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

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.

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Temporal Calibration of an Evaporation-Based Spatial Disaggregation Method of SMOS Soil Moisture Data

Remote Sensing ◽

10.3390/rs12101671 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1671

Author(s):

Vivien-Georgiana Stefan ◽

Olivier Merlin ◽

Maria-José Escorihuela ◽

Beatriz Molero ◽

Jamal Chihrane ◽

...

Keyword(s):

Soil Moisture ◽

Linear Regression ◽

Linear Model ◽

Correlation Coefficient ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remotely Sensed ◽

Current Version ◽

Soil Moisture Data

The resolution of current satellite surface soil moisture (SM) estimates is very low, of tens of kilometers, which proves to be insufficient for various agricultural and hydrological applications. Amongst the existing downscaling approaches of remotely sensed SM, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) improves the resolution of SMOS (Soil Moisture and Ocean Salinity) soil moisture data using soil evaporative efficiency (SEE) estimates at high resolution (HR) and a SEE(SM) model implemented at low resolution (LR). Defined as the ratio of actual to potential soil evaporation, SEE can be derived from the remotely sensed land surface temperature (LST) and normalized difference vegetation index (NDVI). The current version of DISPATCH uses a linear SEE(SM) model. This study aims at improving the SEE(SM) model and testing different calibration strategies, to ultimately have more robust and better downscaled SM products. A nonlinear SEE(SM) model is introduced and its influence on the derived HR SM products is studied over a range of conditions. Each model, linear and nonlinear, is calibrated from remote sensing data on a daily and a multi-date basis. The approaches were tested over two mixed dry and irrigated areas in Catalonia, Spain, and over one dry area in Morocco. When using the linear model, better statistical results were generally obtained using a daily calibration (current version of DISPATCH), most notably over one Spanish site. However, the best results were systematically obtained for an annually calibrated nonlinear model, in terms of all metrics considered: correlation coefficient, slope of the linear regression, bias, unbiased root mean square error. In particular, when using the annually calibrated nonlinear SEE (SM) model, the temporal slope of the linear regression between disaggregated and in situ soil moisture increased to 1.16 and 0.75 for one Spanish site and for the Moroccan site (as opposed to 0.44 and 0.58, respectively, when using the linear model with a daily calibration). The temporal correlation coefficient increased to 0.47 and 0.54 over the Spanish sites (as opposed to 0.18 and 0.27, respectively, when using the linear model with a daily calibration). Those contrasted results indicate compensation effects between the model type and the calibration strategy. Taking into account studies that report the strong nonlinear behavior of the SEE with respect to SM, the introduction of the nonlinear SEE(SM) model in DISPATCH, combined with a multi-date calibration, is proven to perform significantly better under various conditions, leading to more robust disaggregated SM products. The SEE modeling based on the nonlinear SM model, with a multi-date calibration, could be integrated into the CATDS—Centre Aval de Traitement des Données SMOS as a future product, as well as into existing evapotranspiration models, which are based on a combination of thermal and microwave data.

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

Remote Sensing ◽

10.3390/rs13152990 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2990

Author(s):

Sumin Ryu ◽

Young-Joo Kwon ◽

Goo Kim ◽

Sungwook Hong

Keyword(s):

Soil Moisture ◽

East Asia ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Negative Slope ◽

Retrieval Algorithm ◽

Infrared Sensors ◽

Dryness Index ◽

Land Data Assimilation System

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.

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An Improved Method for Estimating Global Evapotranspiration Based on Satellite Determination of Surface Net Radiation, Vegetation Index, Temperature, and Soil Moisture

Journal of Hydrometeorology ◽

10.1175/2007jhm911.1 ◽

2008 ◽

Vol 9 (4) ◽

pp. 712-727 ◽

Cited By ~ 119

Author(s):

Kaicun Wang ◽

Shunlin Liang

Keyword(s):

