Temperature Vegetation Dryness Index Research Articles

Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China

Passive microwave surface soil moisture (SSM) products tend to have very low resolution, which massively limits their application and validation in regional or local-scale areas. Many climate and hydrological studies are urgently needed to evaluate the suitability of satellite SSM products, especially in alpine mountain areas where soil moisture plays a key role in terrestrial atmospheric exchanges. Aiming to overcome this limitation, a downscaling method based on random forest (RF) was proposed to disaggregate satellite SSM products. We compared the ability of the downscaled soil moisture active passive (SMAP) SSM and soil moisture and ocean salinity satellite (SMOS) SSM products to capture soil moisture information in upstream of the Heihe River Basin by using in situ measurements, the triple collocation (TC) method and temperature vegetation dryness index (TVDI). The results showed that the RF downscaling method has strong applicability in the study area, and the downscaled results of the two products after residual correction have more details, which can better represent the spatial distribution of soil moisture. The validation with the in situ SSM measurements indicates that the correlation between downscaled SMAP and in situ SSM is better than downscaled SMOS at both point and watershed scales in the Babaohe River Basin. From the TC method, the root mean square error (RMSE) of the CLDAS (CMA land data assimilation system), downscaled SMAP and downscaled SMOS were 0.0265, 0.0255 and 0.0317, respectively, indicating that the downscaled SMAP has smaller errors in the study area than others. However, the soil moisture distribution in the study area shown by the SMOS downscaled results is closer than the downscaled SMAP to the degree of drought reflected by TVDI. Overall, this study suggests that the proposed RF-based downscaling method can capture the variation of SSM well, and the downscaled SMAP products perform significantly better than the downscaled SMOS products after the accuracy verification and error analysis of the downscaled results, and it should be helpful to facilitate applications for satellite SSM products at small scales.

Temperature Vegetation Dryness Index-Based Soil Moisture Retrieval Algorithm Developed for Geo-KOMPSAT-2A

The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM information for hydrological and meteorological applications. However, GK-2A’s visible and infrared sensors lack direct SM sensitivity. Therefore, in this study, we developed an SM algorithm based on the conversion relationships between SM and the temperature vegetation dryness index (TVDI) estimated for various land types in the full disk area using two of GK-2A’s level 2 products, land surface temperature (LST) and normalized difference vegetation index (NDVI), and the Global Land Data Assimilation System (GLDAS) SM data for calibration. Methodologically, various coefficients were obtained between TVDI and SM and used to estimate the GK-2A-based SM. The GK-2A SM algorithm was validated with GLDAS SM data during different periods. Our GK-2A SM product showed seasonal and spatial agreement with GLDAS SM data, indicating a dry-wet pattern variation. Quantitatively, the GK-2A SM showed annual validation results with a correlation coefficient (CC) >0.75, bias <0.1%, and root mean square error (RMSE) <4.2–4.7%. The monthly averaged CC values were higher than 0.7 in East Asia and 0.5 in Australia, whereas RMSE and unbiased RMSE values were <0.5% in East Asia and Australia. Discrepancies between GLDAS and GK-2A TVDI-based SMs often occurred in dry Australian regions during dry seasons due to the high LST sensitivity of GK-2A TVDI. We determined that relationships between TVDI and SM had positive or negative slopes depending on land cover types, which differs from the traditional negative slope observed between TVDI and SM. The KMA is currently operating this GK-2A SM algorithm.

Application of temperature vegetation dryness index for drought monitoring in Mongolian Plateau.

