LAI无人机多光谱遥感估测及其在盐渍土改良中的应用

【目的】植被产量能综合直观地反映盐渍土改良效果,冬小麦生长旺盛期的叶面积指数（LAI）是植被产量的常用衡量指标。本研究利用无人机多光谱遥感获取冬小麦生长旺盛期的叶面积指数分布数据,对盐渍土改良效果进行客观准确评价,为人们筛选盐渍土改良技术和产品提供科学指导。【方法】以无棣县渤海粮仓滨海盐渍土改良试验区为研究区,基于无人机多光谱遥感数据,利用线性回归分析、偏最小二乘、随机森林和支持向量机等方法,构建拔节期冬小麦LAI反演模型;并利用因子分析法对盐渍土地区抽样地块进行改良效果评价,建立盐渍土改良效果LAI评价模型,基于该评价模型对整个试验区盐渍土改良效果进行评价。【结果】对冬小麦LAI遥感估测而言,并不是分辨率越高越好,而是5×5均值平滑后的光谱数据与一垄小麦叶面积指数的对应最佳。LAI遥感估测模型中,利用支持向量机建立的模型精度最高。改良效果LAI评价模型的预测结果表明,LAI对盐渍土改良效果的预测精度较高,改良效果最优地块的编号为26、27、28、29、30和31,最优改良方法为引黄淤灌和增施有机肥综合改良措施。【结论】无人机遥感可对盐渍土地区拔节期冬小麦的叶面积指数进行准确反演,基于LAI反演结果的盐渍土改良效果评价能够从众多试验小区中定位出最优的改良效果。与传统方法相比,该方法具有成本低廉、精度高等优势,研究结果有广泛推广前景,可以为盐渍土的改良提供重要技术支持。

LAI Estimation Based on Multi-Spectral Remote Sensing of UAV and Its Application in Saline Soil Improvement

【Objective】Vegetation yield can comprehensively and intuitively reflect the improvement effect of saline soil. Leaf area index (LAI) of winter wheat during its vigorous growth period is a commonly used measure of vegetation yield. In this study, unmanned aerial vehicle (UAV) multispectral remote sensing was used to obtain the LAI distribution data of winter wheat during its vigorous growth period to objectively and accurately evaluate the improvement effect of saline soil, so as to provide scientific guidance for people to screen saline soil improvement technology and products. 【Method】Taking the experimental area of coastal saline soil improvement in Bohai granary in Wudi county as the research area, based on UAV multispectral remote sensing data, linear regression analysis, partial least squares, random forest, and support vector machine were used to construct LAI inversion model of winter wheat. The factor analysis method was used to evaluate the improvement effect of the sampled land in saline soil area, and the LAI evaluation model of saline soil improvement effect was established to evaluate the improvement effect of saline soil in the entire experimental area. 【Result】The results showed that, for the winter wheat LAI remote sensing estimation, it was not that the higher the resolution, the better, but the smoothed 5×5 mean spectral data corresponded best to the LAI of a ridge of wheat. Among the LAI remote sensing estimation models, the models were built by using SVM with the highest accuracy. The prediction result of the improvement effect LAI evaluation model showed that the prediction accuracy of the improvement effect of saline soil by LAI was higher, and the best improvement land numbers were 26, 27, 28, 29, 30, and 31, and the optimal improvement method was cited. The best improvement method was comprehensive improvement measures of diversion irrigation and adding organic fertilizer. 【Conclusion】UAV remote sensing could accurately invert the LAI of winter wheat at the jointing stage in saline soil area. The evaluation of the improvement effect of saline soil based on the results of LAI inversion could locate the optimal improvement effect from many experimental plots. Compared with the traditional method, this method had the advantages of low cost and high accuracy. The research results had a broad prospect and could provide important technical support for the improvement of saline soil.