Affiliation:
1. Institute of Agrophysics of Polish Academy of Science, Doświadczalna Str. 4, 29-290 Lublin, Poland
Abstract
The soil moisture at the medium spatial scale is strongly desired in the context of satellite remote sensing data validation. The use of a ground-installed passive L-band radiometer ELBARA at the Bubnów-Sęków test site in the east of Poland gave a possibility to provide reference soil moisture data from the area with a radius of 100 m. In addition, the test site comprised three different land cover types that could be investigated continuously with one day resolution. The studies were focused on the evaluation of the ω-τ model coefficients for three types of land cover, including meadow, wetland, and cropland, to allow for the assessment of the soil moisture retrievals at a medium scale. Consequently, a set of reference time-dependent coefficients of effective scattering albedo, optical depth, and constant-in-time roughness parameters were estimated. The mean annual values of the effective scattering albedo including two polarisations were 0.45, 0.26, 0.14, and 0.54 for the meadow with lower organic matter, the meadow with higher organic matter, the wetland, and the cropland, respectively. The values of optical depth were in the range from 0.30 to 0.80 for the cropland, from 0.40 to 0.52 for the meadows (including the two investigated meadows), and from 0.60 to 0.70 for the wetland. Time-constant values of roughness parameters at the level of 0.45 were obtained.
Funder
21GRD08 SoMMet project
European Partnership on Metrology
Reference40 articles.
1. Agricultural drought over water-scarce Central Asia aggravated by internal climate variability;Jiang;Nat. Geosci.,2023
2. Boken, V. (2005). Agricultural Drought and Its Monitoring and Prediction: Some Concepts. Monitoring and Predicting Agricultural Drought: A Global Study, Oxford Academic.
3. Agricultural extreme drought assessment at global level using the FAO-Agricultural Stress Index System (ASIS);Rojas;Weather Clim. Extrem.,2020
4. Van Alfen, N.K. (2014). Climate Change: New Breeding Pressures and Goals. Encyclopedia of Agriculture and Food Systems, Academic Press.
5. Celik, M.F., Isik, M.S., Yuzugullu, O., Fajraoui, N., and Erten, E. (2022). Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote Sens., 14.