Taking the Mongolian Plateau as the study area, the MODIS normalized difference vegetation index (NDVI) and the land surface temperature (LST) in the growing season from 2000 to 2019 were used to construct the NDVI-LST feature space, and based on which the temperature vege-tation dryness index (TVDI) of the Mongolian Plateau was calculated. We used Theil-Sen Median trend analysis, Mann-Kendall test, and Hurst index method to analyze the spatial and temporal varia-tions and future trends of TVDI on the Mongolian Plateau. Furthermore, we examined the relationship between meteorological factors and TVDI on the Mongolian Plateau using partial correlation analysis. The results showed that the TVDI of the Mongolian Plateau during 2000-2019 showed an increasing trend with a rate of 0.0001·a-1, indicating that the Mongolian Plateau's drought condition became heavier slightly in the last 20 years. The drought condition in meadow steppe and typical steppe gradually decreased, and that in desert steppe and alpine grassland was increased. The average Hurst index of TVDI in the growing season was 0.45, and the area with TVDI less than 0.5 accounted for 71.5% of the total area, which indicated that the TVDI during 2000-2019 in most areas turned opposite to the past. In the future, the drought condition in the central desert steppe area and the eastern meadow steppe area might increase, and that in most of the typical steppe and the desert steppe in Inner Mongolia tended to decrease. The drought change in the alpine grassland area was uncertain. There was a significant positive correlation between the TVDI and temperature in 33.6% area of the Mongolian Plateau and a significant negative correlation between the TVDI and precipitation in 34.8% of the area. Moreover, the meteorological factors heavily affected the typical steppe.

Study on Remote Sensing Retrieval of Soil Moisture in Arid Desert Area Based on FTVDI

In this study, the desert oasis area of Minqin, Gansu Province was taken as the study area, and China Gaofen-6(GF-6) satellite and China-Brazil Earth Resource Resources Satellite - 04 (CBERS-04) were invlked as remote sensing data sources. The soil moisture was retrieved by using Forward Temperature Vegetation Dryness Index (FPVDI), and the spatial distribution of soil moisture was analyzed. The result showed that FTVDI could be directly and accurately retrieve soil moisture.

Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model

This paper describes integrating remote sensing and meteorological data for dynamic winter wheat yield prediction at fixed date intervals with an integral regression model. In most cases, both rainfall and irrigation supply the water needs of crops. We postulated that meteorological precipitation could not fully represent the actual amount of water available to crops, whereas the temperature vegetation dryness index (TVDI) can represent actual water, which monitors soil moisture. According to this, meteorological and remote-sensing data based on sunshine hours, temperature, and TVDI formed a light–temperature–water data combination, which are the three most fundamental and critical factors related to crop growth. The new data combination was used as the input variables of the integral regression model, which could predict the crop yield at a fixed date interval of any length. The dynamic results were then compared with that of the meteorology data combination (sunshine hours, temperature, and precipitation). We selected a total of seven county-level areas in semi-humid and arid regions in China. The proposed dynamic prediction approach achieved good results at the county level from March 21 to maturity at 16-day intervals with predicted R2 > 0.94 and root mean squared error range of 15.54–121.01 kg ha−1 between predicted and actual yield with leave-one-year-out cross-validation for each prediction date. The trend of dynamic prediction accuracy had a relatively stable state. In addition, TVDI contained more crop water information, and prediction results using TVDI and meteorology were generally superior to that using only meteorology for semi-humid and arid areas, with the best results for arid regions. Thus, integrating meteorology and TVDI could effectively dynamically predict yield at county level for most agroclimatic regions, and particularly for arid regions. The work has implications for dynamic yield prediction and improving prediction accuracy and can be extended to other areas.

Assessing vegetation condition across topography in Nainital district, India using temperature vegetation dryness index model

Assessing vegetation condition across topography in Nainital district, India using temperature vegetation dryness index model

