The scarcity of surface water and limited availability of renewable groundwater, coupled with its significant use for irrigation, raises critical concerns for the future management of water resources in Saudi Arabia. Groundwater serves as the primary freshwater resource in the country, with its utilization expanding to meet growing demands, particularly in the agricultural sector. However, due to the lack of in-situ observations, accurately assessing the status of groundwater resources remains a significant challenge. Remote sensing platforms offer a valuable solution by providing global estimates of various water components, including groundwater storage (GWS), evapotranspiration (ET), and precipitation.
This study leverages the Gravity Recovery and Climate Experiment (GRACE) satellite to estimate variations in GWS. The Surface Energy Balance Algorithm for Land (SEBAL) is applied to calculate agricultural water consumption via ET, while the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) algorithm is used to derive monthly precipitation values. The case study focuses on three regions known for their date crop cultivation: Madinah, Al Qassim, and Hofuf.
The results reveal an average annual decline in GWS of -6.7, -10.9, and -3.8 mm/year for the respective regions. The annual precipitation rates are 82.1, 99.8, and 101.2 mm/year, while the estimated ET for date crops is approximately 1940, 1489, and 2126 mm/year, respectively. These findings highlight a noticeable downward trend in GWS, underscoring the impact of intensive irrigation practices, as indicated by the high ET values, and the role of climate change, as evidenced by the low precipitation rates.
How to cite: Badaoud, A., Walsh, C., and O'Donnell, G.: Assessing the Impact of Date Production on Groundwater Resources Using Remote Sensing: A Case Study from Saudi Arabia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4265, https://doi.org/10.5194/egusphere-egu25-4265, 2025.
Evapotranspiration (ET) knowledge is crucial for evaluating crop field water budgets and agricultural water resources management. To monitor crop water requirements various data sources are used such as: in situ (meteorological and soil water content data) measurements, reanalysis database, remote sensing observations, and models. Two approaches can be implemented: the Soil Water Balance (SWB) and the Surface Energy Balance (SEB).
This research aimed to evaluate these two approaches, by combining in situ or reanalysis meteorological data with remotely sensed images to explore the possible synergies between the approaches to propose an operational ET estimation in the context of future Thermal InfraRed (TIR) missions (TRISHNA and LSTM). With a SWB model, both actual evapotranspiration (ETa) and soil water content (SWC) were daily estimated; whereas, with a SEB model latent heat flux (LE) was instantaneously evaluated.
Among the available SWBs, the SAtellite Montoring for Irrigation (SAMIR) is a FAO-2Kc-based model integrating remotely sensed images of vegetation cover for evapotranspiration spatialization and water balance. SAMIR can be forced by irrigation either measured or simulated employing specific rules based on the simulated SWC. Alternatively, the Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) is a two-source SEB model driven by remotely sensed Land Surface Temperature (LST) and vegetation cover. Both SWB and SEB were investigated by using different input variable combinations. For SAMIR, two combinations were employed: a) using in situ and b) using ERA5-Land reanalysis meteorological variables to estimate crop reference evapotranspiration and precipitation depth. Both incorporated farmer irrigation scheduling and Sentinel-2 NDVI-derived vegetation cover. For SPARSE, three combinations were employed: a) using in situ meteorological data, LST, and albedo; b) replacing LST and albedo with Landsat-8/9 data; c) replacing in situ data with ERA5-Land reanalysis while maintaining Landsat-8/9 inputs.
The experiments occurred during seven irrigation seasons, from 2018 to 2024, in a Mediterranean citrus orchard (Citrus reticulata Blanco cv. Mandarino Tardivo di Ciaculli), located near Palermo, Italy (38° 4’ 53.4’’ N, 13° 25’ 8.2’’ E) in which different irrigation systems and management strategies were applied. The field was equipped with a standard weather station, an Eddy Covariance tower, and four “drill and drop” probes to acquire: meteorological variables, energy fluxes, and SWC, respectively.
SAMIR best performance was obtained using the a-combination with Root Mean Square Error (RMSE) always less than 0.54 mm d-1 and 0.02 cm3 cm-3 for ETa and SWC, respectively. These metrics were achieved excluding data from 2021 during which worse metrics (ETa RMSE equal to 0.87 mm d-1) were probably caused by the presence of weeds due to the lack of maintenance provided by the farmer. SPARSE best performance was obtained using a-combination with LE RMSE equal to 53 W m-2. Noticeably, b- and c- combinations were implemented using a limited number of data (contextually to satellites acquisitions) thus achieving worse metrics (RMSE equal to 66 W m-2 and 93 W m-2 for b- and c- combinations, respectively).
Satisfactory results gained permit this work to keep on being updated toward the synergies between the approaches for better ET estimation.
How to cite: De Caro, D., Merlin, O., Rivalland, V., Simonneaux, V., Ippolito, M., Capodici, F., Cammalleri, C., and Ciraolo, G.: Estimation of citrus water requirements by means of water and energy balance models driven by in situ, reanalysis and remote sensing data , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5181, https://doi.org/10.5194/egusphere-egu25-5181, 2025.
