Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (8): 1715-1727.doi: 10.3864/j.issn.0578-1752.2021.08.011

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

A New Method to Improve the Accuracy of Digital Elevation Model in Northeast China by Using Terrain, Soil and Crop Information

MA YuYang1(),GUAN HaiXiang1,YANG HaoXuan1,SHAO Shuai1,SHAO YiQun3,LIU HuanJun1,2()   

  1. 1College of Public Administration and Law, Northeast Agricultural University, Harbin 150030
    2Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012
    3College of Resources and Environment, Northeast Agricultural University, Harbin 150030
  • Received:2020-07-02 Accepted:2020-08-28 Online:2021-04-16 Published:2021-04-25
  • Contact: HuanJun LIU E-mail:2224317974@qq.com;huanjunliu@yeah.net

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Abstract:

【Objective】SRTM DEM is a publicly available DEM accessible at no cost. However, it is well known that SRTM DEM has a large vertical deviation. In order to improve the accuracy of SRTM DEM in cultivated land, the effects of topography on the temporal and spatial distribution of soil physical and chemical properties and crop growth were analyzed to mine the factors of interaction with topography, so as to obtain a digital elevation model for precision agriculture. 【Method】This paper took Helen Dongxing Agricultural Machinery Cooperative in Heilongjiang Province as the study area, the actual ground elevation data were collected, and SPOT-6, Sentinel-2A remote sensing images and SRTM DEM were obtained. The variables, such as normalized differential moisture index (NDMI), normalized difference vegetation index (NDVI), Tasseled Cap Brightness (TCB) and potential solar radiation (PSR), were extracted, and the effects of topography on them were analyzed. Extreme Learning Machine (ELM) and back Propagation Neural Network (BPNN) were used to improve horizontal spatial resolution and vertical accuracy of SRTM DEM. The accuracy was verified with the actual ground elevation point and compared with the DEM generated by the UAV and the ZY-3. 【Result】The correlation degree between SRTM, NDVI, NDMI, TCB and the improved elevation were more than 90%, which were important factors for improving SRTM DEM. In the whole study area, the RMSEP of BPNN method was 0.98, the RMSEP of R2P was 0.98, and the RMSEP of Elm was 1.00, R2P was 0.90. In the flat area, the RMSEP of BPNN method was 0.84 and the RMSEP of ElM was 1.00. In the fluctuation area, the RMSEP of BPNN method was 0.99, and the RMSEP of ELM was 0.94. The vertical accuracy of DEM obtained by this method was higher than that of DEM generated by ZY-3. It provided a new idea for improving the spatial resolution of SRTM. 【Conclusion】 The introduction of auxiliary information of SRTM, NDVI, NDMI and TCB was helpful to improve the spatial resolution and vertical accuracy of SRTM DEM, so as to obtain high accuracy DEM. The accuracy of digital elevation model obtained by BPNN method improved by SRTM DEM was higher than that obtained by ELM method as a whole. In addition, the further research showed that the BPNN method was more suitable for the acquisition of high-precision DEM in the flat area, and the ELM method was more suitable for the acquisition of high-precision DEM in the undulating area. The research results improved the accuracy of the existing DEM and provided data support for accurate farmland management zoning and digital soil mapping.

Key words: SRTM DEM, neural network, multispectral image, grey relational analysis, variance expansion factor

Fig. 1

Spatial location map of the study area"

Table 1

Descriptive statistics of measured elevation"

样本 Sample 差值 Difference 最小值Min 最大值Max 均值Mean 标准差Std
N=363 22.10 230.33 252.43 242.02 5.39

Table 2

Improved the preselected variables of SRTM"

