On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance.
ABSTRACT The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes
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ABSTRACT: Continuous 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 0.5 mm d−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 0.9 mm d−1. This similarity in performance is likely due to the relatively large size of the fields (≈50 ha), 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 1.0 mm d−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 (≈5 ha) 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.Agricultural and Forest Meteorology 01/2014; 186:1–11. · 3.42 Impact Factor
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ABSTRACT: This paper describes a procedure for mapping long-term average, growing season-accumulated growing degree days at an enhanced spatial resolution of 28.5 m. GDD-product enhancement is based on augmenting a previously developed 1 km resolution map of GDD described in Hassan et al. [J. Applied Remote Sens., 1, 013511, 12p (2007)] using data from a series of scene-and date-specific Landsat-7 ETM+ images (at 28.5 m resolution) from the 1999-2002 data collection period and a chronological series of standard MODIS 16-day composites of enhanced vegetation index (EVI; at 250 m resolution) spanning the 2003-2005 growing periods (April-October). Surface reflectances from the Landsat-7 ETM+ images are used to derive fine-scale estimates of EVI, which are then transformed into long-term averages by taking into account growing-season specific, temporal trends in the series of MODIS-EVI images. As values from the 8-day accumulated GDD and 16-day composites of EVI have been shown to be strongly correlated, a new data-fusion method based on the mean and instantaneous values of fine-grain long-term average EVI is used to augment the resolution of the initial GDD map. As a demonstration, we apply the procedure to satellite and climate station data for the Canadian Province of Nova Scotia.07/2013;
- Remote Sensing of Environment 02/2014; 141:90-104. · 5.10 Impact Factor