Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations

Remote Sensing of Environment

Volumn 180, Issue , 2016, Pages 453-464

  Al Yaari, A ^a   Wigneron, J P ^a   Kerr, Y ^b   de Jeu, R ^c,d   Rodriguez Fernandez, N ^b   van der Schalie, R ^c,d   Al Bitar, A ^b   Mialon, A ^b   Richaume, P ^b   Dolman, A ^c   Ducharne, A ^e  

^a BORDEAUX SCIENCES AGRO   (France)

^b CNES   (France)

^c VU UNIVERSITY AMSTERDAM   (Netherlands)

^d Transmissivity B V   (Netherlands)

^e SORBONNE UNIVERSITÉ   (France)

Author keywords

AMSR E; SMOS; Soil moisture; Statistical regression

Indexed keywords

CALIBRATION; MEAN SQUARE ERROR; MICROWAVES; REGRESSION ANALYSIS; REMOTE SENSING; SOIL TESTING; SPACE OPTICS;

ADVANCED MICROWAVE SCANNING RADIOMETER; BRIGHTNESS TEMPERATURES; PASSIVE MICROWAVE REMOTE SENSING; REGRESSION COEFFICIENT; ROOT MEAN SQUARE ERRORS; SMOS; SOIL MOISTURE AND OCEAN SALINITY (SMOS); STATISTICAL REGRESSION;

SOIL MOISTURE;

AMSR-E; BRIGHTNESS TEMPERATURE; CALIBRATION; CORRELATION; DATA SET; REGRESSION ANALYSIS; REMOTE SENSING; SALINITY; SMOS; SOIL MOISTURE; SOIL SURFACE; STATISTICAL ANALYSIS;

ASIA; AUSTRALIA; UNITED STATES;

EID: 84947602443     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.11.022     Document Type: Article

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