Session HS6.9

[Programme]

HS6.9 | Irrigation estimates and management from remote sensing and agro-hydrological modelling

Orals |

Thu, 16:15

Posters on site

Thu, 10:45

Irrigation estimates and management from remote sensing and agro-hydrological modelling

Co-organized by SSS10

Convener: Chiara Corbari | Co-conveners: Francesco Morari, Jacopo DariECSECS, kamal Labbassi

Orals

| Thu, 18 Apr, 16:15–17:55 (CEST)

Room 2.23

Posters on site

| Attendance Thu, 18 Apr, 10:45–12:30 (CEST) | Display Thu, 18 Apr, 08:30–12:30

Hall A

Orals: Thu, 18 Apr | Room 2.23

Chairpersons: Chiara Corbari, Jacopo Dari

16:15–16:25

EGU24-1394

On-site presentation

Assessment of the impact of drought and restrictions on irrigation district water allocations on yield using a remote sensing for evapotranspiration approach

Joaquim Bellvert, Jaume Casadesus, Magí Pamies-Sans, and Joan Girona

The Mediterranean region is warming 20% faster than the global average. In addition, climate change is expected to exacerbate this situation in the next decades by increasing potential evapotranspiration, decreasing rainfall and increasing the frequency and intensity of droughts. During the last two years, many irrigated areas of Spain have already suffered from water shortages due to lack of water in reservoirs. In some cases, this has led to impose severe restrictions on water allocations of irrigation districts (ID). Since yield is inextricably linked to the amount of water used by plants, the primary effects of water shortage often appear on crop production. However, water restrictions may vary among irrigation districts, among others, depending on the total available water in the reservoir, land uses, or level of modernization. Thus, any imposition of water restriction has a different impact on crop productivity depending on these parameters. From a decision-making point of view, it would be very useful for watershed policy makers to have a tool capable to simulate the impact of decreases in rainfall and/or water restrictions on crop productivity at irrigation district and/or catchment level. Therefore, this study introduces a novel approach to assess the impact of different climate scenarios and restrictions of irrigation water allocations on crop productivity. The study was conducted in a total of eight irrigation districts located in the north-east Ebro basin (Catalonia, Spain), with different water allocations, which corresponded with a total irrigated area of 150,028 hectares. The following six scenarios were simulated: Control, without water restrictions; Pr25 and Pr50, a reduction in rainfall of 25 and 50%, respectively; Irri25, Irri50, Irri75, a reduction of irrigation water allocation of 25, 50 and 75%, respectively. The crop water productivity functions defined in the literature for multiple crops were used. In addition, actual crop evapotranspiration (ETa) was estimated daily at 20 m resolution using a remote sensing two-source energy balance model with Copernicus-based inputs. Overall, results showed that averaged ETa of all irrigation districts decreased by 14, 19, 29, 50 and 66% respectively for Pr25, Pr50, Irri25, Irri50, Irri75 in comparison to Control. On the other hand, yield losses varied among irrigation districts. Those IDs with higher water allocations showed a significant decrease in yield of around 27% in comparison to Control for scenarios Pr25, Pr50, Irri25 and Irri50, without significant differences among them. On the other hand, yield decreased by 72% in the Irri75. Instead, other irrigation districts with very low water allocations observed an averaged significant decrease in yield of 62% in comparison to Control in all the scenarios. A detailed analysis of the impact of the six simulated scenarios on crop productivity of each irrigation district and crop type is also conducted in this study.

How to cite: Bellvert, J., Casadesus, J., Pamies-Sans, M., and Girona, J.: Assessment of the impact of drought and restrictions on irrigation district water allocations on yield using a remote sensing for evapotranspiration approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1394, https://doi.org/10.5194/egusphere-egu24-1394, 2024.