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China

Drought is pervasive global hazard and seriously impacts ecology. Particularly, vegetation drought, which is chiefly driven by soil moisture (SM) deficiency, has a direct bearing on grain production and human livelihoods. Various drought indices associated with vegetation and SM conditions have been proposed to monitor and detect vegetation drought. In this study, we evaluated the performance of eight drought indices, including Drought Severity Index (DSI), Evaporation Stress Index (ESI), Normalized Vegetation Supply Water Index (NVSWI), Temperature-Vegetation Dryness Index (TVDI), Temperature Vegetation Precipitation Dryness Index (TVPDI), Vegetation Health Index (VHI), Self-calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI), for capturing SM dynamic (derived from Copernicus Climate Change Service) across the six main vegetation coverage types of China. Our results showed DSI and ESI had the best overall performance. When exploring the reasons for the uncertainty of these indices (except SC-PDSI and SPEI) in the evaluation, we found that, in the non-arable regions, the time lag effect of drought indices on SM, the average state and rangeability of corresponding variables and the climatic conditions (precipitation and temperature) all impacted the performance of DSI, ESI, NVSWI, TVPDI and VHI. In the arable region, cropland types (paddy field and non-paddy field) and the uncertainty of SM data mainly caused the uncertainties of the above five indices. With regard to the TVDI, abnormalities of dry and wet edges fitting may be the primary factor affecting its performance. These results demonstrated that these drought indices with reliable and robust performance of capturing SM dynamics can be suggested to characterize the trend of SM. Certainly, this study can provide a reference for the improvement of existing drought indices and the establishment of new drought indices.

Comparison and evaluation of different dryness indices based on vegetation indices-land surface temperature/albedo feature space

Nowadays, there are many dryness indices based on vegetation indices (VI s )-land surface temperature (T s ) feature space. Which dryness index should we use in dryness monitoring? The differences and capabilities for dryness monitoring among seven indices were evaluated in this study. These dryness indices were based on VI s -T s or Albedo feature space. For instance, Temperature Vegetation Dryness Index (TVDI) from a triangle NDVI (Normalized Difference Vegetation Index)-T s feature space (TVDI t ), TVDI from bi-parabolic NDVI-T s feature space (TVDI c ), TFDI from FPAR (Fraction of Absorbed Photosynthetically Active Radiation)-T s feature space, TVDI from LAI (Leaf Area Index)-T s feature space (TLDI), TVDI from EVI (Enhanced Vegetation Index)-T s feature space (TEDI), TVDI from SAVI (Soil Adjusted Vegetation Index)-T s feature space (TSDI) and Vegetation Condition Albedo Dryness Index (VCADI) from NDVI-Albedo feature space. In this study, the assimilated 5/10 cm depth soil moisture, field measured 10 cm depth soil moisture, precipitation, and the assimilated land surface temperature data were selected as the indicators to test the performance of each dryness index in two periods of 2013/2015 and three periods of 2018. The results showed that the spatial distributions of dryness from TVDI c , TVDI t , TFDI, TLDI, TEDI, and TSDI were similar, except for VCADI. The ability of TSDI and TVDI t in dryness monitoring was not as good as TVDI c and TEDI, however, the robustness of them was stable and can be an alternative dryness index. TFDI can be applied to evaluate dryness conditions, but its robustness was not stable and its monitoring performance was not as good as other indices in Shaanxi province, China. Although TLDI can detect dryness conditions when NDVI reached saturation, its robustness was worse than TEDI. When NDVI did not reach saturation, TVDI c had the best ability in dryness monitoring. TEDI was the optimal dryness index when NDVI was approaching or reaching saturation. This study determined that NDVI reached saturation (LAI = 3) when its average value was >0.5368.

Novel Weight-Based Approach for Soil Moisture Content Estimation via Synthetic Aperture Radar, Multispectral and Thermal Infrared Data Fusion.

