scholarly journals Seasonal Evaluation of Soil Moisture Changes in Isfahan Province Using Temperature Vegetation Dryness Index (TVDI)

2019 ◽  
Vol 23 (4) ◽  
Keyword(s):  
Soil Moisture ◽  
Isfahan Province ◽  
Dryness Index

scholarly journals DROUGHT MONITORING IN SHANDONG PROVINCE BASED ON MODIFIED TEMPERATURE-VEGETATION-SOIL MOISTURE DRYNESS INDEX (TVMDI)

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 911-916
Author(s):  
H. Wan ◽  
Z. D. Wang ◽  
P. Guo ◽  
B. Wang ◽  
X. C. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Land Surface ◽  
Vegetation Index ◽  
Shandong Province ◽  
Severe Drought ◽  
Average Correlation ◽  
Shandong Peninsula ◽  
Wide Range ◽  
Dryness Index

Abstract. Drought is one of the frequent natural disasters in Shandong province, which is characterized by high frequency and wide range. In response to frequent droughts that are not monitored in time, monitoring the changes of drought is of great significance to agricultural production and social development. This study used the Temperature-Vegetation-soil Moisture Dryness Index (TVMDI) model, combined with the optical MODIS land surface temperature, vegetation index, surface albedo data and microwave FY-3B soil moisture data, to monitor the drought of Shandong province in 2016. The precipitation and temperature data of weather station were used to validate the monitoring results. The results show that, in 2016, the drought in Shandong province mainly occurred in winter and spring, and the drought in summer was alleviated. From the perspective of space, the northern Shandong and the Shandong peninsula areas are relatively humid with less drought time, while the local areas in the central and southern Shandong province suffer from severe drought with longer drought time. From the perspective of correlation with meteorological factors, the average correlation coefficient between TVMDI and precipitation can reach 0.45, and the average correlation coefficient between TVMDI and temperature can reach 0.63.

scholarly journals Spatio-Temporal Assessment of Surface Moisture and Evapotranspiration Variability Using Remote Sensing Techniques

2021 ◽  
Vol 13 (9) ◽  
pp. 1667
Author(s):  
Mai Son Le ◽  
Yuei-An Liou
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Meteorological Data ◽  
Growing Season ◽  
Heat Index ◽  
Landsat 8 ◽  
Surface Moisture ◽  
Dryness Index ◽  
Spatio Temporal ◽  
Remote Sensing Techniques

The relationship between the physic features of the Earth’s surface and its temperature has been significantly investigated for further soil moisture assessment. In this study, the spatiotemporal impacts of surface properties on land surface temperature (LST) were examined by using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and meteorological data. The significant distinctions were observed during a crop growing season through the contrast in the correlation between different multi-spectral satellite indices and LST, in which the highest correlation of −0.65 was found when the Normalized Difference Latent heat Index (NDLI) was used. A new index, named as Temperature-soil Moisture Dryness Index (TMDI), is accordingly proposed to assess surface moisture and evapotranspiration (ET) variability. It is based on a triangle space where NDLI is set as a reference basis for examining surface water availability and the variation of LST is an indicator as a consequence of the cooling effect by ET. TMDI was evaluated against ET derived from the commonly-used model, namely Surface Energy Balance Algorithm for Land (SEBAL), as well as compared to the performance of Temperature Vegetation Dryness Index (TVDI). This study was conducted over five-time points for the 2014 winter crop growing season in southern Taiwan. Results indicated that TMDI exhibits significant sensitivity to surface moisture fluctuation by showing a strong correlation with SEBAL-derived ET with the highest correlation of −0.89 was found on 19 October. Moreover, TMDI revealed its superiority over TVDI in the response to a rapidly changing surface moisture due to water supply before the investigated time. It is suggested that TMDI is a proper and sensitive indicator to characterize the surface moisture and ET rate. Further exploitation of the usefulness of the TMDI in a variety of applications would be interesting.

Evaluating soil moisture status in China using the temperature–vegetation dryness index (TVDI)

2004 ◽  
Vol 30 (5) ◽  
pp. 671-679 ◽  
Author(s):  
Changyao Wang ◽  
Shuhua Qi ◽  
Zheng Niu ◽  
Junbang Wang
Keyword(s):  
Soil Moisture ◽  
Dryness Index ◽  
Soil Moisture Status

Assessing Grassland Moisture and Biomass in Tasmania - the Application of Remote-Sensing and Empirical-Models for a Cloudy Environment

1995 ◽  
Vol 5 (3) ◽  
pp. 165 ◽  
Author(s):  
MA Chladil ◽  
M Nunez
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Fire Season ◽  
Fuel Moisture ◽  
Noaa Avhrr ◽  
Dryness Index ◽  
Ground Truthing ◽  
Soil Dryness ◽  
Fuel Moisture Content ◽  
Semi Empirical

The operational feasibility of NOAA/AVHRR data and two semi-empirical moisture models were evaluated in the grasslands of southeastern Tasmania (Australia) during the 1988/89 fire season. A limited ground-truthing experiment compared the grassland dry biomass, soil moisture and fuel moisture with the satellite derived NDVI and the Soil Dryness Index (SDI) and the Grassland Curing Index (GCI). The NDVI gave good results for fuel moisture content (FMC) and soil moisture content (SMC) but unreliable image availability precludes the use of NDVI as a stand alone system for fire managers. The SDI and GCI also performed well in predicting SMC and FMC. Very good results were obtained when the NDVI and the GCI were combined. These results suggest the combination of data will provide both the accuracy and the continuity of information needed for operational use by fire managers. The methods used here could be cheaply and quickly repeated for use in other similar fire prone and cloudy environments.

Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)

2017 ◽  
Vol 197 ◽  
pp. 1-14 ◽  
Author(s):  
Meisam Amani ◽  
Bahram Salehi ◽  
Sahel Mahdavi ◽  
Ali Masjedi ◽  
Sahar Dehnavi
Keyword(s):  
Soil Moisture ◽  
Dryness Index
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