ZHAO J, LIU J, HU F P, et al.Study on soil moisture monitoring method based on Spatiotemporal Image-fusion Model and TVDI[J]. Journal of Henan Polytechnic University(Natural Science) , 2025, 44(3): 112-118.

doi: 10.16186/j.cnki.1673-9787.2023120040

Received:2023/12/14

Revised:2024/02/23

Online:2025-04-18

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

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 limitations of using single-source remote sensing data （e.g.， MODIS or Landsat TIRS/TIRS-2） in monitoring soil moisture through the Temperature Vegetation Dryness Index （TVDI）， which often leads to insufficient spatiotemporal resolution，  Methods a soil moisture monitoring approach was proposed by integrating the spatiotemporal image-fusion model （STI-FM） with TVDI. This study evaluated the monitoring performance of this method and assessed the applicability of STI-FM in fusing multi-source remote sensing land surface temperature （LST） data within the study area.  Results （1）The STI-FM model demonstrates superior performance in fusing MODIS and Landsat TIRS/TIRS-2 land surface temperature （LST） data， achieving strong correlation （R²=0.89） and low error （RMSE=2.85 K） between the predicted and satellite-observed LST values， confirming its applicability in the study area. （2） Compared to traditional TVDI， the modified TVDI combining STI-FM-enhanced LST data and optimized vegetation indices significantly improved soil moisture monitoring accuracy. The correlation coefficients between TVDI and soil moisture increased by 0.07 （June） and 0.14 （July） compared to original NDVI-LST feature space calculations. （3） The improved TVDI achieved higher accuracy than ERA5-Land reanalysis soil moisture data. Correlation coefficients between TVDI and soil moisture surpassed ERA5-Land by 0.10 （June） and 0.44 （July）.  Conclusions The STI-FM-enhanced TVDI framework significantly improves soil moisture monitoring by simultaneously enhancing estimation accuracy and providing higher spatial resolution compared to conventional methods， offering a novel paradigm for TVDI-based moisture retrieval.

Key words: soil moisture; TVDI; spatiotemporal image-fusion model; spatio-temporal fusion; applicability