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Recognition of the importance of soil moisture information to the optimisation of water-limited dryland cereal production has led to Australian growers being encouraged to make use of soil moisture sensors. However, irrespective of the merits of different sensing technologies, only a small soil volume is sensed, raising questions as to the utility of such sensors in broadacre cropping, especially given spatial variability in soil water holding capacity. Here, using data collected from contrasting sites in South Australia and Western Australia over two seasons, during which either wheat or barley were grown, we describe a method for extrapolating soil moisture information away from the location of a probe using freely-available NDVI time series and weather data as covariates. Relationships between soil moisture probe data, cumulative NDVI (ΣNDVI), cumulative net precipitation (ΣNP) and seasonal growing degree days (GDD) were significant (P < 0.0001). In turn, these could be used to predict soil moisture status for any location within a field on any date following crop emergence. However, differences in ΣNDVI between different within-field zones did not fully explain differences in the soil moisture from multiple sensors located in these zones, resulting in different calibrations being required for each sensor or zone and a relatively low accuracy of prediction of measured soil moisture (R2adj ~ 0.4–0.7) which may not be sufficient to support targeted agronomic decision-making. The results also suggest that at any location within a field, the range of variation in soil moisture status down the soil profile on any given date will present as greater than the spatial variation in soil moisture across the field on that date. Accordingly, we conclude that, in dryland cereal cropping, the major value in soil moisture sensors arises from an enhanced ability to compare seasons and to relate similarities and differences between seasons as a guide to decision-making. [ABSTRACT FROM AUTHOR]

Soil moisture plays a key role in hydrology, water soil balance modeling, and drought assessment. In this study, the effects of snow properties in winter on the soil moisture in spring were evaluated in a catchment in the northwest of Iran from 2000 to 2020. For this purpose, the snow depth, water equivalent to snow, and snow-covered area were calculated based on satellite images in four regions with different altitudes. Furthermore, the soil moisture was obtained at two depths of 0–10 cm and 10–40 cm using satellite images. The results showed that the Pearson's correlation coefficient was between 0.4 and 0.5 in all cases, which indicates an average correlation between the snow and soil moisture in the catchment under study. Moreover, the correlation of the snow depth and snow water equivalent with the soil moisture at the depth of 10–40 cm was more than the one at the depth of 0–10 cm. Also, the snow-covered area demonstrated the highest correlation with the soil moisture at a depth of 0–10 cm. Finally, a linear relationship was obtained between the snow properties and soil moisture at two depths using the available data. Such relationships can be used to predict soil moisture for various applications. [ABSTRACT FROM AUTHOR]

Drought, a highly detrimental natural disaster, poses significant threats to both human populations, wildlife, and vegetation. Traditional methods of monitoring soil moisture levels rely on ground-based measurements from meteorological stations. However, these stations often lack comprehensive coverage in certain agricultural areas, necessitating the use of alternative methods such as satellite remote sensing. This technique provides a reliable means of measuring soil moisture, a critical factor in effective agricultural management. This paper investigates variations in soil moisture and drought using data from the Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active and Passive (SMAP) system. To evaluate the accuracy of these data products, we compared both datasets with the Global Land Data Assimilation System (GLDAS) NOAH model from 2018 to 2019. Our findings reveal a strong correlation between the datasets and the model, with Pearson correlation coefficients (r) and Root Mean Square Errors (RMSE) of approximately r = 0.98 and RMSE = 0.03 for SMAP, and r = 0.97 and RMSE = 0.02 for CYGNSS, respectively. We further compared these measurement datasets with drought indicators such as the Standardized Precipitation Index over three months (SPI3), the Normalized Difference Vegetation Index (NDVI), and Total Water Storage (TWS). The correlation coefficients between SMAP and the three indicators (NDVI, SPI3, and TWS) were 0.93, 0.84, and 0.047, respectively, while the coefficients between CYGNSS and the same indicators were 0.86, 0.78, and 0.56, respectively. All the variables also exhibited significant p-values. Despite minor differences, the results demonstrate excellent agreement. Our findings underscore the sensitivity of space-based sensors to drought conditions, highlighting their effectiveness as tools for detecting and monitoring drought (e.g. agricultural drought), particularly in the short term. [ABSTRACT FROM AUTHOR]

Soil Moisture (SM) monitoring is crucial for various applications in agriculture, hydrology, and climate science. Remote Sensing (RS) offers a powerful tool for large-scale SM retrieval. This paper explores the advancements in RS techniques for SM estimation. We discuss the applications of these techniques, along with the advantages and limitations of traditional physical models and data-driven Machine Learning (ML) based approaches. The paper emphasizes the potential of combining ML and physical models to leverage the strengths of both approaches. We explore the challenges associated with this integration and future research directions to improve the accuracy, scalability, and robustness of RS-based SM retrieval. Finally, the paper also discusses a few issues such as input data selection, data availability, ML complexity, the need for public datasets for benchmarking, and analysis. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite Systems (GNSS), including the US's GPS, China's BDS, the European Union's Galileo, and Russia's GLONASS, offer real-time, all-weather, any-time, anywhere and high precision observations by transmitting L band signals continuously, which have been widely used for positioning, navigation and timing. With the development of GNSS technology, it has been found that GNSS-reflected signals can be used to detect Earth's surface characteristics together with other signals of opportunity. In this paper, the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry (GNSS-R) in theory, methods, techniques and observations. New developments and progresses in GNSS-R instruments, theoretical modeling, and signal processing, ground and space-/air-borne experiments, parameters retrieval (e.g. wind speed, sea surface height, soil moisture, ice thickness), sea surface altimetry and applications in the atmosphere, oceans, land, vegetation, and cryosphere are given and reviewed in details. Meanwhile, the challenges in the GNSS-R development of each field are also given. Finally, the future applications and prospects of GNSS-R are discussed, including multi-GNSS reflectometry, new GNSS-R receivers, GNSS-R missions, and emerging applications, such as mesoscale ocean eddies, ocean phytoplankton blooms, microplastics detection, target recognition, river flow, desert studies, natural hazards and landslides monitoring. [ABSTRACT FROM AUTHOR]

This paper presents the composite drought indicator (CDI) that Jordanian, Lebanese, Moroccan, and Tunisian government agencies now produce monthly to support operational drought management decision making, and it describes their iterative co-development processes. The CDI is primarily intended to monitor agricultural and ecological drought on a seasonal time scale. It uses remote sensing and modelled data inputs, and it reflects anomalies in precipitation, vegetation, soil moisture, and evapotranspiration. Following quantitative and qualitative validation assessments, engagements with policymakers, and consideration of agencies' technical and institutional capabilities and constraints, we made changes to CDI input data, modelling procedures, and integration to tailor the system for each national context. We summarize validation results, drought modelling challenges and how we overcame them through CDI improvements, and we describe the monthly CDI production process and outputs. Finally, we synthesize procedural and technical aspects of CDI development and reflect on the constraints we faced as well as trade-offs made to optimize the CDI for operational monitoring to support policy decision-making—including aspects of salience, credibility, and legitimacy—within each national context. [ABSTRACT FROM AUTHOR]

This study investigated the capability of remote sensing soil moisture (SM) datasets to estimate in-situ SM over the Lake Urmia Basin in Iran. A novel meta-estimating approach, called Voting Regression (VR), was used to combine the Gradient Boosting (GB) and Support Vector Regression (SVR) algorithms for developing a new hybrid predictive model named GB-SVR. Six SM products from the Global Land Data Assimilation System (GLDAS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) were used to predict SM at 40 in-situ SM sampling locations. The performance of the proposed novel forecasting technique was evaluated using Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results showed the superiority of GB-SVR compared to GB and SVR, with an average improvement of 17%, 10%, and 13% in CC, RMSE, and MAE, respectively, in predicting in-situ SM. The model performance in different climates, soil textures, and land covers showed its better prediction accuracy in croplands (R 2 = 0.86) , loam soil (R 2 = 0.74) and cold climate R 2 = 0.71 , while the least in clay soil and barren lands. Besides, the in-situ SM prediction using remote sensing SM data performed better than that obtained using in-situ air and soil temperature. The proposed methodology can be used for accurate SM prediction in regions lacking in-situ SM data. [ABSTRACT FROM AUTHOR]

