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Article

Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

1
Department of Remote Sensing and Geographic Information System, Hakim Sabzevari University, Sabzevar 9617976487, Iran
2
Department of Civil and Environmental Engineering, University of Perugia, 06125 Perugia, Italy
3
Department of Climatology and Geomorphology, Hakim Sabzevari University, Sabzevar 9617976487, Iran
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3223; https://doi.org/10.3390/w12113223
Received: 28 September 2020 / Revised: 2 November 2020 / Accepted: 12 November 2020 / Published: 17 November 2020
(This article belongs to the Section Water, Agriculture and Aquaculture)
Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications. View Full-Text
Keywords: soil moisture; remote sensing; machine learning; semi-arid region of Iran soil moisture; remote sensing; machine learning; semi-arid region of Iran
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MDPI and ACS Style

Adab, H.; Morbidelli, R.; Saltalippi, C.; Moradian, M.; Ghalhari, G.A.F. Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data. Water 2020, 12, 3223. https://doi.org/10.3390/w12113223

AMA Style

Adab H, Morbidelli R, Saltalippi C, Moradian M, Ghalhari GAF. Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data. Water. 2020; 12(11):3223. https://doi.org/10.3390/w12113223

Chicago/Turabian Style

Adab, Hamed; Morbidelli, Renato; Saltalippi, Carla; Moradian, Mahmoud; Ghalhari, Gholam A.F. 2020. "Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data" Water 12, no. 11: 3223. https://doi.org/10.3390/w12113223

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