16:25–16:35

EGU24-6626

On-site presentation

Irrigation Management using Machine Learning Regressors of Aggregated Soil Moisture Data

Keith Bellingham

Soil moisture data is highly valuable for irrigation management, however, soil data can often be difficult for farmers to interpret for making informed irrigation decisions. Subsurface drip irrigation targets the root zone of crops. It is commonly used and highly efficient at minimizing evaporative loss. Factors, such as long irrigation lines and hilly terrain, influence the timing and duration of irrigation events, which makes arrival time and duration of crop irrigation water unpredictable even if there is a well-managed schedule. Also, deficit irrigation is a practice where high value crops are intentionally water stressed after the fruiting stage to improve their quality and value.

In this study, we propose a new modeling method for predicting soil moisture that addresses the randomness of one of the primary boundary conditions, the irrigation event. Through machine learning regression, we aim to predict near surface soil moisture values in a subsurface drip irrigated crop in a silt loam soil texture. Our model focuses specifically on the dewatering portion of the time series soil moisture data at two depths, the soil textural data, and the evapotranspiration (ET) as the only boundary condition. By predicting future soil moisture values or stress conditions in the absence of irrigation, our model provides valuable insights for farmers making irrigation management decisions. This presentation serves as a feasibility study and reports the results of the first attempt to apply machine learning regressors to time series soil moisture data to predict future near surface soil moisture values.

In our experiment, we placed two HydraProbe Soil Sensors in the root zone of a blueberry crop located near Wilsonville Oregon in the United States. Soil moisture was logged every five minutes at depths of 15 and 30 cm. The ET and soil moisture data were aggregated and parameterized into the input features for machine learning regressors. To create a training data set, algorithms were developed to isolate only the dewatering portions of the soil moisture time series data for a single growing season. The machine learning input features include: 1) the sum of ET for a specific duration interval, 2) soil moisture percentage, 3) the sum of the ET for the prior 24 hours, 4) the sum of forward-looking ET and 5) capillary features derived from soil texture pedotransfer functions (PFTs) that are part of the Richard’s Equation. The predicted near future soil moisture values are the output target of the model.

Using the Skikitlearn machine learning regressors, we evaluated random forest, support vector machine, ridge, and LASSO regressions. Each regressor underwent regularization through a grid-search of the hyper-parameters using the training data set. To measure potential overfitting of the models, a 15% holdout was examined using r² and the RMSE (root mean square error). Additionally, a validation data set was created using seasonal low soil moisture values and time intervals not included in the training set. Among the regressors evaluated, ridge regression performed well with an r²=0.98, RMSE=0.5% on the 15% holdout, and an r²=0.93, RMSE=1.13% on the validation data set.

How to cite: Bellingham, K.: Irrigation Management using Machine Learning Regressors of Aggregated Soil Moisture Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6626, https://doi.org/10.5194/egusphere-egu24-6626, 2024.

16:35–16:45

EGU24-14168

On-site presentation

Application of satellite data for the quantification of agricultural water use in the context of the transboundary water system of Titicaca Lake, Desaguadero River, and Poopo Lake in South America

Gustavo Ayala Ticona, Tania Santos, Camilo Gonzales, and David Purkey

In the context of transboundary water systems, one of the most relevant challenges involves the quantification of water use for large scale activities such as agriculture. Whether due to methodological differences in the consolidation of inventories of agricultural areas, their production calendars, or due to differences in data availability between neighboring countries, the consolidation of detailed information of water use represent a process to be improved for an appropriate allocation of resources in transboundary management. On the other hand, the advantages in the availability and spatiotemporal homogeneity of satellite data, added to the connotation to minimize issues related to neutrality and stakeholder biases involving the use of only local data threatening consensus in a transboundary framework, offers a strategical opportunity to enhance the integrated water management by using satellite data.

