Nontradional Detection of Soil Moisture Content Based On Hyperspectral Imaging Technique

Autor: Longguo Wu, Qiufei Jiang, Yao Zhang, Songlei Wang
Rok vydání: 2023
Předmět:
Zdroj: Bangladesh Journal of Botany. :1039-1049
ISSN: 2079-9926
0253-5416
DOI: 10.3329/bjb.v51i40.63847
Popis: In the study, Vis-NIR hyperspectral imaging technique was investigated for non-destructive determination of soil moisture content. A total of 208 spectral information of soil samples by hyperspectral imaging system were collected. The differences between soil's moisture content and spectral changes were compared. Different methods of spectral data preprocessing methods, characteristic wavelengths extraction, and building models had influences on the performance of model established. Results from this investigation demonstrated that the reflectivity of soil spectrum were declining with the increase of soil moisture content. Once soil moisture content reached the limit of the field water holding capacity, the reflectivity of soil spectrum were increasing with the increase of soil moisture content. The methods of different pretreatment were analyzed, and the pretreatment method of normalization of unit vector was proposed. The number of characteristic wavelengths extracted by the method of uninformative variable eliminate (UVE), competitive adaptive reweighted sampling (CARS), beta coefficient (β), successive projections algorithm (SPA) were 49, 30, 5, 7, respectively. In order to reduce data redundancy, characteristic wavelengths extracted by the method of UVE and CARS were further extracted by SPA method. The number of characteristic wavelengths extracted by the method of UVE + SPA and CARS + SPA were 5, 8, respectively. The MLR model built based on the characteristic wavelengths extracted by the β coefficient was chosen as the best model, which of the optimal characteristic wavelengths were 411, 440, 622, 713, 790 nm, respectively. The correlation coefficient (Rp) and the root mean square error (RMSEP) for prediction set were 0.979 and 0.763, respectively. Therefore, it is feasible to predict soil moisture content using hyperspectral imaging technique. Bangladesh J. Bot. 51(4): 1039-1049, 2022 (December) Special
Databáze: OpenAIRE
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