In many peri urban areas of Africa, vegetable and fruits crops are expanding rapidly thanks to a fast growing market and an attractive profitability for farmers. These productions mainly grown in off season need irrigation, and water availability is then the main limiting factor of their development. In Burkina Faso a very large number of small scale dams have been built since the dry spells of years 1980 to store surface water and support irrigation. Question arise about the environmental and productive impacts of these dams as many seem under utilized. Groundwater, combined with the multiplication of individual wells and recent availability of solar powered-pumps is increasingly used for irrigation and seems a major alternative source of water. This study focuses on the off season (November – May) cropping conditions of 4 main sites of the Ouagadougou vegetable gardening belt, with comparison of two situations of farmer led development: (i) an area surrounding a small reservoir/dam, (ii) an area of lowland without reservoir. A remote sensing analysis combining a very high spatial resolution coverage (Pléiades, 0.5 m) and Sentinel 2 time series (5 days revisit frequency at 10 m spatial resolution) is performed for mapping irrigated crop area and its monthly temporal dynamics allowing the identification of multiple crop cycles during the dry season. Coupled with climatic water balance data and remotely sensed detection of surface water availability upstream of dams, this analysis highlights the role of small dams and individual wells on groundwater availabilty and extension of the cropping period.
How to cite: Fusillier, J.-L., Lebourgeois, V., Madec, S., Poda, R., and Barbier, B.: Satellite monitoring of the spatio-temporal dynamics of irrigated market garden crops in relation to collective and individual water hydraulic development : Case study of the Ouagadougou (Burkina Faso) vegetable gardening belt , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21742, https://doi.org/10.5194/egusphere-egu25-21742, 2025.
Irrigated agriculture accounts for over 70% of global water consumption, with rice being the most significant irrigated crop in Uruguay, covering 140,000 to 160,000 hectares annually. Approximately half of the irrigation water comes from reservoirs, while the remainder is pumped from rivers and lagoons. However, continuous monitoring of water volumes and flows in irrigation systems is constrained by the high costs of traditional methods, limiting water use planning, efficiency improvements, and equitable water distribution.
Satellite imagery has emerged as a cost-effective tool for natural resource monitoring. Since 2010, platforms like Google Earth Engine have provided free access to geospatial data, enabling environmental analysis without the need for advanced software or hardware. Sentinel-2 (S2) is part of the European Union’s Copernicus Earth Observation program. These satellites are equipped with multiband passive sensors offering 10-30m spatial resolution and a 5-day revisit period, allow the calculation of water indexes like NDWI and MNDWI to measure water surfaces and estimate volumes. However, their performance is influenced by climatic and atmospheric conditions. Sentinel-1 (S1) satellites, with radar sensors providing 10m spatial resolution and a 6-day revisit period, offer all-weather, day-and-night monitoring.
This study was conducted between 2018 and 2024 focused on the "India Muerta" reservoir in Uruguay, using S2 and S1 imagery processed via Google Earth Engine through Google Colab Python scripts. Water surfaces were generated at 20 cm intervals based on the reservoir's digital elevation model and field sensor data, creating a multiband raster.
For S2 image collection, a filter of at least 80% cloud-free coverage was used, applying additional filtering to ensure 70% cloud-shadow-free pixels over the area of interest. NDWI thresholds (-0.4 to 0.4) were tested to minimize errors and improve accuracy, while S1 imagery used Otsu algorithm to fit the most accurate reflectance thresholds for water detection.
The results showed that variable S2 NDWI thresholds outperformed the S1 Otsu-based detection method, achieving higher accuracy (R² = 0.88 vs. 0.77), lower mean absolute error (MAE = 7.92 vs. 13.43), and lower root mean square error (RMSE = 12.76 vs. 17.15). These findings highlight the benefits of adaptive NDWI thresholds for accurately estimating inundated areas and water volumes compared to radar-based methods.
Satellite-based reservoir monitoring provides critical data for both policymakers and farmers. For governments, it facilitates the identification and planning of reservoirs, ensuring equitable water use. For farmers, it offers a reliable tool for optimizing irrigation and improving water management. Furthermore, it helps managing irrigation shortages and addresses water scarcity challenges in present and future irrigated agriculture. This approach represents a cost-effective alternative to traditional monitoring methods, bridging the gap in continuous water resource management in many regions.
How to cite: Campos, F., Fuentes, I., Ernst, F., and Navas, R.: Satellite-Based Reservoir Water Monitoring for Irrigated Agriculture in Uruguay, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9620, https://doi.org/10.5194/egusphere-egu25-9620, 2025.
Irrigation is a critical component of global agriculture, supporting 40% of food production on 22% of cultivated land. As climate change intensifies, the demand for irrigation water is expected to rise, particularly in vulnerable regions like the Mediterranean basin.
This study presents the implementation and performance of a Spatial Decision Support System (SDSS) developed under the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project, funded by the Italian Space Agency, designed to forecast irrigation needs in semi-arid Mediterranean environments.