Under these considerations, the present study applies Landsat satellite images for the spatial and temporal quantification of agricultural water use in the transboundary region (110 969 km²) of the Titicaca Lake, Desaguadero River, and Poopo Lake System (TDP) located between Bolivia (55%), Chile (1%), and Peru (44%) in South America (Lima-Quispe et al., 2022). Data processing first allows, to define irrigated agricultural areas from those which are not irrigated, through a validation process using the inventory of agricultural areas available in the official repositories of the countries and running an analysis using climate data (precipitation and potential evapotranspiration), second; defines the spatiotemporal pattern of water use through the evaluation and combination of vegetation indices (NDVI, EVI, among others) for the total agricultural area of the TDP water system (Linear Regressions) for the crops with the largest extension and/or use of water (potatoes, bean, quinoa, barley) studied at the local level in a process of calibration and validation (Bretreger et al., 2019). The results, from the analysis make possible to classify divergences attributed to the methodology, and use of the remote sensing data (correlation, BIAS in relation to local data) as well as to identify areas where both at the level of surface extension and temporal pattern, real water use would be exceeding the permitted and feasible values (trend test analysis) and therefore would imply a critical condition of alteration over the water bodies involved, which stakeholders may pay attention whether through increasing monitoring to corroborate or to strength penalties for ecosystem protection.

References:

Lima-Quispe, N., Escobar, M., Wickel, A. J., von Kaenel, M., & Purkey, D. (2021). Untangling the effects of climate variability and irrigation management on water levels in Lakes Titicaca and Poopó. Journal of Hydrology: Regional Studies, 37, 100927.

Bretreger, D., Yeo, I. Y., Quijano, J., Awad, J., Hancock, G., & Willgoose, G. (2019). Monitoring irrigation water use over paddock scales using climate data and landsat observations. Agricultural water management, 221, 175-191.

How to cite: Ayala Ticona, G., Santos, T., Gonzales, C., and Purkey, D.: Application of satellite data for the quantification of agricultural water use in the context of the transboundary water system of Titicaca Lake, Desaguadero River, and Poopo Lake in South America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14168, https://doi.org/10.5194/egusphere-egu24-14168, 2024.

16:45–16:55

EGU24-16218

On-site presentation

Super-resolved Land Surface Temperature for irrigation management

Lukas Kondmann, Christian Molliére, Julia Gottfriedsen, and Martin Langer

Demographic growth and economic development are putting unprecedented pressure on finite water resources. It is estimated that global water demand will increase by 50% by 2030 resulting in a potentially devastating water shortage [1]. As 70-95% of all water withdrawals are farming-related [2], agriculture plays a key role in this dynamic.

Inefficient water use in agriculture, often due to the invisibility of crop-specific water requirements, underscores the need for precise irrigation management to optimize water allocation and conservation. Ground sensors and drones can help to tackle this problem but they need to be deployed locally which does not scale. Satellites with instruments in the visible domain such as ESA’s Sentinel-2 reach the necessary spatial resolution but the water needs of crops in the visible spectrum only become apparent once there has been significant damage. Essentially, once a plant is going brown, it is already too late.

Thermal satellites carry the necessary information to obtain evapotranspiration estimates and observe changes in crop health long before visual signs manifest. Existing thermal missions, however, often do not bring the necessary temporal and spatial resolution for large-scale irrigation management. Recent commercial offerings from the New Space industry, such as OroraTech’s upcoming Forest constellation, are beginning to turn the tide on this. Currently, we have two satellites in orbit with 9 more launches planned this year. With this, we will reach a global sub-daily revisit time for our Land Surface Temperature (LST) product which can serve as a basis for derived evapotranspiration or soil moisture data products, informing smart irrigation management

At a native resolution of 200m, our LST product faces a trade-off between high temporal and spatial resolution. Exciting breakthroughs in artificial intelligence allow us to artificially enhance the resolution of our product threefold to 70m. With this, we combine the advantages of high spatial and temporal resolution for better irrigation management and crop stress detection. Our super-resolution product is evaluated based on ECOSTRESS data which comes at 70m. First validation comparisons of our super-resolved data with Ecostress look promising and we aim to explore the applicability of our enhanced data for improved irrigation management and related soil & vegetation water content parameters together with the scientific community.