Aboveground biomass is an important indicator used to characterize the growth status of crops, as well as an important physical and chemical parameter in agroecosystems. Aboveground biomass is an important basis for formulating management measures such as fertilization and irrigation. We selected four irrigated wheat fields in a region near Kaifeng, Henan Province, for this study. The terrain in that region was undulating and had spatial differences. We used a low-altitude unmanned aerial vehicle (UAV) remote sensing platform equipped with a multispectral camera, thermal infrared camera, and RGB camera to simultaneously obtain different remote sensing parameters during the key growth stages of wheat. Based on the extracted spectral reflectivity, thermal infrared temperature, and digital elevation information, we calculated the spatial variability of remote sensing parameters and growth indices under different terrain characteristics. We also analyzed the correlations between vegetation indices, temperature parameters, structural topographic parameters and aboveground biomass. Three machine learning methods were used, including the multiple linear regression method (MLR), partial least squares regression method (PLSR) and random forest regression method (RFR). We compared the aboveground biomass (AGB) estimation capability of single-modal data versus multimodal data fusion frameworks. The results showed that slope was an important factor affecting crop growth and aboveground biomass. We therefore analyzed several remote sensing parameters for three different slope scales. We found significant differences among them for soil water content, water content of plants, and aboveground biomass at four growth stages. Based on the strength of their correlation with aboveground biomass, seven vegetation indices (NDVI, GNDVI, NDRE, MSR, OSAVI, SAVI, and MCARI), four canopy structure parameters (CH, VF, CVM, SLOPE) and two temperature parameters (NRCT, CTD) were selected as the final input variables for the model. There was some variability in the accuracy of the models at different growth stages. The average accuracy of the models was anthesis stage > booting stage > filling stage > jointing stage. For the single-modal data framework, the model constructed with the vegetation indices was better than the aboveground biomass model constructed using the temperature or structure parameters, and the highest accuracy was obtained with an RFR model based on vegetation indices at the anthesis stage (R2 = 0.713). For the double modal data fusion approach, the highest accuracy resulted at the anthesis stage, using the structural parameters combined with the vegetation indices of the RFR model (R2 = 0.842). Even higher accuracies were obtained using the multimodal data fusion approach with an RFR model based on vegetation indices, temperature parameters and structure parameters at the anthesis stage (R2 = 0.897). By introducing terrain factors and combining them with the RFR algorithm to effectively integrate multimodal data, the complementary and synergistic effects between different remote sensing information sources could be fully exerted. The accuracy and stability of the aboveground biomass estimation models were effectively improved, and a high-throughput phenotype acquisition method was explored, which provides a reference and basis for real-time monitoring of crop growth and decoding the correlation between genotype and phenotype. [ABSTRACT FROM AUTHOR]

Soil moisture is the significant hydrologic factor deals with energy balance between the land and the atmosphere. Since the observation of soil moisture at point scale is infrequent and expensive, remote sensing determines the distribution of soil moisture in large scale. In this study, remote sensing techniques have been used to calculate the soil moisture index in the lower Bhavani river basin, Tamil Nadu, India. Landsat 8 satellite data were used for deriving soil moisture in reference with land surface temperature (LST) and normalized difference vegetative index (NDVI). The derived soil moisture was compared to the in situ soil moisture measurements, which were taken at 83 sites at the field level. When a linear regression model was fit between in situ observations and derived soil moisture, a high coefficient of determination (R2) value of 0.83 was found which can be efficiently used for the moisture estimation across greater areas. Since the land-use patterns influences the LST and soil moisture, the variations of these parameters in each land-use classes were studied using independent t test and found that LST demonstrated statistical non-significance (p > 0.05) for each of the studied groups, indicating that each land-use classes temperature were similar, whereas soil moisture in water bodies versus fallow land (p = 0.019), built-ups versus water bodies (p = 0.023), forest versus fallow land (p = 0.018), vegetation versus built-ups (p = 0.028), and built-ups versus forest (p = 0.011) has statistical significance value, which indicates that soil moisture between these compared classes was not similar. [ABSTRACT FROM AUTHOR]

In the background of the greenhouse effect, drought events occurred more frequently. How to monitor drought events scientifically and efficiently is very urgent at present. In this study, we employed the Vegetation Water Supply Index (VSWI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) as individual variables to construct a composite drought index (CDI) using spatial principal component analysis (SPCA). The validity of CDI was assessed using gross primary productivity (GPP), soil moisture (SM), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI). CDI was subsequently used for drought monitoring in northern China from 2011 to 2020. The results showed that (1) at a 99% confidence level, the Pearson correlation coefficients between CDI and GPP was 0.72, while the value between CDI and SM was 0.69, which indicated the relationship between SM, GPP, and CDI was significant. (2) We compared CDI with other variables such as Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Drought Index (CDI) and found that the monitoring result of CDI was more sensitive, which indicated that the proposed CDI had a better effect in local drought monitoring. (3) The results of CDI showed that the drought status in the northern region during 2011–2020 lasted from March to October, and the high severe drought period generally occurs in March-May and September-October, with low severe drought in June-August. [ABSTRACT FROM AUTHOR]

Soil erosion is an important global phenomenon that can cause many impacts, like morphometry and hydrology alteration, land degradation and landslides. Moreover, soil loss has a significant effect on agricultural production by removing the most valuable and productive top soil's profile, leading to a reduction in yields, which requires a high production budget. The detrimental impact of soil erosion has reached alarming levels due to the exacerbation of global warming and drought, particularly in the arid climates prevalent in Tunisia and Algeria and other regions of North Africa. The influence of these environmental factors has been especially evident in the catchment of Mellegue, where profound vegetation loss and drastic changes in land use and cover, including the expansion of urban areas and altered agricultural practices, have played a significant role in accelerating water-induced soil loss between 2002 and 2018. The ramifications of these developments on the fragile ecosystems of the region cannot be overlooked. Accordingly, this study aimed to compare soil losses between 2002 and 2018 in the catchment of Mellegue, which is a large cross-border basin commonly shared by Tunisian–Algerian countries. The assessment and mapping of soil erosion risk were carried out by employing the Revised Universal Soil Loss Equation (RUSLE). This widely recognised equation provided valuable insights into the potential for erosion. Additionally, changes in land use and land cover during the same period were thoroughly analysed to identify any factors that may have contributed to the observed risk. By integrating these various elements, a comprehensive understanding of soil erosion dynamics was achieved, facilitating informed decision-making for effective land management and conservation efforts. It requires diverse factors that are integrated into the erosion process, such as topography, soil erodibility, rainfall erosivity, anti-erosion cultivation practice and vegetation cover. The computation of the various equation factors was applied in a GIS environment, using ArcGIS desktop 10.4. The results show that the catchment has undergone significant soil water erosion where it exhibits the appearance of approximately 14,000 new areas vulnerable to erosion by water in 2018 compared to 2002. Average erosion risk has also increased from 1.58 t/ha/year in 2002 to 1.78 in 2018, leading to an increase in total estimated soil loss of 54,000 t/ha in 2018 compared to around 25,500 t/ha in 2002. Maps of erosion risk show that highly eroded areas are more frequent downstream of the basin. These maps can be helpful for decision-makers to make better sustainable management plans and for land use preservation. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) at the interface between the land surface and atmosphere is one of the major environmental parameters which plays an important role in hydrological applications. In this article, the SM measured by Soil Moisture Active Passive (SMAP) is downscaled from 3- to 1-km spatial resolution. The main purpose is to evaluate the performance of two different downscaling methods over a variety of climatic conditions and land cover types. These two methods, based on regression and artificial neural network (ANN), are used for enabling us to cross-validate and reliably interpret the obtained results since the number of ground measurements is not sufficient for accuracy assessment. These methods are applied over four different case studies; one is located in the USA, i.e., state of Utah (semi-arid), and the remaining three are located in Iran, i.e., Fars (arid and semi-arid), Yazd (hyper-arid), and Golestan (humid). In both methods, different combinations of input features correlated with SM including land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperatures in horizontal and vertical polarizations (TBH and TBV), shortwave infrared (SWIR), and digital elevation model (DEM) are used. It is found that the DEM does not add extra information in downscaling. The reason is due to high correlation between topography and LST. Moreover, SWIR is most likely able to model only large-scale variations of SM. The downscaled SM products are then compared to 1-km resolution SMAP SM extracted from Sentinel-1 for the study areas in Iran and in situ measurements in Utah. Both methods produce results which are considerably consistent except that the regression method adds more spatial details in the downscaled SM. The achievements illustrate that the performance of both downscaling methods is higher in areas with more homogeneous climatic conditions, i.e., Yazd and Golestan. The best evaluation metrics including correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) for Yazd and Golestan are R = 0.89, RMSE = 0.025 m3/m3, and MAPE = 21.13% and R = 0.93, RMSE = 0.044 m3/m3, and MAPE = 21.95%, respectively. Moreover, large model biases are associated with dense vegetated areas and high altitudes. The best downscaling accuracy in both methods over all study areas belongs to bare soil and flat regions. [ABSTRACT FROM AUTHOR]

