| Technology Application |
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The Evaluation of MODIS Data and Geographic Data for Estimating Near Surface Air Temperature |
| QU Pei-qing1,2, SHI Run-he1,2, LIU Chao-shun1,2, ZHONG Hong-lin1,2 |
1. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200062, China;
2. Joint Laboratory for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE, Shanghai 200062, China |
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Abstract The main objective of this study is to discuss the feasibility of predicting near-surface air temperature using MODIS products. Principal component analysis of land surface temperature (LST), ALBEDO, vegetation index (NDVI), altitude (ALT) and latitude (LAT) was employed, with some principal components of the cumulative variance in the front of these principal components as independent variables. Multiple linear relationships between independent variables and meteorological observation temperatures were established. The results show that the range of RMSE is between 0.5 and 2.3. Most of RMSE vary greatly in winter months but are relatively stable in summer months. Compared with Aqua/MODIS, Terra/MODIS can get better results. RMSE of Tmin modeled by Terra nighttime MODIS is lower, and so are Tmax and T14 modeled by Terra daytime MODIS. LST is the most powerful predictor, followed by ALT, LAT, NDVI and ALBEDO. The MODIS products can therefore monitor the spatial distribution of near-surface air temperature at different times, with the optimal products selections being different.
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Issue Date: 16 December 2011
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2011,
Vol. 23
