EGU21-15569, updated on 14 Dec 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

UAS Based Soil Moisture Downscaling Using Random Forest Regression Model

Ruodan Zhuang1, Salvatore Manfreda2, Yijian Zeng3, Nunzio Romano4,5, Eyal Ben Dor6, Antonino Maltese7, Paolo Nasta4, Nicolas Francos6, Fulvio Capodici7, Antonio Paruta7, Giuseppe Ciraolo7, Brigitta Szabó8, János Mészáros8, George P. Petropoulos9, Lijie Zhang10, and Zhongbo Su3,11
Ruodan Zhuang et al.
  • 1Department of Europe and Mediterranean Cultures: Architecture, Environment, and Cultural heritage (DiCEM), University of Basilicata, Matera, Italy
  • 2Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Napoli, Italy
  • 3Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
  • 4Department of Agricultural Sciences, AFBE Division, University of Naples Federico II, Portici, Italy
  • 5Interdepartmental Center for Environmental Research (C.I.R.AM.), University of Naples Federico II, Napoli, Italy
  • 6Department of Geography and Human Environment, Tel Aviv University, Tel Aviv, Israel
  • 7Department of Engineering, University of Palermo, Palermo, Italy
  • 8Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
  • 9Department of Geopgrpahy, Harokopio University of Athens, Athens, Greece
  • 10Institute of Bio- and Geosciences Agrosphere (IBG-3), Forschungszentrum Jülich, Jülich, Germany
  • 11Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Water and Environment, Chang’an University, Xi’an, China

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. High-resolution mapping of SM at field scale is vital for understanding spatial and temporal behavior of water availability in agriculture. Unmanned Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, this data can help the construction of downscaling models to generate high-resolution SM maps. For instance, random Forest (RF) regression model can link the land surface features and SM to identify the importance level of each predictor.

The RF regression model has been tested using a combination of satellite imageries, UAS data and point measurements collected on the experimental area Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). This area has been selected given the number of long-term studies on the vadose zone that have been conducted across a range of spatial scales.

The coarse resolution data cover from Jan 2015 to Dec 2019 and include SENTINEL-1 CSAR 1km SM product, 1km Land surface temperature and NDVI products from MODIS and 30m thermal band (brightness temperature), red and green band data (atmospherically corrected surface reflectance) from LANDSAT-8, and SRTM DEM from NASA. High-resolution land-surface features data from UAS-mounted optical, thermal, multispectral, and hyperspectral sensors were used to generate high-resolution SM and related soil attributes.

It is to note that the available satellite-based soil moisture data has a coarse resolution of 1km while the UAS-based land surface features of the extremely high resolution of 16cm. We deployed a two-step downscaling approach to address the smooth effect of spatial averaging of soil moisture, which depends on different elements at small and large scale. Specifically, different combinations of predictors were adopted for different scales of gridded soil moisture data. For example, in the downscaling procedure from 1km resolution to 30m resolution, precipitation, land-surface temperature (LST), vegetation indices (VIs), and elevation were used while LST, VIs, slope, and topographic index were selected for the downscaling from 30m to 16cm resolution. Indeed, features controlling the spatial distributions of soil moisture at different scale reflect the characteristics of the physical process: i) the surface elevation and rainfall patterns control the first downscaling model; ii) the topographic convergence and local slope become more relevant to reach a more detailed resolution. In conclusion, the study highlighted that RF regression model is able to interpret fairly well the spatial patterns of soil moisture at the scale of 30m starting from a resolution of 1km, while it is highlighted that the second downscaling step (up to few centimeters) is much more complex and requires further studies.

This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.

Keywords: soil moisture, downscaling, Unmanned Aerial Systems, random forest, HARMONIOUS

How to cite: Zhuang, R., Manfreda, S., Zeng, Y., Romano, N., Ben Dor, E., Maltese, A., Nasta, P., Francos, N., Capodici, F., Paruta, A., Ciraolo, G., Szabó, B., Mészáros, J., Petropoulos, G. P., Zhang, L., and Su, Z.: UAS Based Soil Moisture Downscaling Using Random Forest Regression Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15569,, 2021.