Estimating purple-soil moisture content using Vis-NIR spectroscopy
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e-mail: [email protected]
http://jms.imde.ac.cn https://doi.org/10.1007/s11629-019-5848-2
Estimating purple-soil moisture content using Vis-NIR spectroscopy GOU Yu1
https://orcid.org/0000-0002-2195-2313; e-mail: [email protected]
WEI Jie1,2*
https://orcid.org/0000-0002-8726-1371;
LI Jin-lin2
https://orcid.org/0000-0001-7387-2123; e-mail: [email protected]
HAN Chen1
e-mail: [email protected]
https://orcid.org/0000-0001-8800-5831; e-mail: [email protected]
TU Qing-yan1
https://orcid.org/0000-0003-3204-3577; e-mail: [email protected]
LIU Chun-hong1,2
https://orcid.org/0000-0002-3599-4565; e-mail: [email protected]
*Corresponding author 1 School of Geography and Tourism Science, Chongqing Normal University, Chongqing 401331, China 2 Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, Chongqing 401331, China Citation: Gou Y, Wei J, Li JL, et al. (2020) Estimating purple-soil moisture content using Vis-NIR spectroscopy. Journal of Mountain Science 17. https://doi.org/10.1007/s11629-019-5848-2
© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract: Soil moisture is essential for plant growth in terrestrial ecosystems. This study investigated the visible-near infrared (Vis-NIR) spectra of three subgroups of purple soils (calcareous, neutral, and acidic) from western Chongqing, China, containing different water contents. The relationship between soil moisture and spectral reflectivity (R) was analyzed using four spectral transformations, and estimation models were established for estimating the soil moisture content (SMC) of purple soil based on stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR). We found that soil spectra were similar for different moisture contents, with reflectivity decreasing with increasing moisture content and following the order neutral > calcareous > acidic purple soil (at constant moisture content). Three of the four spectral transformations can highlight spectral sensitivity to SMC and significantly improve the correlation between the reflectance spectra and SMC. SMLR and PLSR
methods provide similar prediction accuracy. The PLSR-based model using a first-order reflectivity differential (R′) is more effective for estimating the SMC, and gave coefficient of determination (Rv2), root mean square errors of validation (RMSEV), and ratio of performance to inter-quartile distance (RPIQ) values of 0.946, 1.347, and 6.328, respectively, for the calcareous soil, and 0.944, 1.818, and 6.569, respectively, for the acidic purple soil. For neutral purple soil, the best prediction was obtained using the SMLR method with R′ transformation, yielding Rv2, RMSEV and RPIQ values of 0.973, 0.888 and 8.791, respectively. In general, PLSR is more suitable than SMLR for estimating the SMC of purple soil.
Received: 13-Oct-2019 Revised: 05-Mar-2020 Accepted: 02-Jun-2020
Soil moisture is widely considered to be a pr
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