Soil moisture is a critical parameter for land reclamation and ecological restoration in mining subsidence areas. However, limited research has been conducted into the impact of subsidence on soil moisture in semi-arid areas. In this study, the subsidence and soil moisture of four coal seams (4101 and 4102 coal seams are 2-year and 1-year subsidence area, 4103 and 4104 coal seams are just after mining and ready for mining) in semi-arid region were monitored for a year. A soil moisture retrieval method suitable for semi-arid mining areas was improved. The results showed that the reduced Bias, MRE and RMSE difference between satellite and in situ soil moisture to 0.129, 0.0851 and 0.0219, respectively, which demonstrated the reliability of the proposed soil moisture retrieval method in the semi-arid mining areas. We found that there was ecological self-restoration, the degree of spatial variability of soil moisture was gradually weaker with the increased subsidence time by comparing coal seams with different subsidence time. The average value of soil moisture in 4103 and 4104 coal seams decreased by 0.20% and 1.89%, respectively, whereas the coefficient of variation increased by 13.99% and 10.45%, respectively. Window cross-correlation analysis shows underground mining activities have a lag effect. The mean time lag between subsidence and the soil moisture response varied 90 days after the subsidence occurred. But, about 120 days after the end of mining, soil moisture gradually recovers. All these results indicated repair of land cracks in time at subsidence edge can prevent the decline of soil moisture. [ABSTRACT FROM AUTHOR]

The paper focuses on the mutual influence of moisture, salinity, and particle-size distribution on soil permittivity. The Agilent E8663B Analog Signal Generator is used for measurements in the frequency range of 8 to 18 GHz using the coaxial method. It is found that the maximum amount of bound water in soil depends on the soil type and is 15.9 and 13.7% for CuSO4 and NiSO4 35 mg/kg each, respectively. A 12% growth in the silt and clay content provides a 1.3% increase in the maximum amount of bound water in soil. [ABSTRACT FROM AUTHOR]

The article presents a revue of the estimates of soil surface moisture, soil water content, and evapotranspiration as elements of water and heat regimes of land surface areas at various spatial scales, made with the use of remote sensing data for Earth in various spectral ranges. In most cases considered in the study, such estimates were obtained with the use of land surface models. A special section is focused on the results of estimating soil surface moisture and water content using satellite data from microwave range, including radar data. Estimates of soil surface moisture content obtained with the use of neural networks are presented. A brief description is given to international hydrological–atmosphere experiments carried out under world research projects aimed to obtain data on the processes of moisture and heat exchange between the land surface and the surface atmosphere layer. Land surface, satellite, and model databases that have been formed using the results of studies in the field under consideration since the mid-1980s are reviewed. Prospects of further studies based on the development of new multispectral instrumentation, the creation of new databases, and the use of a new generation of satellites—global-coverage microsatellites with high-resolution sensors are presented. [ABSTRACT FROM AUTHOR]

In recent decades, the earth's surface data have been collected more efficiently using remote sensing, which needs drought indexes update. In this study, soil moisture (SM) data were collected from the surface layer of a high humidity climate in northern Iran using Soil Moisture Active Passive (SMAP) and field measurement. After analyzing the data, we found that the average RMSE between the field and SMAP measurement was 0.054 m 3 m 3 . Considering the same agricultural land use and the strong correlation of 0.92 between them, the validated SMAP data were used to propose an agricultural drought index. After data validation, the extreme learning machine (ELM) model was put to the test using sigmoid, triangular, sine, and hard-limit activation functions. Of all the activation functions tested, the model with the sigmoid activation function yielded the lowest amount of error and was therefore chosen. Five years of continuous daily SM as a target, five-year daily normalized difference vegetation index, land surface temperature, and precipitation were inputs to predict one-year daily SM time series in the humid climate. From 2021 to 2022, daily surface SM was predicted with the average RMSE = 0.03 m 3 m 3 compared to the SMAP data. Finally, a new regional agricultural drought index based on 4 years of SMAP and 1-year prediction of SMAP from 2022 to 2023 was proposed. Further investigation is needed to conclude that the application of the presented index is reliable in other climates. [ABSTRACT FROM AUTHOR]

Drought is a multifaceted natural hazard that has an impact on various aspects like; agricultural, hydrological, ecological and socio-economic systems. Drought has been a predominant concern for farmers in Gamo Zone over the last decades; hence, monitoring drought is important for soil conservation, water planning and management to mitigate impacts on agriculture in the Zone. The vegetation indices normal difference vegetation index and vegetation condition index are popular since they are based only on satellite images. The present study attempts to monitoring spatiotemporal distribution of agricultural droughts and its association with land surface temperature, precipitation and soil moisture from the period 2000–2020 using remote sensing application method in Gamo Zone. The vegetation condition index (VCI) and Normal difference vegetation index (NDVI) were used to evaluate spatial and temporal distribution of agricultural drought in Gamo Zone. The Pearson correlation method was used to identify Normal difference vegetation index association with land surface temperature, soil moisture and precipitation. The results discovered that severe drought to very severe drought was identified in 2000, 2002, 2004, 2008, 2009 and 2015 with the area coverage 39.7%, 28.8%, 33.4%, 24.5%, 61.3% and 23.0%, respectively. Similarly, the findings show that slight to mild droughts have a great chance of occurrence in Gamo Zone. The data also demonstrate that normal difference vegetation index has a strong relationship with land surface temperature (R = − 0.95), precipitation (R = 0.65) and soil moisture (R = 0.85), indicating that normal difference vegetation index is more sensitive to land surface temperature than soil moisture and precipitation, although soil moisture has a positive relationship with precipitation (R = 0.60). Therefore, this study revealed that normal difference vegetation index and vegetation condition index indices are suitable for agricultural drought monitoring and they are strongly associated with precipitation, soil moisture and land surface temperature. This study shall be helpful for decision makers to take the necessary measures by considering the drought risk maps for early warning system and future drought management plans. [ABSTRACT FROM AUTHOR]

An arid climate accompanied by a freshwater shortage plagued Egypt. It has resorted to groundwater reserves to meet the increasing water demands. Fossil aquifers were lately adopted as the sole water source to provide the irrigation water requirements of the ongoing reclamation activities in barren areas. Yet, the scarcity of measurements regarding the changes in the aquifers' storage poses a great challenge to such sustainable resource management. In this context, the Gravity Recovery and Climate Experiment (GRACE) mission enables a novel consistent approach to deriving aquifers' storage changes. In this study, the GRACE monthly solutions during the period 2003–2021 were utilized to estimate alterations in terrestrial water storage (TWS) throughout Egypt. Changes in groundwater storage (GWS) were inferred by subtracting soil water content, derived from the GLDAS-NOAH hydrological model, from the retrieved TWS. The secular trends in TWS and GWS were obtained using the linear least square method, while the non-parametric technique (Mann–Kendall's tau) was applied to check the trend significance. The derived changes in GWS showed that all aquifers are undergoing a significant loss rate in their storage. The average depletion rate over the Sinai Peninsula was estimated at 0.64 ± 0.03 cm/year, while the depletion rate over the Nile delta aquifer was 0.32 ± 0.03 cm/year. During the investigated period (2003–2021), the extracted groundwater quantity from the Nubian aquifer in the Western Desert is estimated at nearly 7.25 km3. The storage loss from the Moghra aquifer has significantly increased from 32 Mm3/year (2003–2009) to 262 Mm3/year (2015–2021). This reflects the aquifer exposure for extensive water pumping to irrigate newly cultivated lands. The derived findings on the aquifers' storage losses provide a vital source of information for the decision-makers to be employed for short- and long-term groundwater management. [ABSTRACT FROM AUTHOR]

