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⁻¹) 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⁻¹ 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.

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 R² 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 (R² = 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.

Fractional green vegetation cover (FVC) is a useful indicator for monitoring grassland status. Satellite imagery with coarse spatial but high temporal resolutions has been preferred to monitor seasonal and inter-annual FVC dynamics in wide geographic area such as Mongolian steppe. However, the coarse spatial resolution can cause a certain uncertainty in the satellite-based FVC estimation, which calls attention to develop a robust statistical test for the relationship between field FVC and satellite-derived vegetation indices. In the arid and semi-arid Mongolian steppe, nadir pointing digital camera images (DCI) were collected and used to produce a FVC dataset to support the evaluation of satellite-based FVC retrievals. An optimal DCI processing method was determined with respect to three color spaces (RGB, HIS, L*a*b*) and six green pixel classification algorithms, from which a country-wide dataset of DCI-FVC was produced and used for evaluating the accuracy of satellite-based FVC estimates from MODIS vegetation indices. We applied three empirical and three semi-empirical MODIS-FVC retrieval models. DCI data were collected from 96 sites across the Mongolian steppe from 2012 to 2014. The histogram algorithm using the hue (H) value of the HIS color space was the optimal DCI method (r² = 0.94, percent root-mean-square-error (RMSE) = 7.1%). For MODIS-FVC retrievals, semi-empirical Baret model was the best-performing model with the highest r² (0.69) and the lowest RMSE (49.7%), while the lowest MB (+1.1%) was found for the regression model with normalized difference vegetation index (NDVI). The high RMSE (>50% or so) is an issue requiring further enhancement of satellite-based FVC retrievals accounting for key plant and soil parameters relevant to the Mongolian steppe and for scale mismatch between sampling and MODIS data.