The THETIS SDSS aims to provide irrigation forecasts at a basin scale, focusing on three critical stages: early, at the beginning, and during the summer season. The early stage is crucial for assessing water availability and managing irrigation efficiently. THETIS integrates a hydrological model (HM) and a crop growth model (CGM), leveraging Earth Observation (EO) data and artificial intelligence (AI) techniques to spatialize forecasted meteorological and climatic data.
The SDSS combines soil water balance at two spatial scales. At the basin scale, the HM, calibrated with daily streamflow data, reliably reproduces soil moisture dynamics. At the district scale, the CGM, initialized by the HM, better models water dynamics at the local scale, accounting for factors like rain, irrigation, transpiration, evaporation, and drainage.
The HM estimates soil water content at the beginning of the crop growing period, provided by the DREAM hydrological model. The CGM, based on AquaCrop and initialized by the HM, simulates crop development and forecasts evapotranspiration and irrigation needs based on meteorological forcing, hydrologic, and EO-derived information. Forecasted meteorological and climatic data are obtained from the C3S Copernicus Service. CGM outputs are early forecast water demand maps (m³/ha) at the field scale, refined as the cropping season progresses.
The EO-derived information used in THETIS comes from both Synthetic Aperture Radar data (e.g., Sentinel-1, COSMO-SkyMed, SAOCOM) and optical data (e.g., Sentinel-2 and hyperspectral PRISMA). The obtained information includes maps of tilled fields , which, combined with historical land use information based on crop rotation, provide an initial estimate of irrigated areas. Maps of surface soil moisture and derived irrigated/non-irrigated fields refine the localization of irrigated areas after sowing, while vegetation index maps are used during the season for identifying sowing dates.
The system has been set up over the Fortore irrigation district in the Apulian Tavoliere, Foggia, Italy, managed by the Reclamation Consortium of Capitanata, covering an area of 141 km². The SDSS performance was evaluated on tomato crops, focusing on cultivated area identification and water consumption. First results obtained for the 2022 irrigation season indicate that the water consumption of 600 m³/ha, estimated early by the THETIS SDSS using tillage change maps, is comparable to the measured value of 500 m³/ha, considering that additional water volumes from groundwater sources were likely used. The application of THETIS to the 2023 and 2024 seasons is in progress.
Acknowledgment: THETIS is funded by ASI under the Agreement N. 2023-52-HH.0 in the framework of ASI’s program “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE).
How to cite: Satalino, G. and the THETIS team: Leveraging Earth Observation for Accurate Early Forecasting of Irrigation Needs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13700, https://doi.org/10.5194/egusphere-egu25-13700, 2025.
Abstract：Maize is an essential grain crop in China, playing a crucial role in safeguarding in national food security. However, the increasing instability of the maize cultivation environment caused by global climate change, along with various adverse stress factors, presents significant challenges to maintaining yield stability. Effective monitoring of maize phenology under stress conditions is crucial for optimizing agricultural management and mitigating yield losses. This study proposes an innovative phenological monitoring model utilizing near-ground remote sensing technology. High-resolution imagery of maize fields was collected using unmanned aerial vehicles (UAVs) equipped with multispectral and thermal infrared cameras. By integrating these datasets with Convolutional Neural Network (CNN) and Transformer, the study developed a robust and efficient model that fuses multispectral, thermal infrared, and accumulated temperature datasets. The proposed model enables accurate inversion and quantitative analysis of maize phenological traits, offering critical insights to support agricultural management strategies and enhance crop yield stability under stress conditions. The results showed that the integration of multispectral imagery and accumulated temperature achieved an accuracy of 92.9%, while the inclusion of thermal infrared imagery further improved the accuracy to 97.5%. Additionally, UAV-based remote sensing offers superior spatial resolution and operational efficiency compared to manual observation methods in precision and scalability. This study highlights the potential of UAV-based remote sensing, combined with CNN and Transformer as a transformative approach for precision agriculture. It provides a valuable framework for advancing agricultural informatization and enhancing crop management.
Key words: Maize; Crop phenology; Deep learning; UAV；Multi-source data
How to cite: Guo, Y., Zeng, W., Ma, T., Huang, J., Liu, Y., Ren, Z., and Ao, C.: Monitoring maize phenology using multi-source data by integrating convolutional neural networks and Transformers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4944, https://doi.org/10.5194/egusphere-egu25-4944, 2025.