The state of Rio de Janeiro has a diverse geography that is mainly composed of Atlantic Forest and mangrove formations. In recent decades, the state has undergone several changes in natural vegetation owing to human action, promoting the emergence of degraded areas that are more susceptible to the effects of climate change, which is proving to be a threat to human health and safety. Using 19 years of data from meteorological variables and remote sensing (2001–2020), the objective was to evaluate the dynamics of vegetation, and to understand the impacts of the climate and its previous and future trends (2021–2040) for the eight regions of government in the State of Rio de Janeiro. Our results identified the Serrana, Norte Fluminense, and Noroeste Fluminense regions as the most susceptible to vegetation degradation and increases in fires particularly during the dry period, which show a strong negative correlation between soil moisture and more intense El Niño events. The results simulated by the autoregressive integrated moving average (ARIMA) model warn of a reduction in rainfall and soil moisture mainly in the Northwestern Fluminense region. Based on these results, public policies should be implemented to mitigate the effects of anthropic actions in these regions, and prevent environmental degradation from becoming irreversible in the future. [ABSTRACT FROM AUTHOR]

North Africa is characterized by several ungauged basins, especially in Morrocco, where satellite products could be an alternative of the lack ground-based measurements. In this study, the Land Parameter Retrieval Model (LPRM) soil moisture and the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG) were used in flood modeling in Moroccan ungauged basins (Bourrous, Al Wiza, El Hallouf and Jamala). We started with comparing GPM-IMERG Early with ground measurements from five rain gauges. Next, the Soil Conservation Service – Curve Number (SCS-CN) was applied with ground precipitation measurements, LPRM and GPM-IMERG Early datasets to simulated flood events in a gauged basin, the Ghdat. Finally, this SCS-CN model was transposed with these satellite data sets validated to those ungauged basins in order to reproduce flood events. The results show that the GPM-IMERG Early is best with in situ measurements (correlation coefficient = 0.50; relative bias = 27.51%; probability of detection = 0.77; false alarm rate = 0.23), on a daily scale. The observed precipitation, LPRM and GPM-IMERG Early were performed well in validation to simulate floods in the Ghdat, where Nash–Sutcliffe criterion range from 0.43 and 0.98 using the SCS-CN model. For Bourrous, Al Wiza, El Hallouf and Jamala all flood events and hydrographs were reproduced by GPM-IMERG Early and LPRM products. Furthermore, LPRM products were validated against soil moisture measurements with a coefficient of determine R2 between 0.72 and 0.84. The results of this work provided interesting insights for flood modeling using GPM-IMERG and LPRM satellite products in ungauged basins. [ABSTRACT FROM AUTHOR]

Hydrologic extremes often involve a complex interplay of several processes. For example, flood events can have a cascade of impacts, such as saturated soils and suppressed vegetation growth. Accurate representation of such interconnected processes while accounting for associated triggering factors and subsequent impacts of flood events is difficult to achieve with conceptual hydrological models alone. In this study, we use the 2019 flood in the Northern Mississippi and Missouri Basins, which caused a series of hydrologic disturbances, as an example of such a flood event. This event began with above-average precipitation combined with anomalously high snowmelt in spring 2019. This series of anomalies resulted in above normal soil moisture that prevented crops from being planted over much of the corn belt region. In the present study, we demonstrate that incorporating remote sensing information within a hydrologic modeling system adds substantial value in representing the processes that lead to the 2019 flood event and the resulting agricultural disturbances. This remote sensing data infusion improves the accuracy of soil moisture and snowmelt estimates by up to 16% and 24%, respectively, and it also improves the representation of vegetation anomalies relative to the reference crop fraction anomalies. [ABSTRACT FROM AUTHOR]

Drought is the most widespread natural disaster in the world. How to monitor regional drought scientifically and accurately has become a hot topic for many scholars. In this paper, Geographically Integrated Dryness Index (GIDI) was integrated from an assortment source including Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI) (as the dependent variable) based on geographically weighted regression method. Besides, the comprehensive drought situation and changing trends in China from 2001 to 2019 were also examined. The results showed that (1) GIDI has excellent performance in monitoring medium- and long-term droughts and the monitoring results shows 2003, 2016, and 2019 were relatively wet years, while 2007, 2009, and 2011 were major drought years, and spring and March were the most frequent droughts season and month, respectively, and (2) except for the middle and upper reaches of the Yellow River and Northeastern China, which have a tendency to become wet, other places have a tendency to fluctuating dry. This study took advantage of simple and efficient methods to integrate existing indices to obtain a new index for monitoring a wider range of droughts, taking into account the physical mechanism of drought formation and the time scale of drought development, so it can scientifically evaluate the spatial and temporal distribution characteristics of drought and changes. [ABSTRACT FROM AUTHOR]

The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002–2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of − 0.80, − 0.58, − 0.43, and − 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of − 0.68, − 0.23, and − 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA–Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods. [ABSTRACT FROM AUTHOR]

Accurate estimations of actual crop evapotranspiration are of utmost importance to evaluate crop water requirements and to optimize water use efficiency. At this aim, coupling simple agro-hydrological models, such as the well-known FAO-56 model, with remote observations of the land surface could represent an easy-to-use tool to identify biophysical parameters of vegetation, such as the crop coefficient Kc under the actual field conditions and to estimate actual crop evapotranspiration. This paper intends, therefore, to propose an operational procedure to evaluate the spatio-temporal variability of Kc in a citrus orchard characterized by the sporadic presence of ground weeds, based on micro-meteorological measurements collected on-ground and vegetation indices (VIs) retrieved by the Sentinel-2 sensors. A non-linear Kc(VIs) relationship was identified after assuming that the sum of two VIs, such as the normalized difference vegetation index, NDVI, and the normalized difference water index, NDWI, is suitable to represent the spatio-temporal dynamics of the investigated environment, characterized by sparse vegetation and the sporadic presence of spontaneous but transpiring soil weeds, typical of winter seasons and/or periods following events wetting the soil surface. The Kc values obtained in each cell of the Sentinel-2 grid (10 m) were then used as input of the spatially distributed FAO-56 model to estimate the variability of actual evapotranspiration (ETa) and the other terms of water balance. The performance of the proposed procedure was finally evaluated by comparing the estimated average soil water content and actual crop evapotranspiration with the corresponding ones measured on-ground. The application of the FAO-56 model indicated that the estimated ETa were characterized by root-mean-square-error, RMSE, and mean bias-error, MBE, of 0.48 and -0.13 mm d−1 respectively, while the estimated soil water contents, SWC, were characterized by RMSE equal to 0.01 cm3 cm−3 and the absence of bias, then confirming that the suggested procedure can produce highly accurate results in terms of dynamics of soil water content and actual crop evapotranspiration under the investigated field conditions. [ABSTRACT FROM AUTHOR]

Utilising cosmic-ray neutron probes is a relatively new approach in obtaining larger area soil moisture and various operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. One such network located in the United Kingdom (UK) is the Cosmic-ray Soil Moisture Observing System, so-called COSMOS-UK, established in 2013. The present study aims at investigating the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed examining the agreement among the soil moisture data retrieved by the Time Domain Transmissometer (TDT) sensors and the corresponding estimates from the COSMOS-UK network station probe. The COSMOS-UK site footprint was compared using GPS-aided information from ground instrumentation, assisted by drone imagery acquisition and the implementation of geospatial interpolation methods in a Geographical Information System (GIS) environment. Altogether, this information was used for assessing the soil moisture footprint extent from the COSMOS-UK site. The COSMOS-UK station footprint was representative for an area shorter in size than the alleged footprint of 600 m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the TDT sensor probes installed in the area. Our study findings although concern specifically the studied experimental site contribute towards efforts aiming at assessing the COSMOS-UK soil moisture measurement footprint showcasing the added value of geospatial analysis in this direction. [ABSTRACT FROM AUTHOR]

