Name
Title | ||
|---|---|---|
Optimal Window Size Selection For Spectral Information Extraction Of Sampling Points From Uav Multispectral Images For Soil Moisture Content Inversion |
Abstract | ||
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Soil moisture content monitoring with UAV remote sensing always involves the selection of an appropriate window size for spectral information extraction, but research on the effect of window size on the accuracy of soil moisture content monitoring models and the selection of the optimal window size has been rarely reported. To solve these problems, an experiment was conducted on three typical bare plots in Shahaoqu Experimental Station at Hetao Irrigation District, Inner Mongolia, China. First, remote sensing images were obtained from the three bare plots from April 15 through 17, 2019, with a six-rotor UAV equipped with a six-channel multispectral camera. Synchronously, the moisture content at 0-10 cm of the surface soil was measured using the drying method. Then, the spectral information was extracted through windows of 16 different sizes (ranging from 1 * 1 to 31 * 31). Followed was the construction of thirty spectral indices using the ratio and normalized ratio methods, and the processing of the constructed indices using principal component analysis. The principal components accounting for 95% of the cumulative contribution rate were selected as the input variables for the construction of the monitoring models based on BP neural network. Finally, the model accuracy was tested using ANOVA, and the local variogram of the spectrum was used to explore the optimal window size selection. The results demonstrated: (1) There are differences in the spectral information extracted from different sizes of windows, which affects the accuracy of soil moisture monitoring model; (2) The spatial autocorrelation threshold of the plots at the local variogram was 13 * 13, resembling the window size with the highest accuracy, so it is feasible to select the optimal window size with the local variogram; (3) As the window size of spectral information increased, R2 first increased and then decreased, reaching the maximum value of 0.261 at the size of 13 * 13, and RMSE first decreased and then increased, reaching the minimum value of 0.017 when at the size of 7 * 7. These results can provide some reference for window size selection in spectral information extraction to monitor soil moisture content. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compag.2021.106456 | COMPUTERS AND ELECTRONICS IN AGRICULTURE |
Keywords | DocType | Volume |
Soil moisture content, Window size, UAV remote sensing, ANOVA, Local variance | Journal | 190 |
ISSN | Citations | PageRank |
0168-1699 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuqian Bai | 1 | 0 | 0.34 |
| Yinwen Chen | 2 | 0 | 0.68 |
| Junying Chen | 3 | 0 | 0.34 |
| Wenxuan Cui | 4 | 0 | 0.34 |
| Xiang Tai | 5 | 0 | 0.34 |
| Zhitao Zhang | 6 | 1 | 1.03 |
| Jiguang Cui | 7 | 0 | 0.34 |
| Jifeng Ning | 8 | 2 | 2.40 |