Though current remote sensing technologies, especially synthetic aperture radars (SARs), exhibit huge potential for soil moisture content (SMC) retrievals, such technologies also present several performance disadvantages. This study explored the merits of proposing a novel data fusion methodology (partly decision level and partly feature level) for SMC estimation. Initially, individual estimations were derived from three distinct methods: the inversion of an Empirically Adapted Integral Equation Model (EA-IEM) applied to SAR data, the Perpendicular Drought Index (PDI), and the Temperature Vegetation Dryness Index (TVDI) determined from Landsat-8 data. Subsequently, three feature level fusions were performed to produce three different novel salient feature combinations where said features were extracted from each of the previously mentioned methods to be the input of an artificial neural network (ANN). The latter underwent a modification of its performance function, more specifically from absolute error to root mean square error (RMSE). Eventually, all SMC estimations, including the feature level fusion estimation, were fused at the decision level through a novel weight-based estimation. The performance of the proposed system was analysed and validated by measurements collected from three study areas, an agricultural field in Blackwell farms, Guildford, United Kingdom, and two different agricultural fields in Sidi Rached, Tipasa, Algeria. Those measurements contained SMC levels and surface roughness profiles. The proposed SMC estimation system yielded stronger correlations and lower RMSE values than any of the considered SMC estimation methods in the order of 0.38%, 1.4%, and 1.09% for the Blackwell farms, Sidi Rached 1, and Sidi Rached 2 datasets, respectively.

Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin.

BackgroundClimate change is an important factor driving vegetation changes in arid areas. Identifying the sensitivity of vegetation to climate variability is crucial for developing sustainable ecosystem management strategies. The Irtysh River is located in the westerly partition of China, and its vegetation cover is more sensitive to climate change. However, previous studies rarely studied the changes in the vegetation coverage of the Irtysh River and its sensitivity to climate factors from a spatiotemporal perspective.MethodsWe adopted a vegetation sensitivity index based on remote sensing datasets of high temporal resolution to study the sensitivity of vegetation to climatic factors in the Irtysh River basin, then reveal the driving mechanism of vegetation cover change.ResultsThe results show that 88.09% of vegetated pixels show an increasing trend in vegetation coverage, and the sensitivity of vegetation to climate change presents spatial heterogeneity. Sensitivity of vegetation increases with the increase of coverage. Temperate steppe in the northern mountain and herbaceous swamp and broadleaf forest in the river valley, where the normalized difference vegetation index is the highest, show the strongest sensitivity, while the desert steppe in the northern plain, where the NDVI is the lowest, shows the strongest memory effect (or the strongest resilience). Relatively, the northern part of this area is more affected by a combination of precipitation and temperature, while the southern plains dominated by desert steppe are more sensitive to precipitation. The central river valley dominated by herbaceous swamp is more sensitive to temperature-vegetation dryness index. This study underscores that the sensitivity of vegetation cover to climate change is spatially differentiated at the regional scale.

Multiple indices based agricultural drought assessment in the northwestern part of Bangladesh using geospatial techniques

Droughts are very common in Bangladesh especially in the northwestern part, due to geographical position, variation in Groundwater table, and the spatial variation in seasonal rainfall pattern. In the present study, three drought indices viz., Vegetation Health Index (VHI), Temperature Vegetation Dryness Index (TVDI), and Shortwave Infrared Dryness Index (VSDI), were calculated utilizing multitemporal Landsat images. Field verification was done for the retrieved parameters needed to calculate the indices i.e., soil moisture content and Land Surface Temperature (LST). Results from various indices were compared to understand the drought pattern in the study area. The VHI revealed that 2014 and 2002 were the most drought-prone year and the drought extent in this region intensified from mild to severe drought. Severe drought was found in northern Lalmonirhat, central Nilphamari, and Thakurgaon, the southern part of Dinajpur, dispersed part of Panchagarh districts and sand bars of the river. It is evident that Thakurgaon, Dinajpur, Gaibandha, and the northern part of Lalmonirhat districts face high agricultural drought risk, Nilphamari, Panchagarh, and Rangpur demonstrate medium risk, and Kurigram district shows slight agricultural drought risk. The drought severity was mainly triggered out due to the decreasing trend of vegetation identified by the Normalized Difference Vegetation Index (NDVI) and also for the climbing trend of LST. In the year 2018, drought mainly prevailed due to the increased amount of LST mainly in the Nilphamari district. Several regions such as dried river channels, sand bars, and uncultivated land show high drought conditions due to lack of moisture in this region and reduce water flow impeded by the upstream dams. As drought has great impacts on agriculture, it is expected that this satellite-based drought assessment will be beneficial to improve the understanding of drought in the northwestern regions of Bangladesh.

