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

AbstractContinuous monitoring of daily evapotranspiration (ET) at field scale can be achieved by combining thermal infrared remote sensing data information from multiple satellite platforms, given that no single sensor currently exists today with the required spatiotemporal resolution. Here, an integrated approach to field-scale ET mapping is described, combining multi-scale surface energy balance evaluations and a data fusion methodology, namely the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), 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 for the Landsat overpass dates suggest that the intrinsic model accuracy is consistent across the different test sites (and on the order of 0.5mmd−1) when contemporaneous Landsat imagery at 30-m resolution is available. 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 0.9mmd−1. This similarity in performance is likely due to the relatively large size of the fields (≈50ha), suggesting that the soil 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 lower than that for the rainfed field (errors of 1.5 and 1.0mmd−1, respectively), likely due to the inability of the model to capture ET spikes right after irrigation events for fields substantially smaller than MODIS data resolution. At this site, the irrigated fields were small (≈5ha) compared to the MODIS pixel size, and sparsely distributed over the landscape, so sporadic contributions to ET from soil evaporation due to irrigation were not captured by the 1-km MODIS ET retrievals. However, due the semiarid environment at Bushland, these irrigation-induced spikes in soil evaporation are not long-lived and these underestimations generally affect the irrigation dates only and they do not seem to influence negatively the estimates at the seasonal scale. ET data fusion is expected to perform better 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