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植被生长态势的监测对城市绿化和社会生态文明建设有重要的作用，植被水分的缺失会影响植被的正常生长发育。传统的高光谱反演方法尽管可以反演出植被冠层含水量，但因为实际问题难以应用监测城市植被。以普通可见光图像为基础，融合HSCNN神经网络和PCA （主成分提取）方法，利用最小二乘法和ElasticNet2种回归方法构建植被冠层含水量反演模型。通过实验验证2种模型均可在可见光范围内反演植被含水量，其中ElasticNet方法的模型准确率更高。
Abstract：The monitoring of vegetation growth situation plays an important role in urban greening and social ecological civilization construction. The lack of vegetation water will affect normal vegetation growth and development. Although the traditional hyperspectral inversion method can invert the vegetation canopy water content, it is difficult to monitor urban vegetation because of practical problems. Based on ordinary visible light images, HSCNN neural network and PCA(principal component extraction) methods were combined, and the least square method and ElasticNet two regression methods were used to construct the vegetation canopy water content inversion model. The experiment results show that both models can invert the vegetation water content in the visible light range, and the ElasticNet method has a higher model accuracy.
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基本信息:
中图分类号:TP391.41;Q948
引用信息:
[1]李雪鹏,许增,杨昱,等.基于无人机可见光谱的城市植被含水量反演[J].地理空间信息,2022,20(06):12-16.
基金信息:
国家自然科学基金资助项目（61976150）; 山西省晋中市科技重点研发计划（Y19No.NGII20181206）; 山西省晋中市科技重点研发计划项目（Y19No.NGII20181206）; 赛尔网络下一代互联网技术创新项目（NGII20181206）
2022-06-28
2022-06-28