Studying terrestrial water and energy flux dynamics is important for understanding the mechanisms leading to changes in temperature and precipitation extremes. However, the non-conservation of energy and water in most products and their coarse spatial and temporal resolution hamper the study of land-atmosphere feedbacks. The combination of remote sensing data and modelling frameworks allows to greatly improve the spatial coverage and resolution of data products. Here, we investigate the performance of a new data product generated with the high-resolution land surface fluxes from satellite and reanalysis data (HOLAPS) framework. HOLAPS is a one dimensional modelling framework that solves the energy and water balance at the land surface, providing consistent surface and soil variables derived from remote sensing data and reanalysis products as forcings. HOLAPS reaches slightly better results than other ET and H products at daily scales in summer (KGE > 0.3 for ET and KGE > 0.0 for H) and during hot conditions (KGE > 0.2 for ET and KGE >-0.2 for H), while the state-of-the-art products show KGE > 0.1 for ET and KGE > -0.41 for H in summer and KGE > -0.1 for ET and KGE > -0.6 for H during hot conditions. All products evaluated here yield a reasonable performance (KGE >-0.41 at most sites) in simulating SM at the surface and in the root zone. The good performance of HOLAPS together with its inherent advantages (RS data driven, high temporal and spatial resolution, spatial and temporal continuity, soil moisture at different depths and long-term consistent evapotranspiration and sensible heat flux estimates) support its use for hydrological studies based on Earth Observations.
How to cite: García-García, A. and Peng, J.: A satellite based product for studying terrestrial water and energy flux dynamics, HOLAPS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9015, https://doi.org/10.5194/egusphere-egu25-9015, 2025.
Land evapotranspiration (ET) primarily involves vegetation transpiration, canopy interception loss, and soil evaporation. Previous studies have made significant progress in total ET estimation; however, substantial challenges remain in partitioning ET on a regional scale, largely due to the intricate water and energy balance that is disrupted by vegetation cover changes. In particular, the use of land surface models to interpret biophysical processes may be susceptible to uncertainties derived from the estimation of vegetation dynamics. In this study, we integrate satellite leaf area index (LAI) and fraction of vegetation coverage (FVC) into the variable infiltration capacity model (VIC) to improve ET partitioning in the Loess Plateau of China. This region has experienced substantial vegetation greening as evidenced by increased LAI and FVC. The results showed that satellite dynamic vegetation parameters in modeling are effective in improving the estimation of ET components compared with the default vegetation parameters. Specifically, the dynamic parameter of LAI in the model altered the inter- and intra-annual variations in vegetation transpiration and canopy interception loss, supporting the application of dynamic FVC in VIC as being reasonable for allocating transpiration to soil evaporation to capture evaporation from forest gaps. This effect is particularly relevant in arid and semiarid regions. Among the ET components, transpiration was the most sensitive to the two dynamic vegetation parameters, followed by canopy interception loss and soil evaporation. In the Loess Plateau, VIC modeling with dynamic vegetation parameters revealed that the effect of soil evaporation was twice that of transpiration, which is appropriate for this semi-arid region with relatively sparse vegetation coverage. Our study offers valuable insights regarding the use of vegetation coverage for partitioning ET and highlights the advantages of integrating satellite vegetation products into land surface models.
How to cite: Peng, D. and Xie, X.: Improving evapotranspiration partitioning by integrating satellite vegetation parameters into a land surface model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9749, https://doi.org/10.5194/egusphere-egu25-9749, 2025.
The Ganges River Basin with an area of 1,087,300 km2 is the most populous in the world. In recent years, the increasing severity of hydrological extremes, driven by climate change and human activities, has made water resources increasingly unpredictable, alarming water risks in the region. By understanding how, where, and when these changes affect water resources, we can better prepare and respond to the needs of ecosystems and communities in a rapidly changing climate.
Hydrological models have achieved varying degrees of success in simulating water cycle responses. In particular, they often struggle to accurately capture the non-linearity and complexity of processes in highly heterogeneous basins, such as the Ganges. This challenge is further exacerbated by factors such as changing weather patterns, variability in temperature throughout the basin, and other effects induced by climate change. The limited availability of representative and compatible input data, combined with uncertainties in meteorological forcing data, empirical parameters, initial conditions, and structural errors resulting from simplifications, leads to an incomplete understanding of the underlying physical processes within the basin.
In this study, we propose a Data Assimilation (DA) framework to improve hydrological simulations of the Variable Infiltration Capacity (VIC) land surface model within the Ganges River Basin. The DA is formulated to use the Ensemble Kalman Filter (EnKF) as its merger and satellite-based daily Surface Soil Moisture (SSM) data as observations. Uncertainties in meteorological inputs, such as precipitation and temperature, and model parameters are utilized to generate ensemble spreads, leading to a representative estimation of model uncertainty. Numerical evaluations are performed to examine the influence of this daily SSM DA on sub-monthly, monthly, seasonal, and multi-year variations of the key model outputs, including evapotranspiration, surface runoff, and base-flow. The findings aim to support the development of a satellite-fed hydrological system for the Ganges that further strengthens water management and reduces disaster risks.
Keywords: Variable Infiltration Capacity (VIC), Surface Soil Moisture (SSM), Data Assimilation (DA), Ensemble Kalman Filter (EnKF)
How to cite: Tiwari, S., Forootan, E., Nikam, B. R., and Schumacher, M.: Improving Hydrological Process Representation in the Ganges River Basin Using a Data-Assimilation Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15446, https://doi.org/10.5194/egusphere-egu25-15446, 2025.
