基于近地面多光谱的黄河三角洲典型地区土壤含盐量估算研究

选择山东省无棣县“渤海粮仓”项目核心示范区为研究区，利用ADC便携式多光谱相机和EC110便携式盐分计，采集该区近地多光谱相片和土壤表层含盐量数据，通过NDVI, SAVI, GNDVI三种植被指数分别与实测土壤含盐量构建线性、指数、对数、乘幂、二次和三次函数共18种模型，进而优选土壤盐分含量最佳估测模型，反演和分析研究区土壤盐分状况。结果显示，各模型均可有效估测土壤盐分含量，以SAVI为因变量构建的各模型估测效果较好，其中以SAVI的线性模型(Y=-0.524x+0.663，n=70)为最佳，显著检验水平下的F检验值最高，为141.347，估测R2为0.797，精度达到93.36%;研究区的土壤盐分含量集中在2.5‰～3.5‰之间，呈现从西南向东北逐渐升高的明显分布规律。探索了基于近地面多光谱数据的土壤含盐量估测方法，为研究区乃至整个黄河三角洲滨海盐碱土的盐分含量估测提供了一种快速有效的技术方法。

Soil Salinity Estimation Based on Near-Ground Multispectral Imagery in Typical Area of the Yellow River Delta

This study chooses the core demonstration area of ‘Bohai Barn’ project as the study area ,which is located in Wudi , Shandong Province .We first collected near‐ground and multispectral images and surface soil salinity data using ADC portable multispectral camera and EC110 portable salinometer .Then three vegetation indices ,namely NDVI ,SAVI and GNDVI ,were used to build 18 models respectively with the actual measured soil salinity .These models include linear function ,exponential function ,logarithmic function ,exponentiation function ,quadratic function and cubic function ,from which the best estimation model for soil salinity estimation was selected and used for inverting and analyzing soil salinity status of the study area .Results indicated that all models mentioned above could effectively estimate soil salinity and models using SAVI as the dependent variable were more effective than the others .Among SAVI models ,the linear model(Y= -0.524x+0.663 ,n=70) is the best ,under which the test value of F is the highest as 141.347 at significance test level ,estimated R2 0.797 with a 93.36% accuracy .Soil salinity of the study area is mainly around 2.5‰ ~3.5‰ ,which gradually increases from southwest to northeast .This study has probed into soil salinity estimation methods based on near‐ground and multispectral data ,and will provide a quick and effective technical soil salinity estimation approach for coastal saline soil of the study area and the whole Yellow River Delta .