[1] FAO, 2023. https://www.fao.org/faostories/article/en/c/1185405/#:~:text=Agriculture%20is%20both%20a%20major,water%20there%20is%20no%20exception.

[2] World Economic Forum, 2023. https://www.weforum.org/impact/sustainable-water-management/

How to cite: Kondmann, L., Molliére, C., Gottfriedsen, J., and Langer, M.: Super-resolved Land Surface Temperature for irrigation management , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16218, https://doi.org/10.5194/egusphere-egu24-16218, 2024.

16:55–17:05

EGU24-1837

ECS

On-site presentation

Retrieving the irrigation actually applied at district scale: assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model

Pierre Laluet, Luis Enrique Olivera-Guerra, Víctor Altés, Giovanni Paolini, Nadia Ouaadi, Vincent Rivalland, Wouter Dorigo, Lionel Jarlan, Josep Maria Villar, and Olivier Merlin

Irrigation is the most water consuming activity in the world. Knowing the timing and amount of irrigation that is actually applied is therefore fundamental for water managers. However, this information is rarely available at all scales and is subject to large uncertainties due to the wide variety of existing agricultural practices and associated irrigation regimes (full irrigation, deficit irrigation, or over-irrigation). To fill this gap, we propose a two-step approach based on 15 m resolution Sentinel-1 (S1) surface soil moisture (SSM) data to retrieve the actual irrigation at the weekly scale over an entire irrigation district. In a first step, the S1-derived SSM is assimilated into a FAO-56-based crop water balance model (SAMIR) to retrieve for each crop type both the irrigation amount (I_dose) and the soil moisture threshold (SM_threshold) at which irrigation is triggered. To do this, a particle filter method is implemented, with particles reset each month to provide time-varying SM_threshold and I_dose. In a second step, the retrieved SM_threshold and I_dose values are used as input to SAMIR to estimate the weekly irrigation and its uncertainty. The assimilation approach (SSM-ASSIM) is tested over the 8000 hectare Algerri-Balaguer irrigation district located in northeastern Spain, where in situ irrigation data integrating the whole district are available at the weekly scale during 2019. For evaluation, the performance of SSM-ASSIM is compared with that of the default FAO-56 irrigation module (called FAO56-DEF), which sets the SM_threshold to the critical soil moisture value and systematically fills the soil reservoir for each irrigation event. In 2019, with an observed annual irrigation of 687 mm, SSM-ASSIM (FAO56-DEF) shows a root mean square deviation between retrieved and in situ irrigation of 6.7 (8.8) mm week^-1, a bias of +0.3 (-1.4) mm week^-1, and a Pearson correlation coefficient of 0.88 (0.78). The SSM-ASSIM approach shows great potential for retrieving the weekly water use over extended areas for any irrigation regime, including over-irrigation.

How to cite: Laluet, P., Olivera-Guerra, L. E., Altés, V., Paolini, G., Ouaadi, N., Rivalland, V., Dorigo, W., Jarlan, L., Villar, J. M., and Merlin, O.: Retrieving the irrigation actually applied at district scale: assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1837, https://doi.org/10.5194/egusphere-egu24-1837, 2024.

