Remotely Sensed Clear-Sky Surface Longwave Downward Radiation by Using Multivariate Adaptive Regression Splines Method
| Autor: | Rui Zhao, Tianxing Wang, Yuechi Yu, Wang Zhou, Bin Peng, Jiancheng Shi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Earth's energy budget
Multivariate adaptive regression splines 010504 meteorology & atmospheric sciences Mean squared error media_common.quotation_subject 0211 other engineering and technologies Longwave 02 engineering and technology Mars Exploration Program Atmospheric model 01 natural sciences Atmosphere Sky Environmental science 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing media_common |
| Zdroj: | IGARSS |
| DOI: | 10.1109/igarss.2018.8519297 |
| Popis: | Surface radiation balance plays a vital role in the earth surface system and affects many biogeophysical processes. As one of components of surface energy balance, longwave downward radiation (LWDR) is considered as the most poorly estimated radiation component, and its uncertainty is regarded as substantially larger than other terms of surface energy budget. In this paper, we applied the multivariate adaptive regression splines (MARS) method to derive LWDR based on MODIS thermal infrared bands top of atmosphere radiances and ground-based LWDR measurements. In model fitting process, the RMSE, bias and R-square value are 25.49 W/m2, −0.000 W/m2 and 0.88, respectively; and in model validation stage, the RMSE, bias and R-square value are 25.63 W/m2, 0.481 W/m2 and 0.87, respectively. The newly proposed model demonstrates comparable accuracy with other LWDR estimating methods and proves that MARS method is very useful in remote sensing based LWDR estimation. |
| Databáze: | OpenAIRE |
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