Soil Science Society of America Journal · 2002 · 663 citations · 17 references
Environmental MonitoringEngineeringLand DegradationTerrestrial SensingEarth ScienceSoil PropertyVegetation-atmosphere InteractionsSpectral ReflectanceForest MeteorologySoil ReflectanceReflectance ModelingDry ReflectanceRadiation MeasurementRadiometryAtmospheric RadiationRemote SensingOptical Remote SensingWater Surface ReflectanceLand Surface Reflectance
Models of soil reflectance under changing moisture conditions are needed to better quantify soil and vegetation properties from remote sensing. The study explores a general reflectance model based solely on dry reflectance and moisture using Monte Carlo analysis to account for spectral variability. The authors measured shortwave reflectance (400–2500 nm) of four soils at varying moisture levels in the lab and used Monte Carlo analysis to model the relationship. The study found that soil reflectance varies nonlinearly with moisture, following an exponential model, with saturation occurring at lower moisture in VNIR than SWIR; SWIR provides a more reliable moisture indicator with lower uncertainties, aiding operational algorithms and future physically based models.
Models of soil reflectance under changing moisture conditions are needed to better quantify soil and vegetation properties from remote sensing. In this study, measurements of reflected shortwave radiation (400–2500 nm) were acquired in a laboratory setting for four different soils at various moisture contents. The observed changes in soil reflectance revealed a nonlinear dependence on moisture that was well described by an exponential model, and was similar for different soil types when moisture was expressed as degree of saturation. Reflectance saturated at much lower moisture contents in the visible and near‐infrared (VNIR) spectral region than in the shortwave‐infrared (SWIR) spectral region, suggesting that longer wavelengths are better suited for measuring volumetric moisture contents above ∼20%. To explore the potential of a general reflectance model based solely on dry reflectance and moisture, we employed a Monte Carlo analysis that accounted for observed variability in the measured spectra. Modeling results indicated that the SWIR region offers significant potential for relating moisture and reflectance on an operational basis, with uncertainties less than half as large as in the VNIR. The results of this study help to quantify the strong influence of moisture on spectral reflectance and absorption features, and should aid in the development of operational algorithms as well as more physically based models in the future.
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