|
| 滨海盐渍区土壤盐分遥感反演及动态监测 |
| Remote sensing inversion and dynamic monitoring of soil salt in coastal saline area |
| 投稿时间：2018-01-13 |
| DOI：10.13254/j.jare.2018.0016 |
| 中文关键词: 滨海盐渍土，土壤盐分，遥感反演，动态变化 |
| 英文关键词: coastal saline soil, soil salinity, remote sensing inversion, dynamic change |
| 基金项目:"十二五"国家科技支撑计划项目课题（2015BAD23B0202，2013BAD05B06-5）；国家自然科学基金项目（41271235）；"双一流"奖补资金项目（SYL2017XTTD02） |
| 作者 | 单位 | E-mail | | 张素铭 | 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 | | | 赵庚星 | 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 | zhaogx@sdau.edu.cn | | 王卓然 | 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 | | | 肖杨 | 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 | | | 郎坤 | 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 | |
|
| 摘要点击次数: 2325 |
| 全文下载次数: 2196 |
| 中文摘要: |
| 为探索快速提取滨海盐渍土信息的有效方法，实现对滨海盐渍区土壤盐分含量变化趋势的分析，本研究以黄河三角洲垦利区作为研究区，采用野外实测土壤盐分数据与遥感影像相结合的方法，通过土壤盐分敏感波段和光谱参量的筛选，构建土壤盐分估测模型；优选出最佳模型用于反演，并结合土壤盐分含量指数、土壤盐分动态度和重心向量模型3个指标对研究区土壤盐分含量动态变化进行统计和分析。结果显示：土壤盐分的敏感波段为绿光、红光和近红外波段；波段组合可以提高其与盐分的相关性，运用敏感波段与波段组合相结合的方法建模更优；土壤盐分最佳估测模型为：Y=-6.94-281.762Bnir+60.625Bg×Br+1 178.14Bg×Bnir-152.396Br×Bnir-1 495.491Bg×Br×Bnir，建模精度和验证精度分别为0.878和0.854，说明模型拟合度好，预测能力强，具有可行性。2001-2005年、2005-2009年和2009-2015年3个时段，研究区土壤盐渍化程度的变化趋势表现为加重-减轻-减轻趋势，盐化重心总体向东部沿海方向迁移，各时段土壤盐渍化变化程度较高。本研究提出了利用卫星遥感影像对土壤盐分含量进行预测和动态监测的快捷方法，对滨海盐渍土地资源的利用管理有积极意义。 |
| 英文摘要: |
| In order to explore an effective method for extracting coastal saline soil information rapidly to analyze the change trend of soil salt content in coastal saline soil area, this study took the Kenli District of the Yellow River Delta as the research area, combined the field measured data of soil salinity with remote sensing images. The soil salinity estimation model was constructed by soil salt sensitive bands and spectral parameters which were screened. Then the best estimation model was selected for inversion, and combined with soil salinity index, soil salinity dynamic degree and barycenter vector model, to count and analyze the dynamic changes of soil salinity in the study area. The results showed that the sensitive bands of soil salinity were green, red and near infrared, combination of bands could improve its correlation with soil salinity, and it was better to incorporate sensitive band with band combination. The best estimation model for soil salinity with fitting accuracy of 0.878 and verification precision of 0.854, which indicated that the model had good fitting and great forecast ability. From the three periods of 2001 to 2005, 2005 to 2009 and 2009 to 2015, the degree of soil salinization in the study area was in trend of aggravation, reduction and reduction. The salinization center was generally migrated to the east coast, and soil salinization had a higher degree of change in each period. This study proposes a quick way to predict and monitor the soil salt content using satellite remote sensing images, which is of positive significance for the utilization and management of coastal saline soil resources. |
| HTML
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
