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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 4, pp. 113-128

Field testing of the cartographic modeling of soil water content of the surface layer of soil cover based on Sentinel-1 radar survey and digital elevation model

A.M. Zeyliger 1 , K.V. Muzalevskiy 2 , E.V. Zinchenko 3 , O.S. Ermolaeva 1 , V.V. Melikhov 3 
1 Russian State Agrarian University ― Moscow Timiryazev Agricultural Academy, Moscow, Russia
2 L.V. Kirensky Institute of Physics SB RAS, Krasnoyarsk, Russia
3 All-Russian Scientific Research Institute of Irrigated Agriculture, Volgograd, Russia
Accepted: 07.06.2020
DOI: 10.21046/2070-7401-2020-17-4-113-128
The surface moisture content (SMC) is one of the key parameters, which is used for the quantitative description of soil hydrological state as well as the estimation of soil water availability to vegetation canopy. Since the radar backscattering coefficient is sensitive to SMC, in this investigation Sentinel-1 data was used for soil moisture mapping with a high spatial resolution, based on which the spatial and temporal patterns of soil moisture distribution at field level was mapped for implementing in the management of soil and water resources. Direct measurements of SMC in a layer thickness of 5 cm were carried out during field monitoring at an experimental test site, located on the territory of the All-Russian Scientific Research Institute of Irrigated Agriculture (Vodny village, Volgograd region). In the given coordinates on the test site, soil samples were taken, the moisture content of which was determined using the thermostat-weight method. As a result, the first point georeferenced layer of SMC was created. At the same time, the estimation of SMC based on Sentinel-1 radar observations was performed for the same spatial extent of the test site. The raster set of SMC within the boundaries of the test site was calculated from the Sentinel-1 remote sensing (RS) observations. This layer will be named SMC-RS. These calculations were based on the assessment of soil reflectivity obtained by neural network (NN) method and the further solution of the inverse problem using a dielectric model, which takes into account the soil clay content at the test site. During the training of the NN, backscatter coefficients measured by Sentinel-1 at co- and cross-polarization were used as input data. As the output data of the NN, the value of soil reflectivity, which was estimated based on point georeferenced layer of SMC and a dielectric model were used. Terrain correction of Sentinel-1 image was carried out using a digital elevation model (DEM), created by Phantom 4 Pro unmanned aerial vehicle as the result of stereo photography. As a result of comparing the georeferenced data sets SMC and SMC-RS obtained during field monitoring and remote sensing, respectively, the following values of determination coefficient (0.948) and standard deviation (2.04 %) were estimated. This result confirms a satisfactory linear correlation between both data sets. The comparison of the two layers of point georeferenced data sets indicates that the first set is well correlated by the second. This conclusion was obtained as the result of ground monitoring and cartographic modelling carried out using the developed method, Sentinel-1 observation and DEM. The developed method can be considered as the scientific and methodological basis of the new technology for the cartographic monitoring of SMC, which is currently treated as one of the main basic characteristics to be used in precision irrigated agriculture.
Keywords: soil cover, soil moisture, point data, raster data, particle size distribution, surface roughness, digital terrain model, UAV, radar imaging, Sentinel-1, radar backscatter, neural networks, dielectric soil model
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