Irrigation water use constitutes the largest share of freshwater consumption by humans. With increasing water withdrawals for irrigation anticipated in the coming years due to population growth and climate change, there is an urgent need for effective strategies to manage agricultural water use sustainably. However, traditional methods for evaluating irrigation water use, such as administrative records and field surveys, are often constrained by limited spatial coverage, delays in reporting, and inconsistencies in data accuracy. These limitations significantly impede the timely and reliable assessment of irrigation practices, particularly in expansive canal command areas.
Satellite-based remote sensing offers a robust solution to these challenges by providing consistent, high-resolution data over large spatial and temporal scales. The complementary strengths of microwave and optical remote sensing are particularly advantageous in estimating soil moisture. Microwave sensors, with their ability to penetrate clouds and operate in all weather conditions, are effective in deriving baseline soil moisture estimates. Optical sensors, such as those on Sentinel-2, enhance these estimates through high spatial and temporal resolution data that capture vegetation dynamics and surface conditions. Models like OPTRAM (Optical Trapezoid Model), which utilizes optical indices such as NDVI and land surface temperature (LST), further enable the derivation of soil moisture by linking vegetation health and thermal properties to soil water content. This integration of optical and microwave data improves the accuracy and spatial detail of soil moisture estimates.
This study addresses these issues by utilizing satellite-based remote sensing products to estimate irrigation water use and validate these estimates with ground-based observations from provincial irrigation departments. High-resolution soil moisture estimates will be developed by downscaling microwave-based remote sensing products from SMAP at 1 km resolution using MODIS products, and at 20 m resolution using Sentinel-2 imagery. These estimates will be validated with ground-based soil moisture sensors. The downscaled soil moisture products will form the basis for a soil moisture-based inversion model to quantify irrigation water amounts at fine spatial and temporal scales.
By integrating remote sensing-derived estimates with ground-based water allocation data, this study seeks to enhance the accuracy and reliability of irrigation water use assessments. The outcomes of this study will provide actionable insights for water resource managers, policymakers, and irrigation departments, leading to more effective management of surface water supply, improved water allocation, and enhanced agricultural sustainability in high irrigated areas.
How to cite: Zohaib, M., Hafeez, M., Masud Cheema, M. J., Liaqat, U. W., and Kim, H.: Leveraging Remote Sensing based Soil Moisture for High-resolution Irrigation Water Use Estimation and Validation with Reference Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1332, https://doi.org/10.5194/egusphere-egu25-1332, 2025.
Hydrological modelling in ungauged basins faces significant challenges due to the lack of in-situ measurements for model calibration and validation. Remote sensing (RS) data has emerged as a valuable alternative, providing spatially distributed estimates of key hydrological variables such as precipitation, evapotranspiration (ET), and vegetation dynamics. These datasets not only serve as model inputs but also are increasingly used for model calibration and validation, thereby reducing uncertainty and enhancing the model applicability. Despite this potential, a major challenge lies in the discrepancies among different RS products for the same variable. Differences in satellite sensors, retrieval algorithms, and assumptions lead to significant variability in RS products, complicating their integration into hydrological models. This variability makes it difficult to select the most reliable product, particularly in data-scarce regions. Traditional practices often involve applying and comparing multiple RS products in regional studies. Different basins frequently yield different best products, resulting in low model transferability across regions. Alternatively, ensemble products created through data fusion of various RS datasets are used as a single reference to reduce uncertainty. Nevertheless, in both cases, the model parameter space is constrained and refined based on a single representative dataset. The reliance on a singular reference makes the model highly sensitive to biases or inaccuracies in the chosen dataset and overlooks the inherent uncertainty across the spectrum of available RS estimates. This limitation becomes particularly concerning for high-dimensional hydrological systems, where the issue of model equifinality arises and becomes more pronounced as model complexity increases. To address this limitation, we explore an interval-based model calibration strategy that incorporates multiple RS datasets instead of the traditional reliance on a single reference. A suite of algorithms with varying levels of complexity, including Set-Membership, Interval Penalty Minimization, Distributionally Robust Optimization, and Bayesian approaches, are applied to calibrate the Soil and Water Assessment Tool (SWAT) model using multiple RS-based ET products in the Adige River Basin, Italy. The conventional single-reference calibration approach serves as a benchmark for comparison. The interval-based calibration approaches go beyond identifying a single best parameter set by generating optimum parameter spaces, worst-case optimal sets, and probabilistic parameter distributions, providing a more holistic assessment of model performance by accounting for both optimal solutions and associated uncertainties. The results demonstrate the advantages of interval-based calibration in capturing the inherent variability in RS data, offering new insights into the integration of diverse datasets with hydrological models, particularly in data-scarce regions. By embracing the full spectrum of variability across multiple RS products, this strategy can reduce dependency on potentially biased datasets, increase model robustness and transferability.
How to cite: Tuo, Y., Duan, Z., Scaria, H., Giacomo, B., and Mariapina, C.: From Single Reference to Interval-Based Calibration: A Paradigm Shift in Hydrological Modelling with Diverse Remote Sensing Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8299, https://doi.org/10.5194/egusphere-egu25-8299, 2025.
