Linear Retrieval of Microwave Land Surface Emissivity over the Desert Area in January

Yuanhong, 官元红 Guan; Wenjun, 王文君 Wang; Qifeng, 陆其峰 Lu; Yansong, 鲍艳松 Bao; Tingwen, 郑婷文 Zheng

doi:10.3788/lop57.212801

Cited by 1 publication

(2 citation statements)

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“…By applying the principles of the Friedman test, the test statistic τ F is calculated, which is 18.06. It follows an F-distribution with degrees of freedom (3,36), and the critical value is 2.565. Therefore, the null hypothesis is rejected, indicating that the performance of the retrieval models significantly differs.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Xie et al [2] found that an uncertainty of 5-10% in land surface emissivity produces a brightness temperature uncertainty of about 5 K. When studying microwave Remote Sens. 2024, 16, 89 2 of 19 land surface emissivity in desert areas, Guan et al [3][4][5] focused on its relationship with surface temperature, surface humidity, and soil moisture in the two subsurface layers, and built a land surface emissivity retrieval model based on the principle of radiative transfer and optimal control theory, which improved accuracy in simulating brightness temperature by the community radiative transfer model. All the above studies show that there are many factors affecting microwave land surface emissivity, and the selection of suitable influencing factors in suitable areas is an important condition for the accurate retrieval of microwave land surface emissivity.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Desert Microwave Land Surface Emissivity Based on Machine Learning Algorithms

Li,

Guan,

et al. 2023

Remote Sensing

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Based on the community radiative transfer model, ensemble tree-based random forest algorithm, and extreme gradient boosting tree algorithm, this study established a random forest retrieval model (RF) and an extreme gradient boosting tree retrieval model (XGBoost) for the microwave land surface emissivity by using ERA5 reanalysis data and the observed brightness temperature of 10.65 GHz vertical polarization from FY3C Microwave Radiation Imager-I. In addition, an optimized Bayesian regularized neural network retrieval model (M2_30NN) has also been established on the basis of the original neural network land surface emissivity retrieval model (M1_20NN). The results show that compared with the simulated brightness temperature of the original land surface emissivity, the simulated brightness temperature of the land surface emissivity from each retrieval model is not only significantly improved in the correlation coefficient with the observed brightness temperature (5.92% (M1_20NN), 4.23% (M2_30NN), 14.21% (RF), 13.07% (XGBoost)), but also in the evaluation indexes of root mean square error, mean absolute error and explained variance regression score in the training datasets. Furthermore, in terms of testing datasets and spatiotemporal independence test datasets, the retrieval results of RF and XGBoost models can capture the spatial distribution patterns that are consistent with observations well, and also show great numerical improvement compared with the original model. In general, the XGBoost retrieval model is the best, followed by the RF retrieval model.

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Section: Model Evaluationmentioning

confidence: 99%