance (SEB). Unfortunately, while many applications in water resource management require ET information at daily and field spatial scales, current satellite‐based thermal sensors are characterized by either low spatial resolution and high repeatability or by moderate/high spatial resolution and low frequency. Here we introduce a novel approach to ET mapping that fuses characteristics of both classes of sensors to provide optimal spatiotemporal coverage. In this approach, coarse resolution daily ET maps generated with a SEB model using geostationary satellite data are spatially disaggregated using daily MODIS (MODerate resolution Imaging Spectroradiometer) 1 km and biweekly Landsat LST imagery sharpened to 30 m. These ET fields are then fused to obtain daily ET maps at 30 m spatial resolution. The accuracy of the fused Landsat‐MODIS daily ET maps was evaluated over Iowa using observations collected at eight flux towers sited in corn and soybean fields during the Soil Moisture Experiment of 2002, as well as in comparison with a Landsat‐only retrieval. A significant improvement in ET accuracy (reducing errors from 0.75 to 0.58 mm d −1 on average) was obtained by fusing MODIS and Landsat data in comparison with the Landsat‐only case, with most notable improvements when a rainfall event occurred between two successive Landsat acquisitions. The improvements are further evident at the seasonal timescale, where a 3% error is obtained using Landsat‐MODIS fusion versus a 9% Landsat‐only systematic underestimation. Key Points: A data fusion procedure was used to combine MODIS‐ and Landsat‐based ET maps This procedure allows mapping of daily ET at sub‐field spatial scales Improvements in ET were obtained when rain occurred between Landsat overpasses