In the Mediterranean environment, water scarcity has always been a structural constraint to the availability of arable land: the current water crisis and the effects of climate change with increasing temperatures, and the different rainfall regime and its increased variability, require the adoption of measures to maximize water use in agriculture.
Particular attention must be paid to the irrigated cropping systems, whose water requirements represent about 60% of the entire water demand; it is essential to encourage both infrastructural interventions and new policies that increase the resilience of supply systems, to promote the use of alternative water resources, while implementing more efficient irrigation practices.
In this context is the research project “EarTH Observation for the Early forecasT of Irrigation needsS (THETIS),” that aims to build a spatial decision support system (SDSS) capable of providing, at the watershed scale, estimates of crop water needs to water supply and management agencies with the goal of improving services to farmers.
The components of the SDSS are: i) a hydrological model (HM); ii) a crop growth model (CGM); iii) Earth observation (EO) data; iv) artificial intelligence (AI) techniques. Earth observation data were used to estimate the transplanting dates of processing tomato in the study area in the province of Foggia, Southern Italy. Sentinel 2 images from the Copernicus constellation were used to calculate the Leaf Area Index of tomato fields from 2000 to 2024, utilizing the biophysical processor in ESA's SNAP application. Additionally, in District 6/B of the CBC, the crop rotations (crop sequences in the same field), were studied to evaluate the occurrence of tomato, as reported by the CBC during in situ surveys in the same period.
The results of the phenological study using LAI data from Sentinel 2 showed that about 50% of tomato fields are transplanted around 15th May and the other half around 15th June. Regarding crop rotations of the 857 monitored tomato fields, 94.2% were found to return to tomato cultivation after 2, 3, and 4 years, with percentages of 35.1, 40.0, and 21.2, respectively.
This study will allow early identification of likely tomato fields (spatial position and area) on 1st April of each year for the THETIS project; these fields are then associated with soil characteristic parameters and forecast weather data to start the simulation of the entire irrigation district with the Aquacrop crop model in the Python version, to serially simulate the predicted fields and estimate irrigation requirements.
Acknowledgment: The project “EarTH Observation for the Early forecasT of Irrigation needS” (THETIS) is funded by ASI under the Agreement N. 2023-52-HH.0 in the framework of ASI’s program “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE).
References
M. Rinaldi, et al., “A crop model for large scale and early irrigation requirements estimation”, Proc. of the 2024 IEEE International Geoscience and Remote Sensing Symposium, pp. 2807- 2810, DOI: 10.1109/IGARSS53475.2024.10640683.
G. Satalino, et al., “Earth observation for the early forecast of irrigation needs”, Proc. of the 2024 IEEE International Geoscience and Remote Sensing Symposium, pp. 4912- 4915, DOI: 10.1109/IGARSS53475.2024.10642240.
How to cite: Rinaldi, M. and the THETIS Team: Estimation of Irrigation Needs by Monitoring Crop Rotations and Phenology of Tomato in Southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11983, https://doi.org/10.5194/egusphere-egu25-11983, 2025.
Spatial and temporal imbalances in rainfall are accelerating due to the increase in extreme weather events caused by climate change. The Korean Peninsula, characterized by a monsoon climate, experiences prolonged periods of summer rainfall. However, in recent years, it has increasingly shifted towards localized heavy rainfall, resulting in frequent saturation of soil moisture and regional imbalances of rainfall. A recent study has demonstrated a correlation between changes in rainfall characteristics and an increase in rainfall imbalance, which has resulted in an escalation in disaster occurrences. To address this challenge, a multifaceted approach to rainfall monitoring has been adopted in Korea, such as a combination of in-situ observations, radar, modeling approaches, and remote sensing data. However, the diversification of rainfall data remains a crucial challenge for effective disaster risk management. In this study, soil physical properties were incorporated into the SM2RAIN algorithm, a simple model that estimates rainfall based on soil moisture content. Soil Moisture Active Passive Level 4 (SMAP L4) data was utilized as the input to SM2RAIN, and the generated rainfall was then subjected to correlation analysis with SM2RAIN-ASCAT and Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG). Rainfall data incorporating soil physical properties exhibited a comparable trend to that of GPM IMERG. The results of this study are anticipated to ensure the diversification of rainfall datasets by providing a relatively simple method for estimating rainfall in ungauged regions.
Keywords: Soil Moisture, Rainfall, SM2RAIN, Soil Physical Properties
Acknowledgment: This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Environment (MOE)(RS-2024-00332300). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266). This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Program for Drought Project, funded by Korea Ministry of Environment (MOE) (RS-2023-00230286).
How to cite: Kim, D., Kim, W., Jeong, J., and Choi, M.: Improved SM2RAIN Algorithm to Estimate Rainfall by Incorporating Soil Physical Properties, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14124, https://doi.org/10.5194/egusphere-egu25-14124, 2025.
