赵军, 刘坚, 胡飞鹏, 等. 基于时空融合模型和TVDI的土壤水分监测方法研究[J].河南理工大学学报(自然科学版), doi: 10.16186/j.cnki.1673-9787.2023120040.

ZHAO J, LIU J, HU F P, et al. Research on soil moisture monitoring method based on Spatiotemporal Image-fusion Model and TVDI[J]. Journal of Henan Polytechnic University(Natural Science), doi:10.16186/j.cnki.1673- 9787. 2023120040.

基于时空融合模型和TVDI的土壤水分监测方法研究

赵军，刘坚，胡飞鹏，托瑞，孙紫云

（西北师范大学 地理与环境科学学院，甘肃 兰州 730070）

摘要：目的 针对以往温度植被干旱指数（temperature vegetation dryness index，TVDI）在监测土壤水分中多采用 MODIS 或 Landsat TIRS/TIRS-2 等单一遥感数据时，在时空分辨率方面存在的数据缺陷问题，提出了一种基于多源遥感数据时空融合模型和 TVDI 的土壤水分监测方法。方法 基于时空融合模型（spatiotemporal image-fusion model，STI-FM）和 TVDI，对 STI-FM 模型融合多源遥感地表温度在实验区的适用性，以及基于时空融合模型和 TVDI 的土壤水分的监测效果进行了研究。结果 结果表明：（1）通过 STI-FM 模型融合 MODIS 和 Landsat TIRS/TIRS-2 的地表温度数据质量较好，运用 STI-FM 模型推测得到的地表温度数据与遥感监测的地表温度数据做对比分析，发现两者之间具有较强的相关性，即 STI-FM 模型适用于本文实验区。（2）利用 STI-FM 模型改进地表温度数据并结合优选植被指数的 TVDI 与传统的 TVDI 相比，对于实验区土壤水分的监测精度有较显著的提高。（3）与再分析土壤水分资料 ERA5-Land 土壤水分数据相比，改进后的 TVDI监测土壤水分的能力有明显的优势。结论 综上所述，基于 STI-FM 模型的 TVDI可以提高土壤水分监测效果，为提高 TVDI 土壤水分监测能力提供了一种新思路。

关键词：土壤水分；TVDI；STI-FM；时空融合；适用性

中图分类号：S152.7；TP79　　

doi: 10.16186/j.cnki.1673-9787. 2023120040

基金项目: 国家自然科学基金资助项目（42161072）

收稿日期：2023-12-14

修回日期：2024-02-23

网络首发日期：2024-08-07

Research on soil moisture monitoring method based on Spatiotemporal Image-fusion Model and

TVDI（Online）

ZHAO Jun，LIU Jian，HU Feipeng，TUO Rui，SUN Ziyun

（College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070，Gansu，China）

Abstract: Objectives To address the data limitations of Temperature Vegetation Dryness Index (TVDI) monitoring using single remote sensing data, a soil moisture monitoring method was proposed. It used a spatiotemporal fusion model and TVDI with multiple satellite data sources, such as MODIS and Landsat TIRS/TIRS-2. Methods The study investigated the applicability of the spatiotemporal fusion model (STI-FM) in fusing the MODIS and Landsat TIRS/TIRS-2 surface temperature data. It also investigated the effectiveness of using the STI-FM model and TVDI for soil moisture monitoring. Results The results showed that: (1) The surface temperature data from MODIS and Landsat TIRS/TIRS-2 was successfully fused by the STI-FM model. The estimated temperature data from the STI-FM model and the remotely sensed temperature data had a good correlation. This indicated the suitability of the STI-FM model for the study area. (2) The surface temperature data was improved by using the STI-FM model. It was combined with an optimized vegetation index-based TVDI. A significant improvement in the accuracy of soil moisture monitoring in the study area was achieved. It was compared to the traditional TVDI. (3) The improved TVDI had a clear advantage in soil moisture monitoring.It was compared to the reanalysis of soil moisture data (ERA5-Land). Conclusions The TVDI based on the STI-FM model can enhance the effectiveness of soil moisture monitoring. It provides a new approach to improve the capability of TVDI in monitoring soil moisture.

Key words：soil moisture; TVDI; STI-FM; spatio-temporal fusion; applicability

CLC: S152.7；TP79