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Pedo-transfer function and remote-sensing-based inversion saturated hydraulic conductivity of surface soil layer in Xilin River basin

: Li, M.; Liu, T.; Luo, Y.; Duan, L.; Zhang, J.; Zhou, Y.; Buren, S.

Tu rang xue bao = Acta Pedologica Sinica (2019), Nr.1, S.90-100
ISSN: 0564-3929
Fraunhofer IOSB ()

Objective: Remote sensing technology has successfully been applied to monitoring and inversion of soil parameters. In order to further promote application of the Synthetic Aperture Radar(SAR) technology to less-destructive soil monitoring and environmental management, based on the field survey and sampling, laboratory experiments and interpretation of microwave radar images, this study explored possibilities of inverting and predicting soil hydraulic parameters of semi-arid grassland basins on the premise of no large scaled damage.
Method: In this paper, the Xilin River Basin, Inner Mongolia was set as subject for the study. The study area had 5 major types of soils, i.e. Thick chestnut soil, Meadow swamp soil, Desert aeolian soil, Limy meadow sandy soil, and Pale black soil. From the surface layers of the soils, soil samples were collected for analysis of particle size composition, bulk density, organic matter content, saturated hydraulic conductivity(KS) and some other physical and chemical properties. First of all,distributions of soil parameters in soil layers, 10 cm each, of the five types of soils were characterized. Then Saxton, Cosby, and Wosten models, three saturated soil hydraulic conductivity pedo-transfer functions(PTFs)and nonlinear multivariate empirical regression models, were used for fitting of KS in soil layers, 10 cm each, within the 0~30 cm soil layers of the 32 sampling sites for modeling. Based on the averages of the soil parameters of these 32 sampling sites for modeling and backscattering of quadrupolarized Radarsat-2,a multivariate linear equation was established, using the radar data of the 10 sampling sites for validation to validate the fitting of KS.
Result: Results show that the study area is extremely high in soil sand content, almost nil in clay content and low in organic matter content. The parameters do not vary much with soil depth from layer to layer. In terms of PTFs, the four models reach 0.778, 0.985, 0.958, and 0.966 in modeling accuracy separately. Among them, Saxton model is the highest, with RMSE being 0.262 and layer average validation accuracy reaching 0.989. In terms of inversion of surface soil parameters based on back scattering coefficient of quadrupolarized RADARSAT-2, the inversions of bulk density and sand content are the best. In using the backscattering coefficient of the 10 validating sampling sites to validate PTFs,Saxton model is superior in fitting, with simulation coefficient reaching as high as 0.964. Consequently, this study has finally chosen Saxton model to predict surface soil saturated hydraulic conductivity on an 8 meter precision extended scale based on remote-sensing images of the study area, by combining Radarsat-2 radar data.
Conclusion: All the findings indicate that compared with inversion directly using SAR to predict surface soil KS in a large-scale, PTFs may better depict saturated conductivity of the surface soil layer in semi-arid grassland watersheds. However, the effect of PTFs are not directly related to the number of parameters contained in the model. Surface soil KS in the degraded grassland varies generally in the range of 4~8 m·d-1. Under natural conditions, the region lacks vegetation and high in sand content, which are the main reasons for rapid water transfer. In developed areas, like irrigation zones or urbanized regions, surface soil KS declines by a large margin, indicating that human activity is one of the main influencing factors of its change. The use of remote sensing to predict surface soil KS over a region is still not fully developed, so more efforts should be done to perfect and validate it.