Crops ›› 2023, Vol. 39 ›› Issue (1): 233-238.doi: 10.16035/j.issn.1001-7283.2023.01.035

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Hyperspectral Response Characteristics and Monitor on Soil Water

Zhang Haitao(), Yan Xiaobin, Feng Meichen, Yang Wude, Xiao Lujie, Song Xiaoyan, Zhang Meijun, Wang Lele, Wang Chao()   

  1. College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, Shanxi, China
  • Received:2021-10-12 Revised:2021-12-01 Online:2023-02-15 Published:2023-02-22

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Abstract:

In order to timely and accurately monitor the soil water content (SMC), using the method of artificial simulation of different SMC environments under indoor conditions, by measuring the SMC and its corresponding soil spectral reflectance, using the continuous projection algorithm (SPA) to extract the characteristic wavelength of soil moisture, combined with multiple linear regression (MLR) method, constructed the hyperspectral quantitative monitoring model of SMC. The results showed that the spectral reflectance gradually decreased with the increase of SMC, and there was an obvious negative correlation between them. These spectral wavelengths of 422, 629, 817, 976, 1121, 1258, 1359, 1448, 1830 and 2022nm extracted by the method of SPA were proved to be closely related with the SMC. The combined method of SPA-MLR was adopted to construct the predictive model of SMC based on the extracted spectral wavelengths and it achieved good accuracy and stability (calibration set: R2=0.930, RMSE=8.845, RPD=3.794; validation set: R2=0.927, RMSE=8.799, RPD=3.581). The research results would provide some practical exploration and theoretical reference for the efficient monitoring of SMC with the hyperspectral technology.

Key words: Soil moisture, Hyperspectral, Successive projection algorithm-multiple linear regression (SPA- MLR), Monitor model

Table 1

Physicochemical properties of soil samples"

编号
Number		 全碳
Total carbon (%)		 全氮
Total nitrogen (mg/g)		 容重
Bulk density (g/cm3)		 黏粒含量
Clay content (%)		 沙粒含量
Sand content (%)
1 0.583 4.318 1.293 22.875 77.125
2 0.236 3.669 1.167 15.376 84.624
3 1.084 4.081 1.268 30.112 69.888
4 0.897 1.395 1.574 38.163 61.837
5 0.806 3.268 1.292 26.084 73.916
6 1.293 2.328 1.394 26.829 73.171
7 1.303 3.421 1.343 28.107 71.893
8 1.491 3.192 1.387 26.884 73.116
9 1.397 2.464 1.480 35.202 64.798
10 2.955 2.828 1.467 32.694 67.306

Table 2

Descriptive statistical analysis of the SMC %"

样本数
Sample		 范围
Range		 最小值
Min.		 最大值
Max.		 平均值
Average		 标准差
SD		 偏度
Skewness		 峰度
Kurtosis
总样本Total sample 88 106.978 0.021 106.999 24.981 32.899 0.968 -0.487
校正集Calibration set 66 106.978 0.022 106.999 25.691 33.554 0.962 -0.479
验证集Validation set 22 91.471 0.021 91.492 22.853 31.510 1.035 -0.459

Fig.1

Hyperspectral characteristics under different levels of the SMC"

Fig.2

Relationship between the SMC and soil spectra and hyperspectral characteristic bands of the SMC"

Fig.3

The 1:1 fitting diagram of SMC spectral monitoring model The dashed line is a 1:1 fitting line, the solid line is a fitting line of predicted and measured values"

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