变量 Variable 描述 Description 数据源 Source
归一化植被指数 NDVI (NIR-RED)/(NIR+RED) SPOT-6
绿度 TCG -0.2848×B2-0.2435×B3-0.5436×B4+0.7243×B8+0.0840×B11-0.1800×B12 Sentinel-2A
亮度 TCB 0.3037×B2+0.2793×B3+0.4743×B4+0.2285×B8+0.5082×B11+0.1863×B12 Sentinel-2A
湿度TCW 0.1509×B2+0.1973×B3+0.3279×B4+0.3406×B8+0.7112×B11+0.4572×B12 Sentinel-2A
归一化湿度指数NDMI (B3-B11)/(B3+B11) Sentinel-2A
潜在太阳辐射 PSR 根据地形和仰视半球视域范围算法得到
According to the terrain and looking up hemisphere field of view algorithm		 SRTM
SRTM 通过航天飞机雷达地形测绘任务获得的30 m高分辨率的地形数据
30 m high-resolution terrain data obtained through SRTM		 SRTM

Fig. 2

Relationship between potential solar radiation and elevation a, b, c, d, e, and f represent the six sections of figure 1, respectively. The same as Fig 3, 4, 7"

Table 3

Descriptive statistics of soil water content (cm·cm-3) on shady and sunny slopes"

差值 Difference 最小值Min 最大值Max 均值Mean 标准差Std
阴坡Sunny slope 0.0627 0.228 0.290 0.252 0.018
阳坡Shady slope 0.0626 0.211 0.274 0.229 0.144

Fig. 3

Relationship between normalized humidity index and elevation"

Table 4

Correlation statistics between measured elevation and variables"

高程
Elevation		 归一化湿度指数
NDMI		 归一化植被指数
NDVI		 亮度
TCB		 潜在太阳辐射
PSR		 航天飞机雷达地形测绘任务SRTM
高程Elevation -0.93** 0.32** 0.29** 0.26* 0.99**
归一化湿度指数NDMI -0.30** -0.24** -0.25** -0.93**
归一化植被指数NDVI 0.06 0.05 0.31**
亮度TCB 0.97** 0.28**
潜在太阳辐射PSR 0.26**
SRTM

Fig. 4

The relationship between tasseled cap brightness and elevation"

Fig. 5

Relationship between normalized vegetation index and topography a: Concave slope; b: Compound slope with the first concave and then convex; c: Compound slope with the first convex and then concave; d: Convex slope"

Table 5

Collinear diagnosis of pre-selected variables and input variables"

共线性统计 Collinearity statistics
容忍度 Tolerance 方差膨胀因子VIF
预选因子Preselected variable
归一化植被指数NDVI 0.676 1.479
归一化湿度指数NDMI 0.021 47.941
湿度TCW 0.004 226.167
亮度TCB 0.002 410.595
绿度TCG 0.349 2.862
潜在太阳辐射PSR 0.885 1.131
航天飞机雷达地形测绘任务SRTM 0.193 5.177
输入因子Input variables
归一化植被指数NDVI 0.717 1.394
归一化湿度指数NDMI 0.136 7.366
亮度TCB 0.124 8.071
潜在太阳辐射PSR 0.786 1.273
SRTM 0.901 1.110

Fig. 6

The correlation between the input variable and the improved SRTM DEM"

Table 6

Accuracy verification of reference and improved SRTM DEM and measured elevation"

米
(m)		 SRTM 重采的SRTM
Resampled SRTM		 反向传播神经网络
BPNN DEM		 极限学习机
ELM DEM		 资源三号DEM
ZY-3 DEM		 无人机DEM
UAV DEM
均方根误差RMSE 14.77 14.82 0.98 1.01 3.69 0.10
偏率Bias 0.92 0.91 0.97 0.97 0.95 0.99

Table 7

Accuracy assessment of models"

建模精度 Modelling Accuracy 验证精度Validation Accuracy
均方根误差RMSE (m) 决定系数R2 均方根误差 RMSE (m) 决定系数 R2
极限学习机ELM 0.82 0.96 1.00 0.90
反向传播神经网络BPNN 0.69 0.98 0.98 0.97

Fig. 7

Spatial distribution map of DEM from different data sources"

Table 8

Accuracy assessment of flat and undulating areas"

平坦区 Flat area 起伏区 Fluctuation area
均方根误差RMSE (m) 偏率 Bias 均方根误差 RMSE (m) 偏率 Bias
极限学习机ELM 1.00 0.98 0.94 1.01
反向传播神经网络BPNN 0.84 0.99 0.99 1.02
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