Abstract
Abstract. Soil moisture is one of key environmental variables that affect vegetation cover and energy exchange between the land surface and the atmosphere. Satellite remote sensing technology can provide information for monitoring large-scale soil moisture dynamics quickly. The temperature vegetation dryness index (TVDI) acts as an effective indicator of inferring soil moisture status which is calculated according to the empirical parameterization of composed of the land surface temperature (LST) and the normalized difference vegetation index (NDVI) characteristic space. In this paper, the MODIS TVDI was calculated based on MODIS LST product (MOD11A2, 1 km) and NDVI data (derived from MOD09A1, 500m). Meanwhile, LST and NDVI from Landsat8 OLI images were estimated to obtain Landsat-based TVDI. Then, a Kalman filter algorithm was used to simulate TVDI time series data with 30m resolution and a revisit period of 8 days combining TVDI derived from Landsat and MODIS data. We selected the west of the Songnen Plain, China as the test area and high quality cloudy-free images during growing season (April to October) of 2018 as the input data. The predicted TVDI time series data of medium resolution not only improved the temporal resolution to capture the changes at fine scale within a short period, but also made up for the deficiency of low spatial resolution MODIS data. The results show that it is feasible to generate medium or high resolution TVDI time series data by applying different remote sensing data by Kalman filtering algorithm.
Highlights
1.1 General InstructionsSoil moisture is one of the important indicators for monitoring land conditions and a decisive factor for crop growth
The scatter plot on April 26 was missing because the image of Landsat temperature vegetation dryness index (TVDI) was acted as the original image for Kalman filter algorithm, so there was a large difference between the synthetic TVDI and the Landsat observations
We adopted a the Kalman filter algorithm to simulate TVDI time series data with 30m resolution and a revisit period of 8 days combining TVDI derived from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data
Summary
Soil moisture is one of the important indicators for monitoring land conditions and a decisive factor for crop growth. Temperature Vegetation Drought Index (TVDI) is an empirical parameterized calculation based on the feature space composed by land surface temperature (LST) and normalized difference vegetation index (NDVI) It integrates the information of vegetation index and land surface temperature, and is regarded as an effective indicator to infer soil moisture status. TVDI data can be derived from many sensors including Moderate Resolution Imaging Spectroradiometer (MODIS), SPOT VEGETATION and Advanced Very High Resolution Radiometer (AVHRR) etc These medium and low spatial resolution data (>300m) can only be used for large scale drought conditions monitoring (Cao, 2019; Garcia, 2014; Qi, 2003; Wang, 2014; Wu, 2007; Wu. 2018), but may not suitable for the areas at fine scale or with high heterogeneity. The Kalman filter (KF) algorithm is adopted to simulate TVDI data based on Landsat OLI images and MODIS products with a resolution of 30m at an 8-day time intervals. KF method is applied to TVDI data for the first time in this study, which can provide reference for the research and application of high spatio-temporal resolution surface parameter simulation method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
