A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine
Due to its relation to the Earth’s climate and weather and phenomena like drought, flooding, or landslides, knowledge of the soil moisture content is valuable to many scientific and professional users. Remote-sensing offers the unique possibility for continuous measurements of this variable. Especia...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/11/2099 |
| _version_ | 1797532461973897216 |
|---|---|
| author | Felix Greifeneder Claudia Notarnicola Wolfgang Wagner |
| author_facet | Felix Greifeneder Claudia Notarnicola Wolfgang Wagner |
| author_sort | Felix Greifeneder |
| collection | DOAJ |
| description | Due to its relation to the Earth’s climate and weather and phenomena like drought, flooding, or landslides, knowledge of the soil moisture content is valuable to many scientific and professional users. Remote-sensing offers the unique possibility for continuous measurements of this variable. Especially for agriculture, there is a strong demand for high spatial resolution mapping. However, operationally available soil moisture products exist with medium to coarse spatial resolution only (≥1 km). This study introduces a machine learning (ML)—based approach for the high spatial resolution (50 m) mapping of soil moisture based on the integration of Landsat-8 optical and thermal images, Copernicus Sentinel-1 C-Band SAR images, and modelled data, executable in the Google Earth Engine. The novelty of this approach lies in applying an entirely data-driven ML concept for global estimation of the surface soil moisture content. Globally distributed in situ data from the International Soil Moisture Network acted as an input for model training. Based on the independent validation dataset, the resulting overall estimation accuracy, in terms of Root-Mean-Squared-Error and R², was 0.04 m<sup>3</sup>·m<sup>−3</sup> and 0.81, respectively. Beyond the retrieval model itself, this article introduces a framework for collecting training data and a stand-alone Python package for soil moisture mapping. The Google Earth Engine Python API facilitates the execution of data collection and retrieval which is entirely cloud-based. For soil moisture retrieval, it eliminates the requirement to download or preprocess any input datasets. |
| first_indexed | 2024-03-10T10:59:30Z |
| format | Article |
| id | doaj.art-7535e8126f494219b9b0ae750b6b3c00 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:59:30Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7535e8126f494219b9b0ae750b6b3c002023-11-21T21:36:00ZengMDPI AGRemote Sensing2072-42922021-05-011311209910.3390/rs13112099A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth EngineFelix Greifeneder0Claudia Notarnicola1Wolfgang Wagner2Institute for Earth Observation, Eurac Research, 39100 Bolzano, ItalyInstitute for Earth Observation, Eurac Research, 39100 Bolzano, ItalyDepartment of Geodesy and Geoinformation, TU Wien, 1040 Vienna, AustriaDue to its relation to the Earth’s climate and weather and phenomena like drought, flooding, or landslides, knowledge of the soil moisture content is valuable to many scientific and professional users. Remote-sensing offers the unique possibility for continuous measurements of this variable. Especially for agriculture, there is a strong demand for high spatial resolution mapping. However, operationally available soil moisture products exist with medium to coarse spatial resolution only (≥1 km). This study introduces a machine learning (ML)—based approach for the high spatial resolution (50 m) mapping of soil moisture based on the integration of Landsat-8 optical and thermal images, Copernicus Sentinel-1 C-Band SAR images, and modelled data, executable in the Google Earth Engine. The novelty of this approach lies in applying an entirely data-driven ML concept for global estimation of the surface soil moisture content. Globally distributed in situ data from the International Soil Moisture Network acted as an input for model training. Based on the independent validation dataset, the resulting overall estimation accuracy, in terms of Root-Mean-Squared-Error and R², was 0.04 m<sup>3</sup>·m<sup>−3</sup> and 0.81, respectively. Beyond the retrieval model itself, this article introduces a framework for collecting training data and a stand-alone Python package for soil moisture mapping. The Google Earth Engine Python API facilitates the execution of data collection and retrieval which is entirely cloud-based. For soil moisture retrieval, it eliminates the requirement to download or preprocess any input datasets.https://www.mdpi.com/2072-4292/13/11/2099soil moistureSentinel-1 SARLandsat-8 optical/thermal datamachine learningcloud-based approachGoogle Earth Engine |
| spellingShingle | Felix Greifeneder Claudia Notarnicola Wolfgang Wagner A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine Remote Sensing soil moisture Sentinel-1 SAR Landsat-8 optical/thermal data machine learning cloud-based approach Google Earth Engine |
| title | A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine |
| title_full | A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine |
| title_fullStr | A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine |
| title_full_unstemmed | A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine |
| title_short | A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine |
| title_sort | machine learning based approach for surface soil moisture estimations with google earth engine |
| topic | soil moisture Sentinel-1 SAR Landsat-8 optical/thermal data machine learning cloud-based approach Google Earth Engine |
| url | https://www.mdpi.com/2072-4292/13/11/2099 |
| work_keys_str_mv | AT felixgreifeneder amachinelearningbasedapproachforsurfacesoilmoistureestimationswithgoogleearthengine AT claudianotarnicola amachinelearningbasedapproachforsurfacesoilmoistureestimationswithgoogleearthengine AT wolfgangwagner amachinelearningbasedapproachforsurfacesoilmoistureestimationswithgoogleearthengine AT felixgreifeneder machinelearningbasedapproachforsurfacesoilmoistureestimationswithgoogleearthengine AT claudianotarnicola machinelearningbasedapproachforsurfacesoilmoistureestimationswithgoogleearthengine AT wolfgangwagner machinelearningbasedapproachforsurfacesoilmoistureestimationswithgoogleearthengine |