Soil moisture change analysis under watershed management practice using in situ and remote sensing data in a paired watershed.

Soil moisture, vegetation cover, and land surface temperature are vital variables in water-energy balance, eco-hydrological processes, and water resources management, which can be influenced by watershed management activities. This research focused on the spatial and temporal variability of soil moisture, vegetation cover, land surface temperature, and Temperature-Vegetation Dryness Index (TVDI) under a biological watershed management practice in the Taleghan paired watershed, namely, treated (TW) and control watersheds (CW), in Alborz province, Iran. In this research, along with the remote sensing techniques, the soil moisture and vegetation cover data were measured and statistically analyzed in the three aspects of both TW and CW during a growth period from May to October 2017. The results indicated that soil moisture, vegetation cover, and land surface temperature values in the paired watershed were significantly different at the 0.01 level during the study period. The increased vegetation cover in the TW had an inverse effect on the land surface temperature and TVDI, while directly impacted the soil moisture content. The average TVDI in the CW was 0.83, while this index was found to be 0.69 in the TW. Unlike the vegetation cover and soil moisture, the results revealed that the southern aspects had the highest TVDI and land surface temperature compared to the northern and eastern aspects of both watersheds. However, the increased vegetation cover as a biological watershed management activity in the steep terrain and mountainous areas of TW led to an increased soil moisture and a decreased land surface temperature and soil dryness. As a result, decreasing soil dryness in the TW can exert vital controls on the water resources and increasing water availability. In the arid and semiarid countries such as Iran, a proper watershed management activity can effectively increase soil moisture and water availability in the watersheds. In particular, the vegetation cover protection and biological practices can be considered as practical solutions in the rehabilitation of exhausted watersheds in arid and semiarid environments.

Spatio-Temporal Assessment of Surface Moisture and Evapotranspiration Variability Using 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.

Remote sensing‐based biological and nonbiological indices for evaluating desertification in Iran: Image versus field indices

AbstractMapping and monitoring of the complex process of desertification based on ground data in broad arid and semiarid areas faces basic limitations. Therefore, the purpose of the present study was to propose a new method for mapping this phenomenon in central Iran using biological and nonbiological (BNB) indices of remote sensing products from 2003 to 2016. For this purpose, BNB indices including normalized difference vegetation index, land surface temperature, temperature vegetation dryness index, precipitation, evapotranspiration, net primary production, rain use efficiency (RUE), aridity index, and slope were extracted using MOD13A2, MOD11A2, PERSIANN‐CDR, and SRTM products. After calibration and normalization of indices, they were combined using fuzzy logic and gamma operator and the combined 2003 map was validated by MEDALUS model map prepared based on ground data in 2003 using Pearson correlation and error matrix. Results showed more than 70% correlation (p < .001) as well as overall accuracy and kappa coefficient of more than 70% and 0.5 between remote sensing‐based and MEDALUS‐based desertification maps. According to the 2003 and 2016 maps, desertification classes including low, moderate, severe, and very severe changed from 11.9, 49.8, 34, and 4.1% to 11.11, 43.21, 40.43, and 5.24%, respectively, which indicate increasing trend of desertification in the region. The findings demonstrate the high capability of proposed method to map and monitor desertification classes. Therefore, it can be used to update existing desertification models and to report desertification condition and its positive and negative trends at local, national, and international levels.

