A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing

Geoderma

Volumn 197-198, Issue , 2013, Pages 17-26

  Casa, R ^a   Castaldi, F ^a   Pascucci, S ^b   Palombo, A ^b   Pignatti, S ^b  

^a UNIVERSITY OF TUSCIA   (Italy)

^b CNR   (Italy)

Author keywords

Clay; Kriging; Regression analysis; Sand; Silt; Spectroscopy

Indexed keywords

AS-SOILS; BARE SOILS; CALIBRATION AND VALIDATIONS; CENTRAL ITALY; CHRIS-PROBA; DATA SETS; ESTIMATION OF PROPERTIES; GEOLOCATIONS; HYPERSPECTRAL REMOTE SENSING; HYPERSPECTRAL SATELLITE; IMAGING SPECTROSCOPY; KRIGING; PARTIAL LEAST-SQUARE REGRESSION; PREDICTION MODEL; QUANTITATIVE ESTIMATION; REGISTRATION ERROR; REMOTE SENSING DATA; ROOT-MEAN-SQUARE ERROR OF PREDICTIONS; SAND FRACTION; SENSOR RESOLUTION; SINGLE PIXEL; SOIL PARTICLE SIZE; SOIL SAMPLE; SOIL SAMPLING; SOIL TEXTURES; SPACE-BORNE; SPATIAL RESOLUTION; SPECTRAL BEHAVIORS; SPECTRAL RANGE; SPECTRAL SIGNATURE; SPECTRO-RADIOMETERS;

CLAY; ESTIMATION; FORECASTING; IMAGE RESOLUTION; MEAN SQUARE ERROR; PIXELS; REGRESSION ANALYSIS; REMOTE SENSING; SAND; SILT; SOIL MOISTURE; SPACE OPTICS; SPECTROSCOPY; TEXTURES;

CALIBRATION;

ACCURACY ASSESSMENT; BARE SOIL; CALIBRATION; CHRIS; CLAY; COMPARATIVE STUDY; DATA SET; ESTIMATION METHOD; KRIGING; MIVIS; PIXEL; PROBA; REGRESSION ANALYSIS; REMOTE SENSING; RESOLUTION; SATELLITE IMAGERY; SILT; SOIL MOISTURE; SOIL TEXTURE;

ITALY;

EID: 84872822059     PISSN: 00167061     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geoderma.2012.12.016     Document Type: Article

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