In many regions, including Southeast Asia, meteorological observation networks remain underdeveloped. While existing satellite rainfall products demonstrate a certain level of accuracy at the macroscale, their accuracy at the watershed scale remains insufficient. This study aims to propose an algorithm that applies deep learning to IR data obtained from Himawari meteorological satellite observations to estimate rainfall with quantitative accuracy at the watershed scale, contributing to predictions of water-related disasters.
The objective of this research is to optimize a deep learning model using meteorological observation data available in abundance in Japan and subsequently apply it to Southeast Asia. The input data consists of IR images from multiple wavelength bands provided by the geostationary meteorological satellites Himawari-8 and 9, as well as elevation data.
The estimated rainfall in the Japanese region, where parameter optimization did not conduct, was evaluated across various watershed scales. As a result, the model outperformed GSMaP in watersheds with areas ranging from approximately 100 km² to 3000 km². Additionally, in tributary watersheds with areas under 100 km², the model was able to qualitatively replicate observed rainfall.
How to cite: Fujimoto, K. and Tebakari, T.: Development of a new satellite rainfall product HiDRED (Himawari Data Rainfall Estimation using Deep learning) and a fundamental study on its applicability to hydrological models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17181, https://doi.org/10.5194/egusphere-egu25-17181, 2025.
The effective management and monitoring of water resources is imperative for the ecosystem and environmental conservation. Satellite remote sensing data is an efficient tool for detecting water resources such as rivers, reservoirs, and lakes. In addition, it is crucial for the management and prevention of water disasters. Optical satellite data can be used to detect water bodies with high accuracy using Near-Infrared (NIR) imagery and Normalized difference water index (NDWI). Optical satellite images used for water body detection are mainly medium-resolution satellite images such as those Landsat series and Sentinel-2. However, there is a limitation that the medium-resolution satellite images are less effective in detecting small water bodies and boundaries due to their spatial resolution constraints. To address this, high-resolution satellite imagery and advanced analytical techniques, such as deep learning, can be utilized. In this study, deep learning techniques were applied to CAS 500 images with 2 m resolution to detect water bodies. The water body detection performance was validate using manual mask data and evaluation metrics based on a confusion matrix. Furthermore, water body detection performance was compared with Sentinel-2 (10 m) and Planet Scope (3.7 m) satellite imageries. The results of this study are expected to provide high accuracy water body detection results under various environmental conditions.
Keywords: Water Body Detection, High-Resolution, CAS500, Deep Learning
Acknowledgement
This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Environment (MOE)(RS-2024-00332300). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266). This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Program for Drought Project, funded by Korea Ministry of Environment (MOE) (RS-2023-00230286).
How to cite: Cho, S., Kim, W., Lee, S., Jeong, S., and Choi, M.: High-Resolution Water Body Detection Using CAS500 in Korean Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14342, https://doi.org/10.5194/egusphere-egu25-14342, 2025.
India is the world’s second-largest producer and consumer of wheat after China. In recent years, it produced 70-75 million tons of wheat and contributed around 12% of the global wheat production. Irrigation plays an important role in increasing crop yield. However, considering the increasing competition for water resources, there is a need to use irrigation water effectively. The present study aims to evaluate the effects of different irrigation methods (drip, sprinkler, flood, and rainfed) and variable irrigation rates (100%, 75%, 50%, and 0% of crop evapotranspiration) on wheat growth. The field experiments on the wheat crop were conducted at the Demonstration farm of the Department of Water Resources Development and Management, Indian Institute of Technology (IIT) Roorkee, for the years 2022-23 and 2023-24, respectively. Results showed that around 28.62 % of water was saved by the drip irrigation system, and the sprinkler irrigation system saved 19.7 % as compared to the flood irrigation system. Among all treatments, the drip irrigation system retained the highest soil moisture in the top 10 cm depth, whereas the sprinkler irrigation system retained the highest soil moisture in 30 cm and 100 cm depths. Additionally, the results showed that the leaf area index and biomass collected from the sprinkler irrigation system were higher as compared to the drip and flood irrigation systems. Thus, sprinkler irrigation systems can be recommended to promote sustainable water management for wheat cultivation in the Indo-Gangetic plains and similar agroclimatic regions. This practice could play a crucial role in conserving precious water resources and achieving sustainable development goals.
Keywords: - Sprinkler, Drip Irrigation Systems and Sustainable Water Management
How to cite: Kumar Vishwakarma, S., Singh, P., Kothari, K., and Pandey, A.: Enhancing Water Use Efficiency in Wheat Cultivation for Sustainable Water Management Using Different Methods and Rates of Irrigation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14739, https://doi.org/10.5194/egusphere-egu25-14739, 2025.