Soil Moisture ◽

Correlation Coefficient ◽

Land Surface ◽

Vegetation Index ◽

The United States ◽

Ratio Method ◽

Daily Maximum ◽

Surface Model ◽

Net Radiation ◽

Improved Method

Abstract A simple and accurate method to estimate regional or global latent heat of evapotranspiration (ET) from remote sensing data is essential. The authors proposed a method in an earlier study that utilized satellite-determined surface net radiation (Rn), a vegetation index, and daytime-averaged/daily maximum air temperature (Ta) or land surface temperature (Ts) data. However, the influence of soil moisture (SM) on ET was not considered and is addressed in this paper by incorporating the diurnal Ts range (DTsR). ET, measured by the energy balance Bowen ratio method at eight enhanced facility sites on the southern Great Plains in the United States and by the eddy covariance method at four AmeriFlux sites during 2001–06, is used to validate the improved method. Site land cover varies from grassland, native prairie, and cropland to deciduous forest and evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-averaged ET using a combination of Rn, enhanced vegetation index (EVI), daily maximum Ts, and DTsR is about 0.92 for all the sites, the bias is −1.9 W m−2, and the root-mean-square error (RMSE) is 28.6 W m−2. The sensitivity of the revised method to input data error is small. Implemented here is the revised method to estimate global ET using diurnal Ta range (DTaR) instead of DTsR because DTsR data are not available yet, although DTaR-estimated ET is less accurate than DTsR-estimated ET. Global monthly ET is calculated from 1986 to 1995 at a spatial resolution of 1° × 1° from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and is compared with the 15 land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months of global ET show that the bias is 4.5 W m−2, the RMSE is 19.8 W m−2, and the correlation coefficient is 0.82. Incorporating DTaR distinctively improves the accuracy of the estimate of global ET.

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Integration of Microwave and Optical/Infrared Derived Datasets from Multi-Satellite Products for Drought Monitoring

Water ◽

10.3390/w12051504 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1504

Author(s):

Zhengdong Wang ◽

Peng Guo ◽

Hong Wan ◽

Fuyou Tian ◽

Linjiang Wang

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Water ◽

Land Surface ◽

North China ◽

Vegetation Index ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Sensing Data ◽

Dryness Index

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the advantages of microwave and optical remote sensing data to improve the original Temperature–Vegetation–Soil Moisture Dryness Index (TVMDI) model, and was constructed based on the Modified Soil-Adjusted Vegetation Index (MSAVI), land surface temperature (LST), and downscaled soil moisture (SM) as the three-dimensional axes. The global land data assimilation system (GLDAS) SM, meteorological data and surface water were used to evaluate and verify the monitoring results. The results showed that iTVMDI had a higher negative correlation with GLDAS SM (R = −0.73) than TVMDI (R = −0.55). Additionally, the iTVMDI was well correlated with both precipitation and surface water, with mean correlation coefficients (R) of 0.65 and 0.81, respectively. Overall, the accuracy of drought estimation can be significantly improved by using multi-source satellite data to measure the required surface variables, and the iTVMDI is an effective method for monitoring the spatial and temporal variations of drought.

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A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133309338997 ◽

2009 ◽

Vol 33 (2) ◽

pp. 224-250 ◽

Cited By ~ 170

Author(s):

G. Petropoulos ◽

T.N. Carlson ◽

M.J. Wooster ◽

S. Islam

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Energy ◽

Land Surface ◽

Vegetation Index ◽

Surface Soil ◽

Surface Soil Moisture ◽

Energy Fluxes ◽

Surface Energy Fluxes ◽

Wide Range

Imagery from remote sensing systems, often combined with ancillary ground information, is able to provide repetitive, synoptic views of key parameters characterizing land surface interactions, including surface energy fluxes and surface soil moisture. Differing methodologies using a wide range of remote sensing data have been developed for this purpose. Approaches vary from purely empirical to more complex ones, including residual methods and those that have their basis in the biophysical properties characterizing a two-dimensional Ts/VI (surface temperature/ vegetation index) scatterplot domain derived from remote sensing observations. The present article aims to offer a comprehensive and systematic review of this latter group of methods, which differ in terms of the complexity and assumptions they entail as well as their requirement for field-based and other ancillary data. Prior to the review, the biophysical meanings and properties encapsulated in the Ts/VI feature space is elucidated, since these represent the building block upon which all the Ts/VI methods described herein are based. The overview of the Ts/VI methods is also very timely, as one such method is being scheduled in the operational retrieval of surface soil moisture content by the National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellite platforms due to be launched in the next 12 years starting from 2016.

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Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

Remote Sensing ◽

10.3390/rs13071340 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1340

Author(s):

Shuailong Feng ◽

Shuguang Liu ◽

Lei Jing ◽

Yu Zhu ◽

Wende Yan ◽

...

