An Innovative Approach for Improving the Accuracy of Digital Elevation Models for Cultivated Land

Abstract

The redistribution of solar radiation, temperature, soil moisture and heat by topography affects the physical and chemical properties of the soil and the spatial distribution characteristics of crop growth. Analyses of the relationship between topography and these variables may help to improve the accuracy of digital elevation models (DEMs). The purpose of correcting Shuttle Radar Topography Mission (SRTM) data is to obtain high-precision DEM data in cultivated land. A typical black soil area was studied. A high-precision reference DEM was generated from an unmanned aerial vehicle (UAV) and extensive measured ground elevation data. The normalized differential vegetation index (NDVI), perpendicular drought index (PDI) extracted from SPOT-6 remote sensing images and potential solar radiation (PSR) extracted from SRTM. The interactions between topography and NDVI, PDI, and PSR were analyzed. The NDVI, PDI and PSR in June, July, August and September of 2016 and the SRTM were used as independent variables, and the UAV DEM was used as the dependent variable. Linear stepwise regression (LSR) and a back-propagation neural network (BPNN) were used to establish an elevation prediction model. The results indicated that (1) The correlation between topography and NDVI, PSR, PDI was significant at 0.01 level. The PDI and PSR improved the spatial resolution of SRTM data and reduce the vertical error. (2) The BPNN (R21 = 0.98, root mean square error, RMSE1 = 0.54) yielded a higher SRTM accuracy than did the studied linear model (RMSE1 = 1.00, R21 = 0.90). (3) A series of significant improvements in the SRTM were observed when assessed with the reference DEMs for two different areas, with RMSE reductions of 91% (from 14.95 m to 1.23 m) and 93% (from 15.6 m to 0.94 m). The proposed method improved the accuracy of existing DEMs and could provide support for accurate farmland management.