Near real-time (NRT) acquisition and accurate prediction of key phenological stages in maize are essential for optimizing irrigation decisions and field water management. The shape model fitting (SMF) approach, based on remote sensing technology, has been widely used for phenological stage detection due to its high accuracy and comprehensiveness. However, existing NRT crop phenology monitoring models are often constrained to specific regions or crop types, with validation primarily focused on temporal scales. Systematic evaluations of these models’ applicability across different regions and crop varieties remain insufficient. Moreover, there is a lack of consensus on the effectiveness of various vegetation indices (VIs) for extracting key phenological stage information and their applicability in phenological inversion. This study integrates an enhanced canopy structure dynamics model (CSDM) with the SMF approach, leveraging Sentinel-2 satellite data to assess the role of different VIs in enhancing the precision of key phenological stage identification and to evaluate the model’s applicability across diverse regions and crop varieties. By analyzing VIs data from two maize varieties cultivated on four farms in Heilongjiang Province, China, we identified nine key phenological stages (seeding, emergence, development of stem, heading, flowering, development of fruit, ripening, senescence, and end of season). The results showed that while different VIs exhibited varying sensitivities and responsiveness to environmental changes at different phenological stages, the enhanced model consistently achieved high predictive accuracy, with RMSEs for most key phenological stages remaining under five days. Additionally, the model exhibited robust fitting performance for varieties with similar thermal time requirements and achieved high accuracy across different regions. It provided stable predictions during early phenological stages, with minor deviations in later stages, primarily attributed to variations in accumulated thermal time rates. In summary, this study systematically evaluated the applicability of the enhanced CSDM-SMF model for maize phenology prediction based on Sentinel-2 data from three perspectives: VI selection, regional differences, and varietal adaptability. The findings provide a more comprehensive understanding for applying this model in academic research and contribute to improving the accuracy of agricultural monitoring and management practices.
How to cite: Ma, T., Zeng, W., Ma, T., Huang, J., Liu, Y., Ren, Z., and Ao, C.: Evaluating the canopy structure dynamics model for maize phenology prediction using Sentinel-2, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4911, https://doi.org/10.5194/egusphere-egu25-4911, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot A
EGU25-14673 | ECS | Posters virtual | VPS11
Optimizing Irrigation for Cotton Crops using Deep Reinforcement Learning AlgorithmsFri, 02 May, 14:00–15:45 (CEST) | vPA.16
Cotton is a one of the major crops in the southeastern United States. It significantly impacts regional water resources since it consumes a large amount of freshwater for irrigation. Current irrigation practices fail to optimize water use accurately since they are largely dependent on soil moisture sensors and grower experience. They do not consider dynamic factors such as soil texture, prevailing weather conditions, and the crop's phenological stage. In this paper we propose an innovative approach to enhance the irrigation efficiency through the use of Deep Reinforcement Learning (DRL) model. It takes into consideration the dynamic variables and optimizes irrigation. We utilize a crop growth simulation model as a learning environment to devise an optimal irrigation strategy. By continuously learning from crop feedback and environmental inputs, the DRL system dynamically modifies irrigation amount to optimize production while consuming the least amount of water. Our approach presents a viable alternative for sustainable irrigation decisions in water-intensive crops, since preliminary findings indicate that it can greatly conserve water without sacrificing crop health or productivity. The goal of this research is to aid in the advancement of precision irrigation technologies that guarantee cotton production's sustainability and resource efficiency.
How to cite: Panthi, K., Samadi, V., and Toxtli, C.: Optimizing Irrigation for Cotton Crops using Deep Reinforcement Learning Algorithms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14673, https://doi.org/10.5194/egusphere-egu25-14673, 2025.
EGU25-18154 | ECS | Posters virtual | VPS11
Assessing the Performance of Crop Model Inversion Technique in the AquaCrop Model under Different Synthetic ScenariosFri, 02 May, 14:00–15:45 (CEST) | vPA.17
Agro-hydrological modeling is crucial for designing climate change adaptations such as irrigation management. However, the accuracy of the simulation results greatly relies on the availability and accessibility of reliable ground data. Many countries extremely vulnerable to climate change have limited ground data as input for agro-hydrological modeling that restricts the validity of model results. A ‘model inversion’ technique can potentially tackle this data-scarce situation. Here, we combine alternative data sources, such as remote sensing for the estimation of crop development, with intense simulations to find missing input data such as irrigation.
The present study aims to assess the performance of the model inversion technique using the AquaCrop model under different synthetic scenarios. The main research question is, ‘Is an inverted AquaCrop model able to identify the irrigation pattern of the crop growing period?’ The different synthetic scenarios for testing the performance include variations in the rainfall amount, irrigation amount and interval, soil texture, and initial soil moisture conditions. Preliminary results for synthetic scenarios show that inverse modeling is feasible for the estimation of irrigation patterns. The results indicate that under conditions of zero rainfall and dry initial soil moisture state, best inversion results were produced in both scenarios where continuous and non-continuous irrigation was applied. The scenarios near real-world conditions yielded the best results when continuously using uniform irrigation. Further research will investigate whether integrating remote sensing-based crop growth indicators like LAI or NDVI into the inverse modeling approach can improve scenarios' simulation with non-continuous irrigation.
How to cite: Saravanan, A., Karthe, D., and Schütze, N.: Assessing the Performance of Crop Model Inversion Technique in the AquaCrop Model under Different Synthetic Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18154, https://doi.org/10.5194/egusphere-egu25-18154, 2025.
