Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation

Jang, Keunchang; Kang, Sinkyu; Kim, Jea-Chul; Lee, Chong Bum; Kim, Tae-Hee; Kim, Joan; Hirata, Ryuichi; Saigusa, Nobuko

doi:10.1016/j.rse.2009.11.010

Cited by 51 publications

(63 citation statements)

References 51 publications

(82 reference statements)

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“…Overall, LE estimation by the MS-PT algorithm shows the obvious sensitivity orders: In the MS-PT algorithm proposed in this study, R n acquires more highly dependent and this is generally consistent with the previous literature [58,59]. For example, Jang et al [58] considered the sensitivity orders of estimated LE using satellite-based models with R n (±12% change of LE for ±20% change of the variable) > LAI (approximate ±10%) > VPD and T a (less than ±5%) at different land cover types. Hwang and Choi [59] found the sensitivity orders: R n > LAI > P a for the revised remote sensing-based PM model.…”

Section: Sensitivity Analysissupporting

confidence: 89%

Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration

et al. 2014

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Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. with the Priestley-Taylor-based LE (PT-JPL) algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R 2 between measured and predicted annual LE anomalies is 0.42 (p = 0.02). The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information.

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Section: Sensitivity Analysissupporting

confidence: 89%

Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration

et al. 2014

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“…Gaps in the Landsat ETM+ SLC-off images were removed using a simple gap-filling extension toolbox (landsat_gapfill.sav) in the ENVI software [41]. This gap-filling method is based on the local linear histogram matching technique chose by USGS [42].…”

Section: Optical Satellite Imagesmentioning

confidence: 99%

Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data

Zhu

Jia

2014

Remote Sensing

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Abstract:The knowledge of water storage variations in ungauged lakes is of fundamental importance to understanding the water balance on the Tibetan Plateau. In this paper, a simple framework was presented to monitor the fluctuation of inland water bodies by the combination of satellite altimetry measurements and optical satellite imagery without any in situ measurements. The fluctuation of water level, surface area, and water storage variations in Lake Qinghai were estimated to demonstrate this framework. Water levels retrieved from ICESat (Ice, Cloud, and and Elevation Satellite) elevation data and lake surface area derived from MODIS (Moderate Resolution Imaging Spectroradiometer) product were fitted by linear regression during the period from 2003 to 2009 when the overpass time for both of them was coincident. Based on this relationship, the time series of water levels from 1999 to 2002 were extended by using the water surface area extracted from Landsat TM/ETM+ images as inputs, and finally the variations of water volume in Lake Qinghai were estimated from 1999 to 2009. The overall errors of water levels retrieved by the simple method in our work were comparable with other globally available test results with r = 0.93, MAE = 0.07 m, and RMSE = 0.09 m. The annual average rate of increase was 0.11 m/yr, which was very close to the results obtained from in situ measurements. High accuracy was obtained in the estimation of surface areas. The MAE and RMSE were only 6 km 2 , and 8 km 2 , respectively, which were even lower than the MAE and RMAE of surface area extracted from Landsat TM images. The estimated water volume variations effectively captured the trend of annual OPEN ACCESSRemote Sens. 2014, 6 10458 variation of Lake Qinghai. Good agreement was achieved between the estimated and measured water volume variations with MAE = 0.4 billion m 3 , and RMSE = 0.5 billion m 3 , which only account for 0.7% of the total water volume of Lake Qinghai. This study demonstrates that it is feasible to monitor comprehensively the fluctuation of large water bodies based entirely on remote sensing data.

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“…The MODIS07_L2 atmosphere profiles product covers the entire globe at 1-2 day intervals, and produces various atmosphere variables, including T a , at 5 km spatial resolution. The temperature variables derived from MODIS07_L2 product have been widely used to estimate surface radiation budget parameters, evapotranspiration, and plant productivity in previous applications [31][32][33][34][35][36][37][38]. However, the MODIS IR and temperature retrievals are significantly degraded by atmosphere aerosol, smoke and cloud cover contamination, which hampers terrestrial monitoring applications.…”

Section: Introductionmentioning

confidence: 99%

Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data

et al. 2014

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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

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Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation

Cited by 51 publications

References 51 publications

Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration

Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration

Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data

Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data

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