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基于无人机影像和宽度学习的小麦分蘖期土壤盐分反演

Soil salinity inversion at wheat tillering stage based on UAV image and broad learning system

  • 摘要: 为提高土壤含盐量的反演精度,该研究基于2023和2024年的无人机多光谱影像数据和野外实测土壤表层(0~15 cm)含盐量,提取采样点光谱反射率与图像纹理特征,在此基础上引入红边波段计算光谱指数,利用皮尔逊相关系数法(pearson correlation coefficient,PCC)、灰色关联度分析法(grey relational analysis,GRA)及变量投影重要性分析(variable importance in projection,VIP)优选特征变量,以光谱指数、纹理特征和光谱指数-纹理特征的组合为模型输入组,构建54个基于宽度学习(broad learning system,BLS)、反向传播神经网络(back-propagation neural network,BPNN)和随机森林(random forest,RF)的反演模型,绘制基于最优模型的土壤盐分空间分布图,以小麦地为例,评价并确定土壤含盐量最佳反演模型。结果表明:从不同特征变量组合方式来看,基于光谱指数-纹理特征作为输入组的PCC-BLS模型反演效果优于其他模型,2023年最优模型的验证集决定系数Rp2为0.851,均方根误差RMSEP为0.032%,平均绝对误差MAEP为0.027%;2024年最优模型的Rp2为0.811,RMSEP为0.058%,MAEP为0.033%。从不同建模方法来看,基于BLS的模型反演精度整体优于BPNN模型和RF模型,2023年Rp2相较于BPNN和RF分别提高了0.189、0.289;2024年Rp2分别提高了0.161、0.145,反演结果能客观反映土壤含盐量。从耦合模型反演结果来看,BLS与3种筛选方法均取得了较好的效果,且PCC-VIP-BLS耦合模型的鲁棒性整体最好,Rp2/Rc2在0.867及以上。研究结果可为土壤盐碱化监测提供参考。

     

    Abstract: Soil salinization has seriously threatened food security and ecological stability in recent years, due to the degradation and crop yield reduction. It is often required to rapidly and accurately obtain soil salinization information in the sustainable production of farmland. One of the typical ' no irrigation, no agriculture ' areas is found in the Bianwan Farm of the Taolai River Basin in Gansu Province, China. There is less rain/drought and strong evaporation. The average annual precipitation and evaporation are 72.6 and 2184.0 mm, respectively. However, the conventional field sampling cannot monitor the large-scale soil salinization, due mainly to its time-consuming, labor-intensive, and costly nature. Remote sensing has emerged as an effective technical support in precision agriculture. This study aims to invert the soil salinity at the wheat tillering stage using UAV images and broad learning. Bianwan Farm was used as the representative sampling area of soil salinization. The multispectral of UAV remote sensing was used to capture the image data and the soil salt content in the wheat field in 2023 and 2024. The spectral reflectance and texture of the sampling points were then extracted from the remote sensing images. The red edge band was introduced to calculate the spectral index. The feature variables were optimized using Pearson correlation coefficient (PCC), grey relational analysis (GRA), and variable importance in projection (VIP). Scheme 1 (spectral index), Scheme 2 (texture feature), and Scheme 3 (spectral index-texture feature) were used as the model input groups. And then 54 models were constructed using Broad learning system (BLS), Back-Propagation Neural Network (BPNN), and Random Forest (RF). The spatial distribution of the soil salinity was then obtained according to the optimal model. A comparison was made on the inversion accuracy of the characteristic variables with the different models. The optimal inversion model of the soil salinity was determined for farmland in the irrigation area. The results show that the better inversion was achieved in the BLS model with Scheme 3 (spectral index-texture feature) as the input group among the different combinations of the characteristic variables. The coefficient of determination (Rp2) on the validation set of the optimal model in 2023 was 0.851, the root mean square error (RMSEP) was 0.032%, and the average absolute error (MAEP) was 0.027%. The Rp2 of the optimal model in 2024 was 0.811, RMSEP was 0.058%, and MAEP was 0.033%. Furthermore, the inversion accuracy of the BLS was better than that of the BPNN and RF models. The Rp2 values in 2023 were 0.189 and 0.289 higher than those of the BPNN and RF, respectively. The Rp2 in 2024 increased by 0.161 and 0.145, respectively, indicating a better representation of the soil salt content. The BLS and three screening were achieved in the excellent inversion of the coupling model. The high robustness of the PCC-VIP-BLS coupling model was also obtained with the Rp2/Rc2 above 0.867. The finding can provide a strong reference and theoretical basis for the rapid inversion of the soil salt content in the saline-alkali soil in the arid area of northwest China.

     

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