Abstract
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 m3·m−3 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.
Highlights
The soil moisture content (SMC) is a crucial state variable in the complex global cycles of water, energy, and carbon, and is very relevant for studying the Earth’s climate and weather [1]
It is noteworthy that a model with a relatively low level of comote Sens. 2021, 13, x FOR PEER REVIEW
This study introduced an approach to estimate SMC at a high spatial resolution on a quasi-global scale
Summary
The soil moisture content (SMC) is a crucial state variable in the complex global cycles of water, energy, and carbon, and is very relevant for studying the Earth’s climate and weather [1]. Satellite remote sensing presents the only possibility for the spatially continuous measurement of surface SMC over large areas. Widely used approaches belong to two main categories: those based on active or passive microwave remote sensing, and those based on optical (i.e., shortwave and thermal radiation) remote sensing. The underlying methods for the estimation of SMC are fundamentally different. Most microwave-based retrieval algorithms rely on the same principle, exploiting the dielectric properties of water and its effect on the reflected microwave radiation [3]. Many different approaches exist, exploiting the relationship between
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