Keyword(s):

Remote Sensing ◽

Environmental Impacts ◽

Land Surface ◽

Vegetation Index ◽

Highway Construction ◽

Temporal Changes ◽

Moisture Index ◽

Buffer Zones ◽

Wide Range ◽

Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

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Characterizing waste disposal sites by using multi-spectral satellite imagery

10.5194/egusphere-egu21-12495 ◽

2021 ◽

Author(s):

Gaetana Ganci ◽

Annalisa Cappello ◽

Giuseppe Bilotta ◽

Giuseppe Pollicino ◽

Luigi Lodato

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Waste Disposal ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

The State ◽

Dump Site ◽

Temperature State ◽

Surrounding Areas

<p>The application of remote sensing for monitoring, detecting and analysing the spatial and extents and temporal changes of waste dumping sites and landfills could become a cost-effective and powerful solution. Multi-spectral satellite images, especially in the thermal infrared, can be exploited to characterize the state of activity of a landfill.&#160; Indeed, waste disposal sites, during the period of activity, can show differences in surface temperature (LST, Land Surface Temperature), state of vegetation (estimated through NDVI, Normalized Difference Vegetation Index) or soil moisture (estimated through NDWI, Normalized Difference Water Index) compared to neighboring areas. Landfills with organic waste typically show higher temperatures than surrounding areas due to exothermic decomposition activities. In fact, the biogas, in the absence or in case of inefficiency of the conveying plants, rises through the layers of organic matter and earth (landfill body) until it reaches the surface at a temperature of over 40 &#176; C. Moreover, in some cases, leachate contamination of the aquifers can be identified by analyzing the soil moisture, through the estimate of the NDWI, and the state of suffering of the vegetation surrounding the site, through the estimate of the NDVI. This latter can also be an indicator of soil contamination due to the presence of toxic and potentially dangerous waste when buried or present nearby. To take into account these facts, we combine the LST, NDVI and NDWI indices of the dump site and surrounding areas in order to characterize waste disposal sites. Preliminary results show how this approach can bring out the area and level of activity of known landfill sites. This could prove particularly useful for the definition of intervention priorities in landfill remediation works.</p>

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Global downscaling of remotely sensed soil moisture using neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5341-2018 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5341-5356 ◽

Cited By ~ 10

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatiotemporal Scales ◽

Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

10.5194/hess-2019-105 ◽

2019 ◽

Cited By ~ 3

Author(s):

Bouchra Ait Hssaine ◽

Olivier Merlin ◽

Jamal Ezzahar ◽

Nitu Ojha ◽

Salah Er-raki ◽

...

Keyword(s):

Soil Moisture ◽

Energy Balance ◽

Surface Temperature ◽

Land Surface Temperature ◽

Vegetation Cover ◽

Land Surface ◽

Surface Soil ◽

Surface Soil Moisture ◽

Wide Range ◽

The Mean

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

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Dynamics of human-induced lakes and their impact on land surface temperature in Toshka Depression, Western Desert, Egypt

10.21203/rs.3.rs-229433/v1 ◽

2021 ◽

Author(s):

Rasha Abou Samra

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Research Area ◽

Western Desert ◽

Severe Drought ◽

Land Use Land Cover

Abstract Land surface temperature (LST) is a significant environmental variable that is appreciably influenced by land use /land cover changes. The main goal of this research was to quantify the impacts of land use/land cover change (LULC) from the drying of Toshka Lakes on LST by remote sensing and GIS techniques. Landsat series TM and OLI satellite images were used to estimate LST from 2001 to 2019. Automated Water Extraction Index (AWEI) was applied to extract water bodies from the research area. Optimized Soil-Adjusted Vegetation Index (OSAVI) was utilized to predict the reclaimed land in the Toshka region until 2019. The results indicated a decrease in the lakes by about 1517.79 km2 with an average increase in LST by about 25.02 °C between 2001 and 2019. It was observed that the dried areas of the lakes were converted to bare soil and are covered by salt crusts. The results indicated that the land use change was a significant driver for the increased LST. The mean annual LST increased considerably by 0.6 °C/y between 2001 and 2019. A strong negative correlation between LST and Toshka Lakes area (R-square = 0.98) estimated from regression analysis implied that Toshka Lakes drying considerably affected the microclimate of the study area. Severe drought conditions, soil degradation, and many environmental issues were predicted due to the rise of LST in the research area. There is an urgent need to develop favorable strategies for sustainable environmental management in the Toshka region.

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