A General Paradigm for Retrieving Soil Moisture and Surface Temperature from Passive Microwave Remote Sensing Data Based on Artificial Intelligence

A General Paradigm for Retrieving Soil Moisture and Surface Temperature from Passive Microwave Remote Sensing Data Based on Artificial Intelligence

Soil moisture (SM) and land surface temperature (LST) are entangled, and the retrieval of one of them requires a priori specification of the other one. Due to insufficient observational information, retrieval of LST and SM from passive microwave remote sensing data is often ill-posed, and the retrie...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy:	Kebiao Mao, Han Wang, Jiancheng Shi, Essam Heggy, Shengli Wu, Sayed M. Bateni, Guoming Du
Format:	Artykuł
Język:	English
Wydane:	MDPI AG 2023-03-01
Seria:	Remote Sensing
Hasła przedmiotowe:	deep learning geophysical logical reasoning interleaved iterative optimization soil moisture land surface temperature collaborative retrieval
Dostęp online:	https://www.mdpi.com/2072-4292/15/7/1793

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