Flooding is currently the most dangerous natural hazard. It can have heavy human and material impacts and, in recent years, flooding has tended to occur more frequently, due to changes our species has made to hydrological regimes, and due to climate change. It is of the utmost importance that new models are developed to predict and map flood susceptibility with high accuracy, to support decision-makers and planners in designing more effective flood management strategies. The objective of this study is the development of a new method based on state-of-the-art machine learning and remote sensing, namely random forest (RF), dingo optimization algorithm, a weighted chimp optimization algorithm (WChOA), and particle swarm optimization to build flood susceptibility maps in the Nghe An province of Vietnam. The CyGNSS system was used to collect soil moisture data to integrate into the susceptibility model. A total of 1650 flood locations and 14 conditioning factors were used to construct the model. These data were divided at a ratio of 60/20/20 to train, validate, and test the model, respectively. In addition, various statistical indices, namely root-mean-square error, receiver operation characteristic, mean absolute error, and the coefficient of determination (R2), were used to assess the performance of the model. The results for all the models were good, with an AUC value of + 0.9. The RF-WChOA model performed best, with an AUC value of 0.99. The proposed models can predict and map flood susceptibility with high accuracy. [ABSTRACT FROM AUTHOR]

Soil moisture content is one of the critical parameters in water resource studies and watershed management. Large-scale field measurement is a tough, time-consuming, and costly task. newly, models based on remote sensing indicators have found special importance with high accuracy to investigate the soil and water resources. This study aims to use Landsat 8 satellite imagery to study variation in topsoil moisture content of dust hotspots of southeastern Ahvaz of Iran, over the five months (from February to June 2019). After monthly field sampling, satellite images were applied to determine the NDMI index and the topsoil moisture content using Bands 5 and 6 (main Bands) of Landsat 8 satellites. Then, by fusion, with Band 8 (panchromatic Band), the soil moisture content map was obtained for each month. The Pearson correlation positive was obtained between the NDMI of the main band and the NDMI of fusion with the soil moisture content of field for April month, with a correlation coefficient 0.543, and a significance level of 0.05 (P-value < 0.05). Also, for each month, the humidity index modeling was obtained for both data (the main (original) band and the fusion band). The proposed model was evaluated using statistical metrics namely R2, RMSE and MAE to April month, and the results were 0.57, 1.25 and 5.45, respectively. After validating the models, the best ones were selected for estimating the soil moisture content. Finally, the obtained results showed that Landsat 8 data presented satisfying outcomes for estimating map soil moisture content. [ABSTRACT FROM AUTHOR]

Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. [ABSTRACT FROM AUTHOR]

Precision irrigation management requires operational monitoring of crop water status. However, there is still some controversy on how to account for crop water stress. To address this question, several physiological, several physiological metrics have been proposed, such as the leaf/stem water potentials, stomatal conductance, or sap flow. On the other hand, thermal remote sensing has been shown to be a promising tool for efficiently evaluating crop stress at adequate spatial and temporal scales, via the Crop Water Stress Index (CWSI), one of the most common indices used for assessing plant stress. CWSI relates the actual crop evapotranspiration ET (related to the canopy radiometric temperature) to the potential ET (or minimum crop temperature). However, remotely sensed surface temperature from satellite sensors includes a mixture of plant canopy and soil/substrate temperatures, while what is required for accurate crop stress detection is more related to canopy metrics, such as transpiration, as the latter one avoids the influence of soil/substrate in determining crop water status or stress. The Two-Source Energy Balance (TSEB) model is one of the most widely used and robust evapotranspiration model for remote sensing. It has the capability of partitioning ET into the crop transpiration and soil evaporation components, which is required for accurate crop water stress estimates. This study aims at evaluating different TSEB metrics related to its retrievals of actual ET, transpiration and stomatal conductance, to track crop water stress in a vineyard in California, part of the GRAPEX experiment. Four eddy covariance towers were deployed in a Variable Rate Irrigation system in a Merlot vineyard that was subject to different stress periods. In addition, root-zone soil moisture, stomatal conductance and leaf/stem water potential were collected as proxy for in situ crop water stress. Results showed that the most robust variable for tracking water stress was the TSEB derived leaf stomatal conductance, with the strongest correlation with both the measured root-zone soil moisture and stomatal conductance gas exchange measurements. In addition, these metrics showed a better ability in tracking stress when the observations are taken early after noon. [ABSTRACT FROM AUTHOR]

Efforts to apply gridded root-zone soil moisture (RZSM) products for irrigation decision-support in vineyards are currently hampered by the difficulty of obtaining RZSM products that meet required accuracy, resolution, and data latency requirements. In particular, the operational application of soil water balance modeling is complicated by the difficulty of obtaining accurate irrigation inputs and representing complex sub-surface water-flow processes within vineyards. Here, we discuss prospects for addressing these shortcomings using the Vineyard Data Assimilation (VIDA) system based on the assimilation of high-resolution (30-m) soil moisture information obtained from synthetic aperture radar and thermal-infrared (TIR) remote sensing into a one-dimensional soil water balance model. The VIDA system is tested retrospectively (2017–2020) for two vineyard sites in the California Central Valley that have been instrumented as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Results demonstrate that VIDA can generally capture daily temporal variations in RZSM for vertical depths of 30–60 cm beneath the vine row, and the assimilation of remote sensing products is shown to produce modest improvement in the temporal accuracy of VIDA RZSM estimates. However, results also reveal shortcomings in the ability of VIDA to correct biases in assumed irrigation applications—particularly during well-watered portions of the growing season when TIR-based evapotranspiration observations are not moisture limited and, therefore, decoupled from RZSM. Prospects for addressing these limitations and plans for the near-real-time operational application of the VIDA system are discussed. [ABSTRACT FROM AUTHOR]

High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at different scales is useful for a variety of agricultural, hydrologic, and climate applications. RZSM can be estimated using remote sensing, empirical equations, or process-based simulation models. Machine learning (ML) approaches for evaluating RZSM across numerous spatial–temporal scales are less generalizable than process-based models. However, data-driven ML approaches offer a unique opportunity to develop complex models of soil moisture without making assumptions about the processes governing soil water dynamics in a given study region. In this study, comparisons were made between two models, pySEBAL and EFSOIL, which were based on evaporation fraction (EF) and soil properties, and a data-driven model based on the Random Forest (RF) ensemble algorithm. These approaches were evaluated to demonstrate their capabilities for RZSM estimation. The EF obtained from Landsat images was used after validation with eddy covariance measurements as the major input to all three models, along with other meteorological and soil physical properties. The RF model was trained using in situ soil moisture data from Time Domain Reflectometry (TDR) sensors installed in a vineyard from 2018 to 2020. The predictor variables comprised of meteorological, soil properties, EF, and a vegetation index. The results reveal that there was a strong correlation between the in situ measured soil moisture and the RF predicted soil moisture at all sensor locations. Due to the complexity of the physical processes involved in soil water flow, the empirical models pySEBAL and EFSOIL were unable to reliably predict RZSM values at all monitored locations. The high RZSM predicted by pySEBAL demonstrated the presence of possible bias in the model's algorithm used to estimate soil moisture. We also demonstrated that ML based on the RF algorithm may be used to predict spatially distributed RZSM when a few soil moisture ground measurements are combined with remote sensing to produce soil moisture maps. [ABSTRACT FROM AUTHOR]

