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Remote Sens. 2016, 8(9), 714; doi:10.3390/rs8090714

Agricultural Soil Alkalinity and Salinity Modeling in the Cropping Season in a Spectral Endmember Space of TM in Temperate Drylands, Minqin, China

Land Resources and Management Department, College of Natural Resources and Environmental Science, China Agricultural University, Beijing 100193, China
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Academic Editors: José A.M. Demattê, James Campbell, Clement Atzberger and Prasad S. Thenkabail
Received: 4 July 2016 / Revised: 23 August 2016 / Accepted: 25 August 2016 / Published: 31 August 2016
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Abstract

This paper presents the potential of the four-image spectral endmember (EM) space comprising sand (SL), green vegetation (GV), saline land (SA), and dark materials (DA), unmixed from Landsat TM/ETM+ to map dryland agricultural soil alkalinity and salinity (i.e., soil alkalinity (pH) and soil electrical conductivity (EC)) in the shallow root zone (0–20 cm) using partial least squares regression (PLSR) and an artificial neural network (ANN). The results reveal that SA, SL, and GV fractions at the subpixel level, and land surface temperature (LST) are necessary independent variables for soil EC modeling in Minqin Oasis, a temperate-arid system in China. The R2 (coefficient of determination) of the optimized parameters with the ANN model was 0.79, the root mean squared error (RMSE) was 0.13, and the ratio of prediction to deviation (RPD) was 1.95 when evaluated against all sampled data. In addition to the aforementioned four variables, the DA fraction and the recent historical SA fraction (SAH) in the spring dry season in 2008 were also helpful for soil pH modeling. The model performance is R2 = 0.76, RMSE = 0.24, and RPD = 1.96 for all sampled data. In summary, the stable EMs and LST space of TM imagery with an ANN approach can generate near-real-time regional soil alkalinity and salinity estimations in the cropping period. This is the case even in the critical agronomic range (EC of 0–20 dS·m−1 and pH of 7–9) at which researchers and policy-makers require near-real-time crop management information. View Full-Text
Keywords: dryland soil alkalinity and salinity; spectral end-member; land surface temperature; crop season; artificial neural network; TM/ETM+ dryland soil alkalinity and salinity; spectral end-member; land surface temperature; crop season; artificial neural network; TM/ETM+
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sun, D.; Jiang, W. Agricultural Soil Alkalinity and Salinity Modeling in the Cropping Season in a Spectral Endmember Space of TM in Temperate Drylands, Minqin, China. Remote Sens. 2016, 8, 714.

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