Estimating daytime surface air temperature using multi-source remote sensing and climate reanalysis data at glacierized basins: A case study at Langtang valley, Nepal

Zhou, Wang; Peng, Bin; Shi, Jiancheng; Dhital, Yam Prasad; Wang, Tianxin; Ji, Dabin; Zhao, Tianjie; Yao, Panpan; Cui, Yurong; Shi, Lijuan; Yao, Ruohe; Liu, Chunguang

doi:10.1109/igarss.2016.7730079

Cited by 2 publications

(1 citation statement)

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“…However, some parameters in this method (such as the ground heat flux to net radiation ratio and crop water stress index) highly depend on NDVI, which limits its application in the glacierized basins. Semi-empirical methods establish the relationship between Ta and LST using regression formula with some theoretical basis [20][21][22]. One of the widely used semi-empirical methods is the temperature-vegetation index (TVX) method, which assumes that the Ta equals to the canopy temperature when vegetation coverage Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

et al. 2017

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Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins.

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Section: Introductionmentioning

confidence: 99%