Abstract

Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results (R¯) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum R¯ of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 104 km2 and 667.44 × 104 km2 with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.

Highlights

  • Accurate and timely soil moisture (SM) information has essential applications in different fields, such as flood/drought forecasting, climate and weather modeling, water resources, and agriculture management [1]

  • This study aimed to improve the relative soil moisture (RSM) estimation that is based on the apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI)

  • By optimizing the identification of normalized difference vegetation index (NDVI) thresholds, Criterion 2 (NDVIATI

Read more

Summary

Introduction

Accurate and timely soil moisture (SM) information has essential applications in different fields, such as flood/drought forecasting, climate and weather modeling, water resources, and agriculture management [1]. Proper water resource management is crucial in declining vulnerability to drought and other extreme events that may occur with increasing frequency because of climate change. This has been widely recognized in the arid and Remote Sens. In order to explore the potential of optical and thermal remote sensing imagery for SM estimation, different indices

Objectives
Methods
Conclusion
Full Text
Published version (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