Soil moisture plays a critical role in agricultural productivity and water resource management, especially in a diverse and populous country like India. Understanding variations in soil moisture across different regions and seasons is essential for adapting agricultural practices and water management strategies to local conditions. This study examines changes in soil moisture levels across India, comparing contemporary data from 2023 with historical averages from 2000 to 2005 using advanced remote sensing and GIS technologies. The primary objective of this study is to identify Soil Moisture Anomalies (SMA) across India, quantify their impacts on agriculture and water resources, and provide recommendations for targeted management strategies. By comparing recent soil moisture data against historical averages, the study aims to highlight trends and changes that could influence future water resource planning and agricultural practices. The research utilizes data from the Famine Early Warning Systems Network’s (FEWS NET) i.e. Land Data Assimilation System (FLDAS), obtained from NASA’s data archives. The study employs a systematic approach to analyze seasonal variations in soil moisture across different Indian states. Soil moisture levels were analyzed using zonal statistics in GIS to classify regions into categories based on the degree of anomaly observed. This classification helped in understanding the spatial distribution of soil moisture during the pre-monsoon, monsoon, and post-monsoon seasons. The study found significant regional and seasonal variations in soil moisture across India. During the monsoon period, areas such as Bihar and Jharkhand consistently showed significant moisture deficits, indicative of drought conditions, affecting agricultural output and necessitating urgent water conservation measures. Conversely, regions like Punjab benefited from positive soil moisture anomalies, enhancing agricultural productivity. The pre-monsoon and post-monsoon seasons also showed variations, with some areas experiencing deficits requiring careful water management while others had surpluses that increased the risk of flooding. The analysis of SMA in India underscores the need for region-specific agricultural and water management strategies that consider significant variability in soil moisture conditions. The study highlights the importance of integrating soil moisture monitoring into national policy frameworks to enhance climate resilience and sustainable agricultural practices. Future research should focus on updating soil moisture assessments with more recent data and refining predictive models to improve the accuracy and effectiveness of water management and agricultural interventions. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a valuable tool for terrestrial remote sensing applications, particularly in the context of land Surface Soil Moisture (SSM) detection. The high-resolution capability of GNSS-R complements traditional satellite-based active and passive missions but the product reliability and robustness evaluations are still absent due to an efficient retrieval algorithms. In this study, we addressed this lack of reliability and robustness by comprehensively assessing the SSM retrievals from CYclone Global Navigation Satellite System (CYGNSS) data with the satellite-based microwave radiometry products Soil Moisture Active Passive (SMAP) and Modern Era Retrospective-Analysis for Research and Applications (MERRA2) over Punjab in various seasons. ERA5 model-based products for the same period in 2022–2023. Our study reveals a distinct seasonal average SSM variation during autumn (0.20 cm3/cm3), followed by winter values of 0.19 cm3/cm3. Subsequently, the minimum SSM values are observed during summer (0.11 cm3/cm3) and an increase in spring to 0.13 cm3/cm3. Moreover, a strong positive linear relationship (0.74) is evident between SMAP and ERRA 5 in contrast to a low correlation (0.03) between MERRA2 and both the SMAP and ERRA 5. Additionally, SMAP demonstrates moderate and weak correlation of 0.53 and 0.03 with CYGNSS and MERRA2, respectively. The CYGNSS exhibits moderate correlations (0.46) with ERRA 5 and SMAP and a weaker association (0.14) with MERRA2. Our analysis concluded that MERRA2 (Bias = 0.20 cm³/cm³, ubRMSD = 0.25 cm³/cm³, RMSE = 0.12 cm³/cm³, SD = 0.13 cm³/cm³, MAE = 0.04 cm³/cm, R = 0.03) SSM product performs poorly as compared to SMAP (Bias = 0.03 cm³/cm³, ubRMSD = 0.03 cm³/cm³, RMSE = 0.04 cm³/cm³, SD = 0.05 cm³/cm³, MAE = 0.03 cm³/cm³, R = 0.74) and CYGNSS (Bias = -0.01 cm³/cm³, ubRMSD = 0.09 cm³/cm³, RMSE = 0.07 cm³/cm³, SD = 0.06 cm³/cm³, MAE = 0.05 cm³/cm³, R = 0.46) products. This study provides accurate future predictions of SSM with delineating the limitations of GNSS-R in comparison to remote sensing and model values. The findings from this study have also significant implications for the advancement of GNSS-R applications in agriculture and crop management. [ABSTRACT FROM AUTHOR]

Remote sensing using passive solar illumination in the Short-Wave Infrared spectrum is exposed to strong intensity variation in the spectral bands due to atmospheric changing conditions and spectral absorption. More robust spectral analysis methods, insensitive to these effects, are increasingly required to improve the accuracy of the data analysis in the field and extend the use of the system to "non ideal" illumination condition. A computational hyperspectral image analysis method (named HIAM) for deriving optimal reflectance indices for use in remote sensing of soil moisture content is detailed and demonstrated. Using histogram analysis of hyperspectral images of wet and dry soil, contrast ratios and wavelength pairings were tested to find a suitable spectral index to recover soil moisture content. Measurements of local soil samples under laboratory and field conditions have been used to demonstrate the robustness of the index to varying lighting conditions, while publicly available databases have been used to test across a selection of soil classes. In both cases, the moisture was recovered with RMS error better than 5%. As the method is independent of material type, this method has the potential to also be applied across a variety of biological and man-made samples. [ABSTRACT FROM AUTHOR]

Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation ecosystems. The temperature-vegetation dryness index based on the triangle or trapezoid method has been used widely in previous studies. However, most existing studies simply used linear regression to construct empirical models to fit the edges of the feature space. This requires extensive data from a vast study area, and may lead to subjective results. In this study, a Modified Temperature-Vegetation Dryness Index (MTVDI) was used to monitor surface soil moisture status using MODIS (Moderate-resolution Imaging Spectroradiometer) remote sensing data, in which the dry edge conditions were determined at the pixel scale based on surface energy balance. The MTVDI was validated by field measurements at 30 sites for 10 d and compared with the Temperature-Vegetation Dryness Index (TVDI). The results showed that the R2 for MTVDI and soil moisture obviously improved (0.45 for TVDI, 0.69 for MTVDI). As for spatial changes, MTVDI can also better reflect the actual soil moisture condition than TVDI. As a result, MTVDI can be considered an effective method to monitor the spatio-temporal changes in surface soil moisture on a regional scale. [ABSTRACT FROM AUTHOR]

Drought as a complex event depends on some geo-environmental parameters. The impact of this phenomenon on the economy and environment can be devastating. Recognizing drought-prone regions is essential for planning and adopting mitigation measures. This study focused on introducing a multiscale method based on the satellite datasets to assess drought vulnerability in non-gage areas. In this regard, two steps were considered and drought monitoring was done for the northwest part of Iran as the study area. In the first step, vulnerable areas were identified using multi-criteria-kernel-based techniques which integrated 17 geo-environmental parameters extracted from the in-situ observations and satellite datasets to develop drought vulnerability map. The considered models were evaluated via different metrics and most important variables were determined by performing sensitivity analysis. Results indicated that the south and central parts of the selected area were more prone to severe droughts. Among the used variables, soil moisture, precipitation, evaporation, humidity, and vegetation condition were the most effective parameters. In the second step, due to limitations of the in-situ observations, drought-prone areas were identified using only precipitation, vegetation condition, and land surface temperature variables extracted from the satellites. For this aim, a new multi-scale method was developed based on the Wavelet Transform (WT), Variational Mode Decomposition (VMD), Permutation Entropy (HPE), and kernel-based methods. The obtained results showed the appropriate efficiency of the proposed multiscale method in identifying drought-prone areas with different intensity. [ABSTRACT FROM AUTHOR]

Drought imposes severe, long-term effects on global environments and ecosystems. A better understanding of how long it takes a region to recover to pre-drought conditions after drought is essential for addressing future ecology risks. In this study, drought-related variables were obtained using remote sensing and reanalysis products for 2003 to 2016. The meteorological drought index [standardized precipitation evapotranspiration index (SPEI)] and agricultural drought index [vegetation condition index (VCI)] were employed to estimate drought duration time (DDT) and drought recovery time (DRT). To the basin's west, decreasing rainfall and increasing potential evapotranspiration led to decreasing SPEI. On the east side, decreasing soil moisture from each depth effects vegetation condition, which results in a decreasing gross primary productivity and VCI. Extreme meteorological drought events are likely to occur in the basin's northeastern and middle western areas, while the southern basin is more likely to suffer from extreme agricultural drought events. The mean SPEI-based DDT (2.45 months) was smaller than the VCI-based DDT (2.97 months); the average SPEI-based DRT (2.02 months) was larger than the VCI-based DRT (1.63 months). Most of the area needs 1 or 2 months to recover from drought except for the basin's northwestern area, where the DRT is more than 8 months. DDT is the most important parameter in determining DRT. These results provide useful information about regional drought recovery that will help local governments looking to mitigate potential environmental risks and formulate appropriate agricultural policies in Lake Victoria Basin. [ABSTRACT FROM AUTHOR]