17:05–17:15

EGU24-5126

ECS

On-site presentation

Synthesizing regional irrigation data using machine learning – towards global upscaling via metamodeling

Søren Kragh, Raphael Schneider, Simon Stisen, Rasmus Fensholt, and Julian Koch

Knowledge on irrigation is key to sustainable water resource management, but spatio-temporal irrigation data are rarely available. Recent advances are based upon satellite remote sensing data to quantify irrigation at high spatial resolution, and this study utilizes published irrigation datasets at regional scale to develop a metamodel approach to synthesize the available irrigation knowledge. We investigate the potentials and limitations of a Random Forest-based metamodeling approach that predicts irrigation at monthly timescale using only globally available and easily accessible features related to hydroclimatic and vegetation variables. The training dataset consists of three irrigation water use datasets derived from the soil moisture-based inversion framework and covers a variety of climatic conditions and irrigation practices in Spain, Italy, and Australia. Further, the study includes irrigation predictions from three test sites representing major global hot spots for unsustainable irrigation management: the North China Plain, Indus, and Ganges Basins. Our study aims to test the model transferability in space and time based on a series of split-sample experiments. We quantify and outline model transferability based on the area of applicability analysis, showing that although the feature space was mostly well represented, the magnitude of the target variable was equally important for assessing model transferability. A comprehensive feature importance analysis reveals that ranking of the most important input features depends on geographical extent of the training dataset. We find that model transferability was more robust across space than time within the small study areas, mainly because of the small geographical extents of the training datasets. The developed metamodel demonstrates satisfying performance with less than 10% bias and 3 mm/month mean error for a successful model transferability outside the training study areas and predicted spatial patterns of irrigation closely linked to climate and vegetation features. Given the increase in published regional irrigation datasets, we see great potential for further developing metamodel approaches for synthesizing existing knowledge and work towards global upscaling opportunities.

How to cite: Kragh, S., Schneider, R., Stisen, S., Fensholt, R., and Koch, J.: Synthesizing regional irrigation data using machine learning – towards global upscaling via metamodeling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5126, https://doi.org/10.5194/egusphere-egu24-5126, 2024.

17:15–17:25

EGU24-18745

ECS

On-site presentation

Irrigation Estimation from Soil Water Balance and the Water Cloud Model by leveraging Sentinel-1 and Sentinel-2 observations

Martina Natali, Sara Modanesi, Domenico De Santis, Luca Brocca, Fabio Mantovani, Andrea Maino, Gabrielle De Lannoy, and Christian Massari

Irrigation plays a pivotal role in the hydrological cycle, representing about 70% of freshwater withdrawals. However, its representation in Earth System models is characterized by significant uncertainties in terms of amount, timing and spatial distribution. Earth Observation data offer a viable way to reduce this uncertainty thanks to their ability to sense the soil and vegetation in its real condition with few-days revisit timing and high spatial resolution (~ 10 m), e.g. with the new Sentinel missions.

In this contribution, we use remote sensing observations from the Sentinel-1 and Sentinel-2 satellite missions to constrain a simple Soil Water Balance (SWB) model coupled with the semi-empirical Water Cloud Model (WCM) and obtain irrigation estimates via an inverse modelling solution. The WCM, which is a model simulating backscatter observations (σ⁰) from soil moisture and a vegetation descriptor, is forced by vegetation indexes from Sentinel-2 data and soil moisture simulated by the SWB that includes a sprinkler irrigation scheme. The model outputs are then matched with Sentinel-1 observations to obtain irrigation estimates.

The model is tested over an irrigated field of the Po River valley, one of the most intensively European irrigated areas. Results show that the model can capture the irrigation signal with relatively good accuracy. It also provides an estimate of soil moisture in the field. Nonetheless the revisit time of the satellite platforms and the simplicity of backscatter model, especially in the representation of the vegetation component, constitute two main limitations of the model. This model is a viable tool that can be easily applied in the context of precision agriculture to optimize irrigation practices and conserve water resources even when in-situ soil moisture and irrigation measurements are not available.

How to cite: Natali, M., Modanesi, S., De Santis, D., Brocca, L., Mantovani, F., Maino, A., De Lannoy, G., and Massari, C.: Irrigation Estimation from Soil Water Balance and the Water Cloud Model by leveraging Sentinel-1 and Sentinel-2 observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18745, https://doi.org/10.5194/egusphere-egu24-18745, 2024.

