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Predicting Near‐Surface Moisture Content of Saline Soils from Near‐Infrared Reflectance Spectra with a Modified Gaussian Model
Author(s) -
Zeng Wenzhi,
Xu Chi,
Huang Jiesheng,
Wu Jingwei,
Tuller Markus
Publication year - 2016
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2016.06.0188
Subject(s) - water content , environmental science , remote sensing , soil science , moisture , hyperspectral imaging , soil water , soil salinity , gaussian function , reflectivity , salinity , gaussian , geology , meteorology , chemistry , optics , geography , physics , geotechnical engineering , computational chemistry , oceanography
Core Ideas Moisture estimates from near‐infrared reflectance are improved with a modified Gaussian model. Artificial neural network simulations further improve moisture prediction accuracy. The proposed method yields better results than standard reflectance moisture indices. Near‐surface soil moisture is a spatially highly heterogeneous state variable that affects the hydrologic partitioning of precipitation, net ecosystem exchange, and land–atmosphere interactions. In concert with the rapid advancement of large‐scale hyperspectral remote sensing technology over the last decade, numerous methods relating reflectance to near‐surface soil moisture have been proposed. Although there is evidence that surface reflectance is conjointly controlled by moisture and salt content, little is known about the impact of soil salinity. To study the effects of soil salinity, near infrared to short‐wave infrared reflectance spectra were measured in a controlled laboratory environment for samples representing a wide range of salinity levels. A previously proposed surface moisture prediction method based on geometrical attributes of an inverted Gaussian (IG) function fitted to hyperspectral reflectance curves was revisited. Improvements were proposed by first modifying the geometrical attributes of the IG applied to predict near‐surface soil moisture and by concurrently considering multiple geometrical attributes of the IG function as input for trained artificial neural networks (ANNs). Although previously applied linear regression models that relate a single geometrical parameter to soil moisture failed to satisfactorily predict independently measured surface soil moisture, considerable improvements in prediction accuracy were achieved with both geometrical attribute modification and ANN simulations.