Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)

Amani, Meisam; Salehi, Bahram; Mahdavi, Sahel; Masjedi, Ali; Dehnavi, Sahar

doi:10.1016/j.rse.2017.05.026

Cited by 128 publications

(72 citation statements)

References 30 publications

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“…Soil moisture is a key hydrological variable that plays an important role in physical processes such as rainfall-runoff generation, infiltration, photosynthesis, evapotranspiration, and groundwater recharge (Amani et al, 2017;Ford et al, 2015;Orth & Seneviratne, 2017). Soil moisture governs exchanges of water, energy, and carbon fluxes among land surface, vegetation, and atmosphere (Amani et al, 2017;Ford et al, 2015;Orth & Seneviratne, 2017). Therefore, representation and estimation of soil moisture in climate models and hydrological models (HMs) largely influence the performances of simulations and predictions of hydrological cycle (Cheng et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Chen

et al. 2019

JGR Atmospheres

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A large population of global soil moisture data sets generated by a variety of models is compared with the latest satellite‐based Essential Climate Variable (ECV) soil moisture product in a common framework. The model‐based surface soil moisture data sets include Global Land Data Assimilation System (GLDAS), reanalysis products, Coupled Model Intercomparison Project Phase 5 Global Climate Models (GCMs), and Inter‐Sectoral Impact Model Intercomparison Project (including observation‐driven outputs ISI‐MIP_OBS and GCM‐driven outputs ISI‐MIP_GCM). We evaluate the model‐based surface soil moisture against ECV with focuses on spatial patterns, temporal correlations, long‐term trends, and relationships with precipitation and Normalized Difference Vegetation Index. The results indicate that all data sets reach a good agreement on the spatial patterns of surface soil moisture, which are also consistent with that of precipitation. However, data sets produced by different techniques have considerable discrepancies in the absolute values of surface soil moisture. Specifically, GCMs tend to underestimate the absolute values of surface soil moisture relative to ECV. In comparisons that remove the influence of absolute values (e.g., unbiased root‐mean‐square error), all model‐based data sets show comparable performances against ECV. GLDAS, reanalysis, and ISI‐MIP_OBS data sets show significant positive temporal correlations with ECV. Model‐based data sets and ECV consistently indicate widespread drying trends during 1980–2005, but the regional trends vary in different data sets. Compared to ECV, GLDAS and reanalysis data sets exhibit more intensive drying trends, while Coupled Model Intercomparison Project Phase 5 and ISI‐MIP_GCM tend to underestimate the drying. In most of the regions, the wetting/drying trends are consistent with the increases/decreases in precipitation and Normalized Difference Vegetation Index.

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Section: Introductionmentioning

confidence: 99%

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Chen

et al. 2019

JGR Atmospheres

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“…Other channel combinations produce a false-color composite ( [20] found that the NDVI values obtained by highresolution images still have a high correlation and the spectral characteristics are unchanged when NDVI is applied at high spatial resolution. Other studies suggest that the NDVI profile in high-resolution images is very similar to NDVI processing in the original multispectral image value, so there is no significant change when using highresolution images as well as the medium resolution in NDVI processing [21]. The result of NDVI transformation is given in Land surface temperature (LST) is an important factor for the determination of several biophysical parameters and processes.…”

Section: Data Analysis Resultsmentioning

confidence: 99%

Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery

Hidayati

Suharyadi

Danoedoro

2019

KSS

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The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.

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“…The vegetation responses can be observed through vegetation greenness changes with land-atmosphere water, carbon and energy fluxes, and linked climate feedbacks [24,30,85]. Additionally, the LST derived from MOD11A2 has been used as a key factor to examine drought severity [33,40,86] as a response of soil moisture and its texture [29]. Based on the relationship among parameters and the subsequent computation of the drought severity index (DSI) [24], an integration of the operational MOD16 ET/PET, MOD13 EVI, and MOD11 LST products was employed to develop a remotely sensed agricultural drought index for the MRD as an enhancing piece of information of water stress.…”

Section: Datamentioning

confidence: 99%

“…Drought variations, in fact, depend on various factors such as precipitation, soil moisture, and ET. Several studies [29,88,89] have reported that there is a significant relationship between drought and other relevant parameters (e.g., vegetation indices, LST, rainfall, ET). The ET reflects the status of ecosystem function and is directly related to water, carbon, and energy cycles of the land surface [45].…”

Section: Enhanced Drought Severity Indexmentioning

confidence: 99%

“…This indicator manifests a condition of wetness or dryness [24,25], as shown within Equation (3). Besides, both the vegetation and LST reflect the water-energy balance of the surface [29,33], and thus, are good indicators for drought assessment. Consequently, the additional environment variables (i.e., ET, PET, EVI, and LST) provide information about water content as an effective indicator of agricultural drought monitoring.…”

Section: Enhanced Drought Severity Indexmentioning

confidence: 99%

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Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Tran

Myint

et al. 2019

Remote Sensing

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Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001-2018 period, along with the Mann-Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

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Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)

Cited by 128 publications

References 30 publications

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

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