Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band

Abstract

This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a focus on mountain areas. The novelties of the paper are: the extension of an already developed method to coarse resolution data (150 m) in mountain environment with high land heterogeneity, with only VV polarization and the proper selection of input features. During the result analysis, several algorithm characteristics were clearly identified: 1) the performances showed to be strongly related to input features such as topography and vegetation indices; 2) the algorithm needs a training phase; 3) the averaging window needs to be proper selected to take into account both the speckle noise and the characteristics of the area under investigation; and 4) the algorithm, being data driven, can be considered as site dependent. The experimental analysis is carried out on images acquired over the Südtirol/Alto Adige Province in Italy during 2010-2011 from the RADARSAT2 and Envisat ASAR in Wide Swath mode. SMC maps were compared with spatially distributed ground measurements, resulting in a root mean squared error (RMSE) value ranging from 0.045 to 0.07 m 3 /m 3 . Concerning the multiscale analysis, the results indicated that RADARSAT2 maps are able to detect the spatial heterogeneity and soil moisture dynamics at local scale, while ASAR WS SMC maps are able to identify mainly the two main classes of pasture and meadows. When these estimates are compared with SMC values from meteorological stations a RMSE value of 0.10 m 3 /m 3 for both satellites indicated a reduced capability to follow the temporal dynamics.

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

“…Many studies have highlighted the importance of exploiting additional vegetation information provided by optical remote sensing for retrieving soil moisture from a vegetated area [21][22][23][45][46][47]. The indices NDVI and LAI are among the most widely used vegetation indices.…”

“…It should be noted that the radar data used in this study was C-band data from the Sentinel-1 satellite and in the single polarization mode (VV polarization). Previous studies have shown that VV polarization is not the most relevant polarization in the soil moisture retrieval process [29,44,45]. Also, the studied areas are under vegetation cover and the C band is not considered the best band for exploiting in areas with vegetation cover.…”

“…Due to its flexibility, estimation accuracy, and generalization capabilities, SVR is usually exploited in the field of geo/biophysical variable retrieval [45,61,62].…”

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

“…Physically based models have been applied to retrieve the SM successfully [6,7], but these models require extensive information regarding the vegetation and soil surface, which might be difficult to obtain at a larger scale. Several empirical, regression or machine learning based models have been used to retrieve the SM with relative success (e.g., [8][9][10][11][12]). A major disadvantage of these models is the requirement of extensive in situ SM to calibrate the models.…”

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

“…leads to the same electromagnetic response at the sensor. In addition to this, one has to take into account the sensitivity of the microwave signal to various target properties (e.g., soil roughness and vegetation coverage) and the effect of topography and land use heterogeneity [12,115,116]. Soil moisture retrieval has been addressed by several methodologies that fall into the following main categories:…”