This study examines the trends in MODIS/TERRA derived Normalized Difference Vegetation Index (NDVI) and its correlation with Land Surface Temperature (LST), Soil Moisture (SM), and precipitation over Gautam Buddh Nagar (India), during the period 2005–2018. The region have a sub-humid and quite moderate climate, scattered into cultivable land, forest and fast growing urbanization zone, making it suitable for monitoring vegetation trends and its accompanying factors. The NDVI-derived vegetation growth patterns over the study region of District Gautam Buddh Nagar, illustrate vigorous seasonal cycles, and interannual variations. The correlation between NDVI, and LST (− 0.45) was observed to be higher than the correlation of NDVI with SM (r = 0.43), and precipitation (r = 0.341), suggesting NDVI as more sensitive to LST as compare to SM, and precipitation, while SM shows the worthy positive correlation (r = 0.63) with the precipitation. On a seasonal basis, NDVI shows high values during winter (0.45 ± 0.02) followed by monsoon (0.44 ± 0.04), post-monsoon (0.41 ± 0.02), and pre-monsoon (0.37 ± 0.04). This study also aims to determine the phase wise status of NDVI and associated parameters. [ABSTRACT FROM AUTHOR]

The lack of observed streamflow datasets for calibration of rainfall-runoff models imposes substantial problems for their applicability, especially in poorly gauged or ungauged river basins. Developing satellite technologies and increasing computational powers over the past decades, have provided an environment for researchers to simulate several water balance components globally using these datasets and assimilation techniques. Due to importance of accurate hydrologic modeling, this study aims to investigate the applicability of global water resources reanalysis (GWRR) datasets including surface soil moisture (SSM), evapotranspiration (ET), and surface runoff (SR) components for calibration of the macro-scale hydrological model (VIC-3L) over the SefidRood basin (SRB) in Iran at different calibration scenarios. Results show that in the case of using SSM datasets, the model's performance in the simulation of streamflow hydrograph, with the NSE value higher than 0.65, is better than using other datasets. Among different datasets, the SSM based on LISFLOOD and HBV are the best ones for calibration of VIC-3L model over SRB. In contrast, using ET datasets aren't so reliable for hydrological calibration in the study area. Furthermore, in the cases of using SSM and surface runoff datasets, the model tends to overestimation of low-flows, while, ET datasets are more reliable for simulation of such these flows. Also, findings displayed that the combination of ET and SSM datasets for hydrological calibration performed better than using only one dataset. In conclusion, this research gives useful and applied insights in the applicability of GWRR data sources for hydrological modeling and water resources studies, especially in data limited regions. [ABSTRACT FROM AUTHOR]

Frequent droughts can cause severe losses to economy, society, and environment. This study aims to develop a remote sensing-based drought monitoring and information management system (RDMIMS) to help managers to better understand the regional drought situation and define quick and responsive measures. RDMIMS includes four modules for data management, drought monitoring and analysis, business operation, and information inquiry. Furthermore, RDMIMS combines the ground observation data of soil moisture content with the remote sensing inversion parameters to obtain regional drought information with increased accuracy. It provides users with more flexible operation methods that can obtain more accurate monitoring results by changing the weight coefficients of each monitoring model. Additionally, the business operation mode can provide monitoring results by "single click" thus enabling the managers to have more choices under different requirements. The results showed that the system can effectively monitor the drought situation in the study area and can meet the needs and expectations of manager's for information inquiry. [ABSTRACT FROM AUTHOR]

The Pingluo area, as an experimental study area in Yinchuan, has been subjected to major environmental degradation due to soil salinization problems. Soil salinization is one of the main problems of land degradation in arid and semiarid regions. In the present study, remote sensing was integrated with mathematical modeling to evaluate soil salinization adequately. To detect soil salinization, soil water content and electrical conductivity of soil samples were analyzed. The reflectance of soil samples was measured using a spectrometer (SR-3500) with 1024 bands. Indices of soil salinity, vegetation and drought were analyzed using Landsat images over the study area. Based on Landsat images, physicochemical analysis, reflectance of sensitive bands for soil salinization and environmental indices, canopy response salinity index (CRSI), perpendicular drought index (PDI) and enhanced normalized difference vegetation index (ENDVI), a new model was established for simulation and prediction of soil salinization in the study area. Correlation analyses and multiple regression methods were used to construct an accurate model. The results showed that green, blue and near-infrared light was significantly correlated with soil salinity and that the spectral parameters improved this correlation significantly. Therefore, the model was more effective when combining spectral parameters with sensitive bands with modeling. After mathematical transformation of soil reflectance, the correlations of bands sensitive to soil salinization were 0.739 and 0.7 for electrical conductivity and water content, respectively. After transformation of vegetation reflectance, the correlation coefficient of soil salinity became 0.577. After inversion of the model based on soil hyperspectral and water content, the significance became 0.871 and 0.726, respectively, which can be used to predict soil salinity and water content. The spectral soil salinity model had a coefficient of 0.739 for soil salinity prediction. Among the salinity indices, the CRSI was selected as the most significant, with R2 of 0.571, whereas the R2 for PDI reached only 0.484. Among the vegetation indices, the ENDVI had the highest response to soil salinity, with R2 of 0.577. After scale conversion, the correlation percentages between CRSI and measured soil salinity and between ENDVI and measured soil salinity increased to 16.2% and 8.5%, respectively. Following the correlation between PDI and soil water content, the percentage of correlation increased to 11.6%. The integration of hyperspectral remote sensing, ground methods and an inversion method for salinity is a very important and effective technique for rapid and nondestructive monitoring of soil salinization. [ABSTRACT FROM AUTHOR]

Sound management of native forests used for cattle grazing requires understanding the dynamics of forage productivity in the openings. Despite their importance, forage productivity drivers in highly heterogeneous forested landscapes, or their variability over the year, are still unclear. The aim of this work is to find predictors of Normalized Difference Vegetation Index (NDVI) variation in the openings of native temperate forests and to evaluate how these predictors change within the growing season. We used high spatial resolution remote sensing imagery from NW Patagonia to separate forest openings from tree dense canopy. We obtained data of each opening related with herbaceous and shrub forage productivity and calculated landscape metrics. We estimated a multiple linear regression model for predicting NDVI in each season. Beyond known variables related with forage productivity (altitude, precipitation, etc.), the shape of forest' openings appeared as relevant in predicting NDVI. Higher values of forest opening perimeters were related with a decrease in NDVI in spring when soil water content is not limiting and conversely with an increase in NDVI in summer when water is limiting growth. These results suggest that environmental drivers such as temperature and soil moisture inside the opening, and competition or facilitation process between trees and grasses are mediated by the shape of the opening. Management of heterogeneous native forests for cattle raising requires considering the shape of the openings to maximize forage productivity. [ABSTRACT FROM AUTHOR]

Remote sensing was used to visualize the West region with the purpose of investigating recent natural hazards observed in this area. Various approaches used based on multi-spectral information such as PCA, band compositions, NDVI, and texture analysis are used to investigate landslide and new faults in the region of West Cameroon. The combination of remote sensing techniques with fieldwork and photogrammetry has been probed again and helped to detect three (03) new faults and to characterise the recently assumed landslide as a rock fall characterised by an accumulation of fresh rock deposits downwards, from the edge to the valley. In this study, we extracted in this region all linear and curvilinear edges resulted from geomorphological features, geological structures, rock composition, vegetal tonal contrasts, soil moisture, and human activities. These features are terraces, linear valleys and cliffs, faults, joints, and other line weaknesses, tracks, buildings, roads and rivers. They are overlain on rivers, highways and digital elevation model maps for preliminary validation. Then, a post-processing fieldwork has been conducted to avoid any confusion with faults. The digital elevation model and the derived slope maps enabled fracturing to be discerned. Existing known faults have also been identified in this study. [ABSTRACT FROM AUTHOR]

