Temperature and Vegetation Indices Based Surface Soil Moisture Estimation: A Remote Sensing Data Approach

2018 ◽  
pp. 281-289 ◽  
Author(s):  
V. Vani ◽  
K. Pavan Kumar ◽  
Mandla Venkata Ravibabu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Moisture Estimation

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals Surface Soil Moisture Retrieval Using Optical/Thermal Infrared Remote Sensing Data

2018 ◽  
Vol 56 (9) ◽  
pp. 5433-5442 ◽  
Author(s):  
Yawei Wang ◽  
Jian Peng ◽  
Xiaoning Song ◽  
Pei Leng ◽  
Ralf Ludwig ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Thermal Infrared ◽  
Surface Soil Moisture ◽  
Thermal Infrared Remote Sensing ◽  
Sensing Data ◽  
Infrared Remote Sensing

An integrated methodology for surface soil moisture estimating using remote sensing data approach

2019 ◽  
pp. 1-16
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Aafaf El Jazouli ◽  
Abdelghani Boudhar ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Integrated Methodology

scholarly journals Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3223
Author(s):  
Hamed Adab ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Mahmoud Moradian ◽  
Gholam Abbas Fallah Ghalhari
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
Machine Learning Methods ◽  
Land Use Types ◽  
Semi Arid Region ◽  
Semi Arid

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.

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