Development and evaluation of the MTVDI for soil moisture monitoring

Zhu, Wenxiang; Lv, Aifeng; Sun, Ling

doi:10.1002/2017jd026607

Cited by 22 publications

(3 citation statements)

References 80 publications

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“…Surface moisture plays a significant role in land-surface processes by modulating the exchanges of water and energy between land and atmosphere [1][2][3][4]. Understanding surface moisture and evapotranspiration (ET) can provide valuable information for a variety of subsequent applications, such as hydro-meteorological predictions, drought monitoring, and agriculture management [5,6].…”

Section: Introductionmentioning

confidence: 99%

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

Liou

2021

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

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

Liou

2021

Remote Sensing

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“…Several assumptions and limitations need to be considered when applying the TVDI: (1) a given spatial domain is needed to present SM and vegetation cover conditions, and the dimension of the mapping area should be large enough to ensure that the scatterplots of LST versus VI can adequately form a regular triangular or trapezoid shape including completely wet edges and extremely dry edges; (2) in the spatial domain, surface properties and atmospheric conditions are relatively homogeneous and the variability of SM simply depends on variations of LST; (3) land cover types and topography should be fully considered to reduce the uncertainty of TVDI in SM retrieval. Previous studies have proposed many calibrated or improved TVDI methods according to these assumptions and limitations, which are classified into three categories: (1) a temperature correction model using surface-air temperature differences based on regional DEM data [26][27][28][29], day-night temperature differences [30] to effectively eliminate the impact of solar radiation and atmospheric background reflectance; (2) a dry edge correction method [31] or bi-parabolic LST-NDVI space method [32,33] to reshape the fitting equations of the dry edge or wet edge; (3) a time domain solution considering the maximum surface temperature of bare soil, which is a special case of TVDI [7,34]. These improvements have considerable scientific significances and provide promising approaches with potential application for SM estimation and drought monitoring.…”

Section: Introductionmentioning

confidence: 99%

A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

et al. 2020

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The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that of the TVDI. Compared with the MPDI and TVDI for soil moisture monitoring at crop sites, the CVDI exhibited a performance superior to the MPDI and TVDI in most days where the coefficients of determination (R2) achieved can reach to 0.67 on DOY023, 137, 274 and 0.71 on DOY 322 and reproduced more accurate spatial and seasonal variation of soil moisture. Moreover, the CVDI showed higher correlation with the Australian Water Resource Assessment Landscape (AWRA-L) soil moisture product on temporal scales. The R2 can reach to 0.69 and the root mean square error (RMSE) is also much better than that of the MPDI and TVDI. Overall, it can be concluded that the CVDI appears to be a feasible method and can be successfully used in regional soil moisture monitoring.

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“…Nevertheless, TVDI is usually used in comparatively small regions. Therefore, for vast, complicated climate and terrain regions, TVDI is improved or modified by the difference between LST in the day and night, the relationship between vegetation index and evapotranspiration, or the representative of surface moisture content [36][37][38][39]. Moreover, TVDI is employed in the analysis of temporal-spatial pattern of drought effectively [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Trends in Long-Term Drought Changes in the Mekong River Delta of Vietnam

Dinh

2020

Remote Sensing

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In recent years, short droughts in the dry season have occurred more frequently and caused serious damages to agriculture and human living in the Mekong River Delta of Vietnam (MRD). The paper attempts to quantify the trends of drought changes in the dry seasons from 2001 to 2015 in the region, using daily MODIS MOD09GQ and MOD11A1 data products. Here, we exploit the Temperature Vegetation Dryness Index (TVDI) to assess levels of droughts. For each image-acquisition time, the TVDI image is computed, based on the Normalized Difference Vegetation Index (NDVI), derived from red and near infrared reflectance data, and the Land Surface Temperature (LST), derived from thermal infrared data. Subsequently, a spatiotemporal pattern of drought changes is estimated, based on mean TVDI values of the dry seasons during the observed period, by a linear regression. As a result, the state of drought in the dry seasons in the MRD has mostly been at light and moderate levels, occupying approximately 62% and 34% of the total area. Several sub-areas in the center have an increased trend of drought change, occupying approximately 12.5% of the total area, because impervious surface areas increase, e.g., the obvious land use change, from forest land and land for cultivation for perennial trees being strongly converted to built-up land for residence and public transportation. Meanwhile, several sub-areas in the coastal regions have a negative trend of drought change because water and absorbent surface areas increase, e.g., most of land for cultivation for perennial trees has been converted to aquaculture land. These cases usually occur in and surrounding forest and wet land, also occupying approximately 12.5% of the total area.

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Development and evaluation of the MTVDI for soil moisture monitoring

Cited by 22 publications

References 80 publications

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

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

A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

Trends in Long-Term Drought Changes in the Mekong River Delta of Vietnam

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