In this study, a novel approach defined for drought severity of Bundelkhand region of India, especially for rabi (winter) season crops using analytical hierarchy process established a Multi-Criteria Decision-Making approach based combined drought index (CDI). The remote sensing multi-sensor derived datasets, as long-term MODIS satellite data, rainfall from CHIRPS datasets and soil moisture from Era Interim datasets, used for quantification for the 2001–2018 period. The CDI is generated by integrated multi-spectral indices as Vegetation Condition Index, Temperature Condition Index, Precipitation Condition Index, Soil Moisture Condition Index using analytical hierarchy process derived weightage with a consistency ratio of 4.2% and consistency index of 0.038 value. The results show that Jalaun, Hamirpur and Banda districts as Northern part affected more as compared to Lalitpur and parts of Jhansi districts as the Southern part of the study area. The statistical analysis illustrated a significant correlation between crop yield anomaly and CDI for all districts for rabi pulses crops. Thus, a geospatial platform-based approach using historical earth observations with analytical hierarchy process integrated expert advice to finalize variables and their weighing will make this methodology more realistic, easier and quicker to apply in future at any region. Eventually, remote sensing can address the drought risk or severity for all kind of crop ecosystem by using cohesive approach derived from multi-sensor satellite datasets. [ABSTRACT FROM AUTHOR]

Coal mining provides energy to support the development of society. However, mining affects the environment. Thus, substantial attention has been placed on environmental change in mining areas. Research assessing the environmental change in arid and semiarid mining areas has mainly been conducted by analyzing the variation in a single factor. However, the impact of coal mining on the environment is manifold, and there is no comprehensive assessment method. In this paper, we used a remote sensing index referred to as the comprehensive evaluation index (CEI) to comprehensively assess the environmental changes in arid and semiarid mining areas. The CEI in this study was calculated by integrating the changes in vegetation coverage (VC), land surface temperature (LST), and soil moisture (SM) using long time-series Landsat images. The Shendong mining area located in Northwest China was the study area. The results indicate that the environment of the Shendong mining area improved overall from 2000 to 2018. Environmentally degraded areas accounted for approximately 7.59% of the mining area and were concentrated in areas damaged by coal mining. [ABSTRACT FROM AUTHOR]

Space-borne measurements of atmospheric greenhouse gas concentrations provide global observation constraints for top-down estimates of surface carbon flux. Here, the first estimates of the global distribution of carbon surface fluxes inferred from dry-air CO2 column (XCO2) measurements by the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) are presented. An ensemble transform Kalman filter (ETKF) data assimilation system coupled with the GEOS-Chem global chemistry transport model is used to optimally fit model simulations with the TanSat XCO2 observations, which were retrieved using the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS). High posterior error reduction (30%–50%) compared with a priori fluxes indicates that assimilating satellite XCO2 measurements provides highly effective constraints on global carbon flux estimation. Their impacts are also highlighted by significant spatiotemporal shifts in flux patterns over regions critical to the global carbon budget, such as tropical South America and China. An integrated global land carbon net flux of 6.71 ± 0.76 Gt C yr−1 over 12 months (May 2017–April 2018) is estimated from the TanSat XCO2 data, which is generally consistent with other inversions based on satellite data, such as the JAXA GOSAT and NASA OCO-2 XCO2 retrievals. However, discrepancies were found in some regional flux estimates, particularly over the Southern Hemisphere, where there may still be uncorrected bias between satellite measurements due to the lack of independent reference observations. The results of this study provide the groundwork for further studies using current or future TanSat XCO2 data together with other surface-based and space-borne measurements to quantify biosphere-atmosphere carbon exchange. [ABSTRACT FROM AUTHOR]

The research was aimed at the analysis of relationship between the soil surface spectral reflectance and the moisture content in soil samples as the basis for moisture detection based on remote sensing data. By the example of nine samples from the arable horizons of podzolized chernozem, gray forest and soddy-podzolic soils analyzed in laboratory, the relationship was assessed between the spectral reflectance of soil surface in the visible spectral band detected with the HandHeld-2 spectroradiometer and the moisture content in samples. It was found that changes in the soil moisture content induce synchronous changes in the integral reflection in the visible spectral band only in a rather narrow interval of moisture, being specific for different soils. Variation in soil moisture content beyond these intervals does not change the spectral reflectance of soil surface. The results obtained prove that most of the satellite survey data in the optical range register the dry state of open soil surface, though the arable horizon proper may be rather moist. These regularities should be taken into account when developing satellite techniques for prompt monitoring of soil moisture by the remote sensing data obtained within the optical range of electromagnetic waves. [ABSTRACT FROM AUTHOR]

Soil moisture assessment on production land is gaining more attention as one of the critical factors and had a remarkable impact on agriculture production, life as well as global warming. Bibliometric analysis is performed by extracting datasets from SCOPUS from 2000 to 2020 to analyse soil moisture using remote sensing study and progress in the last two decades. The outcome indicates that study on the development of soil moisture monitoring tools using remote sensing has been increasing especially for international cooperation articles. International Geoscience and Remote Sensing Symposium (IGARSS) recorded the most productive journal published articles in this field. Among the top active countries that produce most articles were the USA followed by China. The current keywords search on soil mechanism and satellite technology frequently searched in this field. Global issues that focus on the relationship between soil moisture and environmental forecast such as drought, climate change and global warming by using remote sensing technology needed more high impact research outputs in the future. [ABSTRACT FROM AUTHOR]

Among the Iranian plains which have experienced land subsidence, there is an indicator, in addition to its high rate of subsidence, soil salinity, and proximity to the centers of population, transportation routes, and infrastructures in Varamin plain, which has raised serious concerns about this center of land subsidence. Excessive use of groundwater and aquifers and lack of optimal cultivation patterns decrease soil fluidity, leading to land subsidence, increased soil salinity, and reduced surface moisture and soil moisture to a depth of 1–2 m. The subsidence phenomenon also disrupts water storage in underground aquifers, making it difficult to reduce the volume of the reservoir and break the soil layers and fill the pores of the water sediment into the lower layers, and store water in the reservoirs. It cannot reach the underground, which is one of the main reasons for the decrease in soil moisture and the increase in soil salinity. In this study, the changes in the land subsidence during the 2014–2020 time period, were evaluated by Sentinel-1 satellite imagery and SNAP software. The soil electrical conductivity, soil surface moisture content, and soil moisture content in the depth of 1–2 m were calculated by Sentinel-2 satellite imagery, Landsat 8 satellite, and FLDAS data in the GEE platform, respectively. The results showed that the increase in land subsidence has raised soil salinity covering area and its intensity. As a result, moisture reduction is observed in different depths in recent years. [ABSTRACT FROM AUTHOR]

Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13A3, TRMM 3B43, and SRTMDEM, for Yunnan Province, China from 2009 to 2018 to calculate the tropical rainfall condition index (TRCI), vegetation condition index (VCI), temperature condition index (TCI), and elevation factors. Principal component analysis (PCA) and analytic hierarchy process (AHP) were used to construct comprehensive drought monitoring models for Yunnan Province. The reliability of the models was verified, following which the drought situation in Yunnan Province for the past ten years was analysed. The results showed that: (1) The comprehensive drought index (CDI) had a high correlation with the standardized precipitation index, standardized precipitation evapotranspiration index, temperature vegetation dryness index, and CLDAS (China Meteorological Administration land data assimilation system), indicating that the CDI was a strong indicator of drought through meteorological, remote sensing and soil moisture monitoring. (2) The droughts from 2009 to 2018 showed generally consistent spatiotemporal changes. Droughts occurred in most parts of the province, with an average drought frequency of 29% and four drought-prone centres. (3) Monthly drought coverage during 2009 to 2014 exceeded that over 2015 to 2018. January had the largest average drought coverage over the study period (61.92%). Droughts at most stations during the remaining months except for October exhibited a weakening trend (slope > 0). The CDI provides a novel approach for drought monitoring in areas with complex terrain such as Yunnan Province. [ABSTRACT FROM AUTHOR]