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

Yuyang Ma; Huanjun Liu; Baiwen Jiang; Linghua Meng; Haixiang Guan; Mengyuan Xu; Yang Cui; Fanchang Kong; Yue Yin; MengPei Wang

doi:10.3390/rs12203401

Remote Sensing (Oct 2020)

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

Yuyang Ma,
Huanjun Liu,
Baiwen Jiang,
Linghua Meng,
Haixiang Guan,
Mengyuan Xu,
Yang Cui,
Fanchang Kong,
Yue Yin,
MengPei Wang

Affiliations

Yuyang Ma: School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China
Huanjun Liu: School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China
Baiwen Jiang: College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China
Linghua Meng: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China
Haixiang Guan: School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China
Mengyuan Xu: College of Resources and Environmental Sciences, China Agricultural University, Beijing 100000, China
Yang Cui: School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China
Fanchang Kong: College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China
Yue Yin: School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China
MengPei Wang: School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China

DOI: https://doi.org/10.3390/rs12203401
Journal volume & issue: Vol. 12, no. 20
p. 3401

Abstract

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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.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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