Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Estimating soil moisture using Temperature–Vegetation Dryness Index (TVDI) in the Huang-huai-hai (HHH) plain

Estimating soil moisture using Temperature–Vegetation Dryness Index (TVDI) in the Huang-huai-hai... Soil moisture is an important indicator to describe soil conditions, and can also provide information on crop water stress and yield estimation. The combination of vegetation index (VI) and land surface temperature (LST) can provide useful information on estimation soil moisture status at regional scale. In this paper, the Huang-huai-hai (HHH) plain, an important food production area in China was selected as the study area. The potential of Temperature–Vegetation Dryness Index (TVDI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data in assessing soil moisture was investigated in this region. The 16-day composite MODIS Vegetation Index product (MOD13A2) and 8-day composite MODIS temperature product (MOD11A2) were used to calculate the TVDI. Correlation and regression analysis was carried out to relate the TVDI against in-situ soil moisture measurements data during the main growth stages of winter wheat/summer maize. The results show that a significantly negative relationship exists between the TVDI and in-situ measurements at different soil depths, but the relationship at 10–20 cm depth (R 2 = 0.43) is the closest. The spatial and temporal patterns in the TVDI were also analysed. The temporal evolution of the retrieved soil moisture was consistent with crop phenological development, and the spatial distribution of retrieved soil moisture accorded with the distribution of precipitation during the whole crop growing seasons. The TVDI index was shown to be feasible for monitoring the surface soil moisture dynamically during the crop growing seasons in the HHH plain. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Remote Sensing Taylor & Francis

Estimating soil moisture using Temperature–Vegetation Dryness Index (TVDI) in the Huang-huai-hai (HHH) plain

13 pages

Loading next page...
 
/lp/taylor-francis/estimating-soil-moisture-using-temperature-vegetation-dryness-index-Kg0ak3k9kQ

References (24)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1366-5901
DOI
10.1080/01431160903527421
Publisher site
See Article on Publisher Site

Abstract

Soil moisture is an important indicator to describe soil conditions, and can also provide information on crop water stress and yield estimation. The combination of vegetation index (VI) and land surface temperature (LST) can provide useful information on estimation soil moisture status at regional scale. In this paper, the Huang-huai-hai (HHH) plain, an important food production area in China was selected as the study area. The potential of Temperature–Vegetation Dryness Index (TVDI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data in assessing soil moisture was investigated in this region. The 16-day composite MODIS Vegetation Index product (MOD13A2) and 8-day composite MODIS temperature product (MOD11A2) were used to calculate the TVDI. Correlation and regression analysis was carried out to relate the TVDI against in-situ soil moisture measurements data during the main growth stages of winter wheat/summer maize. The results show that a significantly negative relationship exists between the TVDI and in-situ measurements at different soil depths, but the relationship at 10–20 cm depth (R 2 = 0.43) is the closest. The spatial and temporal patterns in the TVDI were also analysed. The temporal evolution of the retrieved soil moisture was consistent with crop phenological development, and the spatial distribution of retrieved soil moisture accorded with the distribution of precipitation during the whole crop growing seasons. The TVDI index was shown to be feasible for monitoring the surface soil moisture dynamically during the crop growing seasons in the HHH plain.

Journal

International Journal of Remote SensingTaylor & Francis

Published: Feb 24, 2011

There are no references for this article.