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Satellite optical-infrared remote sensing from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides effective air temperature (T a ) retrieval at a spatial resolution of 5 km. However, frequent cloud cover can result in substantial signal loss and remote sensing retrieval error in MODIS T a . We presented a simple pixel-wise empirical regression method combining synergistic information from MODIS T a and 37 GHz frequency brightness temperature (T b ) retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for estimating surface level T a under both clear and cloudy sky conditions in the United States for 2006. The instantaneous T a retrievals showed favorable agreement with in situ air temperature records from 40 AmeriFlux tower sites; mean R 2 correspondence was 86.5 and 82.7 percent, while root mean square errors (RMSE) for the T a retrievals were 4.58 K and 4.99 K for clear and cloudy sky conditions, respectively. Daily mean T a was estimated using the instantaneous T a retrievals from day/night overpasses, and showed favorable agreement with local tower measurements (R 2 = 0.88; RMSE = 3.48 K). The results of this study indicate that the combination of MODIS and AMSR-E sensor data can produce T a retrievals with reasonable accuracy and relatively fine spatial resolution (~5 km) for clear and cloudy sky conditions. OPEN ACCESSRemote Sens. 2014, 6 8388

[1] We applied an approach for daily estimation and monitoring of evapotranspiration (ET) over the Northeast Asia monsoon region using satellite remote sensing observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Frequent cloud cover results in a substantial loss of remote sensing information, limiting the capability of continuous ET monitoring for the monsoon region. Accordingly, we applied and evaluated a stand-alone MODIS ET algorithm for representative regional ecosystem types and an alternative algorithm to facilitate continuous regional ET estimates using surface meteorological inputs from the Korea Land Data Assimilation System (KLDAS) in addition to MODIS land products. The resulting ET calculations showed generally favorable agreement (root-mean-square error < 1.3 mm d À1 ) with respect to in situ measurements from eight regional flux tower sites. The estimated mean annual ET for 3 years (2006 to 2008) was approximately 362.0 ± 161.5 mm yr À1 over the Northeast Asia domain. In general, the MODIS and KLDAS-based ET (MODIS-KLDAS ET) results showed favorable performance when compared to tower observations, though the results were overestimated for a forest site by approximately 39.5% and underestimated for a cropland site in South Korea by 0.8%. The MODIS-KLDAS ET data were generally underestimated relative to the MODIS (MOD16) operational global terrestrial ET product for various biome types, excluding cropland; however, MODIS-KLDAS ET showed better agreement than MOD16 ET for forest and cropland sites in South Korea. Our results indicate that MODIS ET estimates are feasible but are limited by satellite optical-infrared remote sensing constraints over cloudy regions, whereas alternative ET estimates using continuous meteorological inputs from operational regional climate systems (e.g., KLDAS) provide accurate ET results and continuous monitoring capability under all-sky conditions.

Lake area is an important indicator for climate change and its relationship with climatic factors is critical for understanding the mechanisms that control lake level changes. In this study, lake area changes and their relations to precipitation were investigated using multi-temporal Landsat Thermatic Mapper (TM) and Enhanced Thermatic Mapper plus (ETM+) images collected from 10 different regions of Mongolia since the late 1980s. A linear-regression analysis was applied to examine the relationship between precipitation and lake area change for each region and across different regions of Mongolia. The relationships were interpreted in terms of regional climate regime and hydromorphological characteristics. A total of 165 lakes with areas greater than 10 hm 2 were identified from the Landsat images, which were aggregated for each region to estimate the regional lake area. Temporal lake area variability was larger in the Gobi regions, where small lakes are densely distributed. The regression analyses indicated that the regional patterns of precipitation-driven lake area changes varied considerably (R 2 =0.028-0.950), depending on regional climate regime and hydromorphological characteristics. Generally, the lake area change in the hot-and-dry Gobi regions showed higher correlations with precipitation change. The precedent two-month precipitation was the best determining factor of lake area change across Mongolia. Our results indicate the usefulness of regression analysis based on satellite-derived multi-temporal lake area data to identify regions where factors other than precipitation might play important roles in determining lake area change.

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