Specifying the relationship between land use/land cover change and dryness in central Vietnam from 2000 to 2019 using Google Earth Engine

Land use/land cover (LULC) change and climate change are thought to be closely related and mutually influential, especially in contexts where land is converted for urban expansion or agriculture. We represent a first attempt to specify the relationship between LULC change and dryness in a region of Vietnam that is profoundly affected by climate change. Using the temperature–vegetation dryness index (TVDI), we specified the relationships and changes underway in Vietnam’s Ba river basin, one of the largest river systems in the South Central Coast. Using Google Earth Engine, we extracted land use data from Landsat images and calculated TVDI values from Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000 to 2019. We found, first, that agricultural area and deforestation rose by 7.2% and 2.4% annually, respectively. These changes were driven by economic development, rising crop prices, illegal logging, wildfires, and emergence of new agricultural areas. Second, areas classified in the driest TVDI intervals (dry and very dry) occupied 57% of the basin in 2019, 70% of which was agricultural lands and 20% other (mainly urban and bare lands). These driest land categories expanded at an average rate of 0.08% to 0.1% per year. Moreover, 90% of areas classified as “very wet” and “wet” were forest. We observed a strong relationship between LULC change and TVDI. Climate change and LULC change thus appear to be propelling the basin’s climate toward increasing dryness, suggesting the need for policies to reduce the agricultural area and expand forests while developing more adaptive and sustainable livelihoods.

Study on the Influence and Correction of Spatial Heterogeneity of Air Temperature in Drought Remote Sensing Monitoring

Temperature Vegetation Dryness Index (TVDI) is one of the commonly used methods in remote sensing drought monitoring. Since this method requires the study area to be large enough to achieve the ideal fitting of the dry and wet edges of the temperature-vegetation feature space, the spatial heterogeneity of meteorological elements has a significant influence on the results. Taking Hebei Plain as an example, the air temperature data at the moment of satellite transit are used to modify the surface temperature retrieved by remote sensing, that is, the surface temperature subtracts the spatial heterogeneity of air temperature, to eliminate the influence of the spatial heterogeneity of air temperature on the TVDI index. TVDI index and surface temperature before and after correction are compared in this paper. The results show that the spatial heterogeneity of air temperature does have a significant effect on TVDI index, and the surface temperature distribution after temperature correction is more reasonable, and will not have great effect on the method of using TVDI to monitor the degree of drought. It can be seen that the correction of surface temperature is more helpful to improve the accuracy of agricultural drought monitoring.

Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39–0.80) than for the Copernicus Sentinel-1-based (−0.03 to 0.69) and SMAP/S1 (0.37–0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

An alternative approach to delineate wetland influence zone of a tropical intertidal mudflat using geo-information technology

Mudflats are important tropical coastal wetlands having high carbon sequestration and biodiversity potentials. The actual reach of a mudflat environment spreads far beyond its perennial wetland areas in a coastal site. Accurate demarcation of the extent of these buffer areas is imperative for sustainable wetland management. This study tried to delineate the wetland influence zone (WIZ) of an intertidal mudflat in the Medinipur coastal plain, India as a functional extension around the considerably smaller yet hydrous cum muddy depressions. Normalized Difference Water Index (NDWI), derived from Sentinel-2 MultiSpectral Instrument (MSI) datasets, and Temperature Vegetation Dryness Index (TVDI), derived from the coupled use of Sentinel-2 MSI and Landsat-8 datasets, had been analyzed and merged to develop nine characteristic zones. Among these, eight zones (Zone I to Zone VIII) were identified as part of the WIZ and the rest one (NDWI: < −0.15, TVDI > 0.7) was considered as a supra-tidal zone, devoid of the mudflat features. Although the overall extent of WIZ (Minimum: 339.20 ha, Maximum: 350.22 ha) did not change much from 2016 to 2020, the inter-zonal spatial arrangements of vegetation and physiography transformed continuously. The gradual disappearance of the major tidal creek from the south-western parts and increasing human footprints throughout the mudflat had expedited this transformation. Accordingly, periodic assessments incorporating microwave and optical datasets along with high precision in-situ measurements of soil moisture was suggested for the efficient monitoring of these fragile tropical ecosystems.

Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

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.