基于分数阶微分光谱指数的冬小麦根域土壤含水率估算模型  

Estimation model of soil moisture content in root domain of winter wheat using a fractional-order differential spectral index

作　　者：刘浩 杨锡震 张蓓 黄嘉亮 赵笑[3] 吴雨箫 向友珍[1] 耿宏锁 陈皓锐[4] 陈俊英[1,2] LIU Hao;YANG Xizhen;ZHANG Bei;HUANG Jialiang;ZHAO Xiao;WU Yuxiao;XIANG Youzhen;GENG Hongsuo;CHEN Haorui;CHEN Junying(College of water Resources and Architectural Engineering,Northwest A&F University,Yangling 712100,China;Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas,Ministry of Education,A&F University,Yangling 712100,China;School of Quality Education,Northwest A&F University,Yangling 712100,China;Department of Water Resources,China Institute of Water Resources and Hydropower Research,Beijing 10037)

机构地区：[1]西北农林科技大学水利与建筑工程学院,杨凌712100 [2]西北农林科技大学旱区农业水土工程教育部重点实验室,杨凌712100 [3]西北农林科技大学综合素质教育学院,杨凌712100 [4]中国水利水电科学研究院流域水循环模拟与调控国家重点实验室,北京100037

出　　处：《农业工程学报》2023年第13期131-140,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(51979232,52179044)。

摘　　要：为探讨分数阶微分(fractional-order differentiation,FOD)技术联合光谱指数改善高光谱反演冬小麦根域土壤含水率(soil moisture content,SMC)的效果,该研究以冬小麦为研究对象,测取高光谱反射率和土壤含水率数据,将高光谱反射率经Savitzky-Golay(SG)平滑处理后计算典型光谱指数以此构建偏最小二乘回归(partial least squares regression,PLSR)、随机森林(random forest,RF)和BP神经网络(back propagation neural network,BPNN)3种土壤含水率反演模型;将高光谱反射率进行0~2.0阶(步长为0.2)的分数阶微分处理后计算比值指数(ratio index,RI)和归一化指数(normalized difference index,NDI),分析不同阶的RI、NDI与SMC之间的二维相关性,筛选得出敏感光谱指数并分组,以此构建3种反演模型(PLSR、RF和BPNN)。结果表明:不同典型光谱指数与土壤含水率的相关性存在很大差异,相关系数波动范围在0.2~0.6之间,基于典型光谱指数的土壤含水率反演模型效果最好的是PLSR模型,RF模型次之,BPNN模型最低;经分数阶微分处理后筛选的敏感光谱指数与SMC之间的相关性较高,相关系数在不同分数阶上呈阶梯状变化,敏感光谱指数与SMC的相关系数从0.76(0.2~1.0阶)递减至0.65(1.6~2.0阶);最优SMC反演模型为FOD处理后的归一化敏感指数建立的RF模型,所建模型的决定系数为0.75,均方根误差为0.024 g/g,相对分析误差为2.08。基于分数阶微分改进的光谱指数反演土壤含水率模型较典型光谱指数反演模型效果提升明显(决定系数提升136%),研究成果可为冬小麦根域土壤含水率高光谱监测提供了一种可靠途径。Soil moisture content(SMC)is one of the most important conditions to affect crop growth and development in the irrigation of agricultural production.Monitoring SMC of crop roots is much more conducive to guiding precise crop irrigation.This study aims to investigate the effect of fractional-order differentiation(FOD)combined with the spectral index on the hyperspectral inversion of SMC in the winter wheat root domain.The winter wheat was selected as the research object in the Water-saving Irrigation Experimental Station.The data was also measured for the hyperspectral reflectance and soil water content.The hyperspectral reflectance was smoothed by Savitzky-Golay(SG).Then the typical spectral index was calculated to construct the partial least squares regression(PLSR),random forest(RF),and back propagation neural network(BPNN)inversion models of soil water content.The hyperspectral reflectance was processed by fractional differentiation of 0-2.0 order(step of 0.2),in order to calculate the ratio index(RI)and normalized difference index(NDI).A systematic analysis was made to determine the two-dimensional correlation between the RI of different orders,the normalized index and SMC.The sensitive spectral index was screened and grouped to construct three inversion models(PLSR,RF,and BPNN).The results show that:There was a very different correlation between different typical spectral indices and soil moisture content.The correlation coefficient fluctuated between 0.2 and 0.6,the highest of which with the SMC was the near-infrared spectral RI(about−0.6).The best effect was achieved in the SMC inversion model using the typical spectral index,followed by RF and BPNN models.The coefficient of determination of the improved model was 0.55,the root mean square error(RMSE)was 0.027 g/g,and the relative analysis error was 1.64.There was a high correlation coefficient between the sensitive spectral index and SMC after the fractional-order differential processing.The correlation coefficient between the sensitive spectral index a

关 键 词：遥感 模型 分数阶微分 土壤含水率 冬小麦 光谱指数 

分 类 号：S15[农业科学—土壤学]