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[Remote sensing inversion of surface soil organic matter at jointing stage of winter wheat based on unmanned aerial vehicle multispectral]. / 基于无人机的冬小麦拔节期表层土壤有机质含量遥感反演.
Wang, Xi; Li, Yu-Huan; Wang, Rui-Yan; Shi, Feng-Zhi; Xu, Shao-Tang.
Afiliación
  • Wang X; College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, Shandong, China.
  • Li YH; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Tai'an 271018, Shandong, China.
  • Wang RY; College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, Shandong, China.
  • Shi FZ; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Tai'an 271018, Shandong, China.
  • Xu ST; College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, Shandong, China.
Ying Yong Sheng Tai Xue Bao ; 31(7): 2399-2406, 2020 Jul.
Article en Zh | MEDLINE | ID: mdl-32715706
The rapid monitoring of soil organic matter (SOM) content in large-scale salinized wheat fields can provide data for promoting research in saline soils and carbon cycle. Based on field sampling and remote sensing images of unmanned aerial vehicle, we established remote sensing prediction models of regional SOM using three methods, i.e., multiple linear regression (MLR), partial least squares (PLSR), and support vector machine regression (SVR) for bare land and wheat field, respectively. The models were validated and compared to identify the optimal inversion model of SOM. Moreover, the SOM in the area was inverted using the optimal model, with the inversion results being compared with the data by interpolation. The results showed that the spectrum after the filtering of 5×5 median was best related to surface SOM. Among the three models, the SVR model had the highest prediction accuracy, followed by the PLSR, while the MLR lowest. The SVR model was the best one for estimating wheat field, with coefficient of determination (R2) and root mean square error (RMSE) of 0.89 and 0.20, respectively, and the validated R2 and RMSE were 0.82 and 0.24, respectively. The bare land SOM was also best fitted by the SVR model, with R2 and RMSE were 0.63, 0.26, respectively, and the verified R2 and RMSE were 0.61, 0.25, respectively, but without statistical significance. The inversion of the optimal model revealed that SOM content in this region ranged from 17.51 to 22.53 g·kg-1, with an average of 19.51 g·kg-1, which was generally consistent with the field measurement. Compared with the inversion results, the interpolation data were limited in accuracy. Overall, our study suggested that the unmanned aerial vehicle-based multi-spectral analysis could be applied to quick and accurate estimation of SOM content in saline soil at the jointing stage of winter wheat.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Suelo / Triticum Tipo de estudio: Prognostic_studies Idioma: Zh Revista: Ying Yong Sheng Tai Xue Bao Asunto de la revista: SAUDE AMBIENTAL Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Suelo / Triticum Tipo de estudio: Prognostic_studies Idioma: Zh Revista: Ying Yong Sheng Tai Xue Bao Asunto de la revista: SAUDE AMBIENTAL Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: China