The remote sensing inversion and validation of land surface temperature based on ASTER data: A case study of the Heihe River basin
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摘要: 针对黑河流域的地表类型特点和大气特征,基于 ASTER发射率产品和植被覆盖度法(vegetation cover method,VCM)计算了研究区地表发射率,并利用改进的多层前馈神经网络(multilayer feedforword neural network,MFNN)算法估算了区域大气水汽含量,通过对输入参数分组构建系数查找表,发展了适用于ASTER数据遥感反演地表温度的分裂窗算法。为检验算法的适应性和精度,利用黑河流域2019年的地表温度实测数据和MODIS温度产品对算法进行评价。结果表明,与站点数据相比,均方根误差在1.81~3.01 K之间; 在与MODIS数据产品交叉验证中,本文提出的算法误差和偏差相对较小,均方根误差在1.11~1.75 K之间。总体来说,利用本算法反演得到的温度产品精度可满足气象气候学研究的需要,算法的构建思路也可为类似的热红外传感器提供借鉴。Abstract: Given the land surface types and atmospheric features of the Heihe River basin, this study calculated the surface emissivity of the study area using the ASTER Global Emissivity Database and the vegetation cover method (VCM) and estimated the atmospheric water vapor content using the improved multilayer feed-forward neural network (MFNN). Moreover, by establishing the coefficient lookup table of input parameter groups, this study developed an ASTER data-based split-window algorithm for the remote sensing inversion of land surface temperature. To validate the applicability and accuracy of the split-window algorithm, this study elevated the algorithm using the measured site data on the land surface temperature of the Heihe River basin in 2019 and MODIS instruments. Compared with the site data, the results of the split-window algorithm had root mean square errors of 1.81~3.01 K. In the cross-validation using the MODIS instruments, the split-window algorithm had relatively small errors and deviations, with root mean square errors of 1.11~1.75 K. Overall, the accuracy of the land surface temperature obtained from the inversion using the split-window algorithm can meet the needs of meteorological and climatological studies. Moreover, the development philosophy of the split-window algorithm can be used as a reference for similar thermal infrared sensors.
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Key words:
- ASTER data /
- land surface temperature /
- cross-validation
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