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A New Approach for Estimating Soil Salinity Using A Low-Cost Soil Sensor In Situ: A Case Study in Saline Regions of China’s East Coast

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Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
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Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
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Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A OC6, Canada
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Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
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Joint International Research Laboratory of Agriculture and Agricultural Product Safety, Yangzhou University, Yangzhou 225009, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 239; https://doi.org/10.3390/rs12020239
Received: 10 November 2019 / Revised: 25 December 2019 / Accepted: 8 January 2020 / Published: 10 January 2020
Accurate and timely information on soil salinity is crucial for vegetation growth and agricultural productivity in coastal regions. This study investigates the potential of using Wifi POGO, an in situ electromagnetic sensor, for soil salinity assessment over saline coastal regions in eastern China. The sensor readings, soil moisture, and temperature-corrected apparent electrical conductivity (ECa) were used to generate models for EC1:5 (a surrogate for soil salinity) estimation. Two salty areas with distinct soil textures, sandy loam (Shuntai) and clay (Dongxin), were selected. This study revealed that the difference between soil salinity and the in situ measured soil ECa (i.e., EC1:5-ECa) had a strong curvilinear relationship with soil moisture. Such a relationship allows for the direct estimation of soil salinity from soil ECa with the aid of soil moisture information. Both ECa and soil moisture can be measured in situ using a Wifi POGO, a low-cost ground-based soil sensor. By using the leave-one-out cross-validation (LOOCV), the achieved root mean square error (RMSE) and relative RMSE (RRMSE) in EC1:5 estimation were 0.0109 S/m and 19.24% respectively in Shuntai, and 0.0157 S/m and 16.05%, in Dongxin. This new method offers a simple, cost-effective and reliable tool for assessing soil salinity in dynamic coastal regions. View Full-Text
Keywords: soil salinity estimation; coastal saline soils; soil apparent electrical conductivity (ECa); electromagnetic soil sensor soil salinity estimation; coastal saline soils; soil apparent electrical conductivity (ECa); electromagnetic soil sensor
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MDPI and ACS Style

Wang, J.; Sun, Q.; Shang, J.; Zhang, J.; Wu, F.; Zhou, G.; Dai, Q. A New Approach for Estimating Soil Salinity Using A Low-Cost Soil Sensor In Situ: A Case Study in Saline Regions of China’s East Coast. Remote Sens. 2020, 12, 239.

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