摘要
【目的】快速准确监测大面积区域土壤水分,提高遥感监测土壤水分的效率。【方法】采用Landsat 8 OLI影像构建了地表温度(Ts)-植被指数(NDVI)特征空间,拟合了特征空间的干湿边方程,并根据干湿边方程计算的温度植被干旱指数(TVDI)与同期野外不同深度的实测土壤含水率进行了回归分析与验证。【结果】遥感影像反演所得的TVDI与野外实测土壤湿度显著相关(α=0.05);0~10、10~20、20~30 cm土层中,TVDI与10~20 cm土层土壤湿度相关性最高(r=0.79);遥感影像反演的土壤湿度时空分布变化特征与作物分布生长情况以及气候变化规律基本吻合。【结论】根据温度植被干旱指数法反演监测区域土壤湿度是切实可行的,尤以10~20 cm土层土壤湿度的反演监测最为精准与可靠。
【Objective】Remote sensing has been increasingly used to monitor soil moisture at large scales and the purpose of this paper is to improve its accuracy and reliability for vegetative lands.【Method】The Landsat 8 OLI imagery was used to construct the surface temperature（Ts）-vegetation index（NDVI） feature space, and the TemperatureVegetation Drought Index（TVDI） was calculated based on the wet and dry equation gotten from the TsNDVI space. The regressive models were verified against the soil moisture measured concurrently at different depths in a field.【Result】The TVDI obtained retrievably from the remote sensing was significantly correlated with the measured soil moisture with a=0.05. For three soil layers of 0-10 cm, 10-20 cm, 20-30 cm, the TVDI had the highest correlation with the soil moisture in 10-20 cm of soil with r=0.79. The spatiotemporal distribution of the soil moisture retrievably calculated from the remote sensing imagery was consistent with the distribution of crop and the climate change.【Conclusion】It is feasible to monitor the soil moisture using the temperature and vegetation drought index, especially for soil moisture in 10-20 cm of soil.
作者
高培霞
张吴平
梁爽
毕如田
王国芳
GAO Peixia;ZHANG Wuping;LIANG Shuang;BI Rutian;WANG Guofang(College of Resources and Environment,Shanxi Agricultural University,Taigu 030801,China)
出处
《灌溉排水学报》
CSCD
北大核心
2018年第10期123-128,共6页
Journal of Irrigation and Drainage
基金
山西省重点研发计划重点项目(201703D211002-2)
山西省科技攻关项目(20130311008-5)
