自然资源学报 ›› 2016, Vol. 31 ›› Issue (6): 1051-1060.doi: 10.11849/zrzyxb.20150669

• 资源研究方法 • 上一篇    下一篇

基于近地多光谱和OLI影像的黄河三角洲冬小麦种植区盐分估算及遥感反演——以山东省垦利县和无棣县为例

张同瑞, 赵庚星*, 高明秀, 常春艳, 王卓然   

  1. 山东农业大学资源与环境学院,土肥资源高效利用国家工程实验室,山东 泰安 271018
  • 收稿日期:2015-06-15 出版日期:2016-06-20 发布日期:2016-06-20
  • 通讯作者: E-mail: zhaogx@sdau.edu.cn
  • 作者简介:第一作者简介:张同瑞(1989- ),男,硕士研究生,主要研究方向为土地资源与遥感。E-mail: zhangtr0924@163.com
  • 基金资助:
    “十二五”国家科技支撑计划项目课题(2013BAD05B06,2015BAD23B0202);国家自然科学基金(41271235)。

Soil Salinity Estimation and Remote Sensing Inversion Based on Near-ground Multispectral and TM Imagery in Winter Wheat Growing Area in the Yellow River Delta—Case Study in Kenli County and Wudi County, Shandong Province

ZHANG Tong-rui, ZHAO Geng-xing, GAO Ming-xiu, CHANG Chun-yan, WANG Zhuo-ran   

  1. College of Resources and Environment, National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Taian 271018, China
  • Received:2015-06-15 Online:2016-06-20 Published:2016-06-20
  • Supported by:
    “Twelfth Five Year”National Science and Technology Support Project, No. 2013BAD05B06 and 2015BAD23B0202;National Natural Science Foundation of China, No. 41271235.

摘要: 黄河三角洲是我国重要的后备土地资源区,而土壤盐渍化对该区的农业生产影响巨大,因此,及时获取该区农作物种植区土壤盐分含量及其分布具有重要意义。论文利用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,结合两期遥感影像提取黄河三角洲冬小麦种植区面积分布,构建基于近地多光谱植被指数的土壤含盐量估算模型,进而将模型拟合反演到黄河三角洲OLI影像,得到黄河三角洲冬小麦种植区土壤盐分含量空间分布图,对麦区土壤盐分状况进行了分析。结果显示,土壤含盐量最佳估算模型为以SAVI为因变量的线性模型(Y=-0.754x+0.870,n=80),估测R2为0.747,精度达到81.44%;研究区冬小麦分布由西南部内陆至东北部沿海呈明显递减的空间趋势;麦田土壤盐分含量主要集中在1.5~3.0 g/kg之间,占种植总面积的76.90%,而含盐大于3.0 g/kg的麦田占14.09%,宜采取针对性栽培管理措施。该研究探索了基于近地多光谱数据和OLI影像的土壤含盐量估算方法,为黄河三角洲麦田管理和土壤盐分含量估算提供了快速有效的技术手段。

关键词: ADC多光谱相机, OLI影像, 黄河三角洲, 土壤含盐量, 遥感反演

Abstract: The Yellow River Delta is an important area of reserve land. Because of the great effects of the soil salinization on the agricultural production in this area, momentarily acquiring the soil salt content and its distribution in the region is of great significance. We first collected near ground and multispectral images and surface soil salinity data using ADC portable multispectral camera and EC110 portable salinometer, and extracted the growing areas of winter wheat in the Yellow River Delta with two phases of remote sensing images. We built a soil salinity estimation model based on the vegetation index from near earth multispectral images, and then, the fitted the model to the OLI image of the Yellow River Delta to obtain the spatial distribution of soil salinity in winter wheat growing areas in the target region. The soil salinity in the winter wheat growing areas was analyzed. Results indicated that the best model of estimating salt content of soil was the linear model of SAVI (Y=-0.754x+0.870, n=80), estimated R2 being 0.747 and the accuracy being 81.44%; the winter wheat planed in the study area decreased from the southwest inland to the northeast coast; 76.90% of the total cultivated area has the soil salinity ranging between 1.5-3.0 g/kg, and 14.09% of the total cultivated area has the soil salinity more than 3.0 g/kg. This study has probed into soil salinity estimation methods based on the near earth multispectral data and OLI images, which provides a quick and effective approach for crop management and soil salinity estimation in the Yellow River Delta.

Key words: ADC portable multispectral camera, OLI images, remote sensing inversion, soil salinity, the Yellow River Delta

中图分类号: 

  • TP79