17:25–17:35

EGU24-11957

On-site presentation

Landsat-based ET to assess illicit groundwater use: The Ica Valley, Peru

Brian Thomas

Rapid agricultural development in the Ica Valley of Peru has translated to historic and on-going unsustainable use of groundwater. Decades of ineffective water resources management threatens the future of agricultural production, requiring an overhaul of water management decisions and actions, particularly for groundwater sustainability. A key measure of robust groundwater management is an accurate estimate of groundwater use, particularly if groundwater use is thought to exceed regulatory abstraction limits. In this study, remote sensing estimates of evapotranspiration are combined with precipitation and water use permit databases to quantify groundwater use that exceeds regulatory limits, termed illicit use. We apply two energy balance approaches, METRIC and SEBAL, combined with gridded climate information to robustly quantify agricultural water use. Our findings document that illicit groundwater use is approximately twice that of regulation abstraction rates, suggesting current management strategies are failing to mitigate unsustainable groundwater use. The remote sensing workflow can be applied to quantify groundwater use to inform efficacy of future groundwater management decisions and aid to identify regions for future interventions.

How to cite: Thomas, B.: Landsat-based ET to assess illicit groundwater use: The Ica Valley, Peru, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11957, https://doi.org/10.5194/egusphere-egu24-11957, 2024.

17:35–17:45

EGU24-14960

On-site presentation

Identifying irrigated areas in the Rhine basin using land surface temperature and hydrological modelling

Devi Purnamasari, Judith ter Maat, Adriaan Teuling, and Albrecht Weerts

In recent years, the Rhine has experienced summer drought which led to extremely low water availability throughout the basin. Additionally, combination of high temperature and low precipitation may increase irrigation demand, putting even more pressure on water availability. Identifying where irrigation occurs and how it evolves over time offers improved insight water use for sustainable water resources planning and management. However, high-resolution maps of irrigated areas for basin-scale studies on water use are often lacking. Here, as part of the HorizonEurope project STARS4Water, we aim to develop a methodology for identifying irrigated areas in the Rhine basin at a 1 km resolution for the period of 2010-2019. This involves utilizing a combination of the hydrological model wflow_sbm to produce land surface temperature (LST_sim) and thermal observations data (LST_obs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. To provide consistent annual irrigation maps, we employed random forest classification model to further identify irrigated areas from the LST difference between LST_simand LST_obs. In the absence of ground information data, the irrigated maps are evaluated against national agricultural statistics and compared with existing developed irrigated maps. The results can be used to comprehend the interannual variability in the extent and location of irrigated croplands in the Rhine basin and are a start to assess and model agricultural water use in the Rhine basin.

How to cite: Purnamasari, D., ter Maat, J., Teuling, A., and Weerts, A.: Identifying irrigated areas in the Rhine basin using land surface temperature and hydrological modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14960, https://doi.org/10.5194/egusphere-egu24-14960, 2024.

17:45–17:55

EGU24-17205

On-site presentation

On the potential of monitoring small water structures with SWOT

Lionel Zawadzki, Nicolas Gasnier, Roger Fjortoft, Santiago Pena Luque, Damien Desroches, Nicolas Picot, and Thérèse Barroso

Launched in December 2022, the SWOT satellite is a joint mission between NASA, CNES, UKSA, and CSA. It marks a significant breakthrough in the fields of oceanography and hydrology.

Historically, water quantity data derived from satellites relied on a combination of different types of imagery (such as SAR and optical) and nadir altimetry or bathymetry. However, these methods have several limitations, especially when it comes to accurately observing hydrology features without relying on data from Very-High Resolution commercial satellites.

As an example, Sentinel-2 imagery can detect water in optical images down to 10x10 m2 pixels [Pena-Luque et al, 2021]. As a result, Sentinel-2-derived land-water masks over rivers that are less than 20-m-wide often contain significant gaps. On the other hand, SAR imagery from Sentinel-1 can detect water surfaces larger than its 22 m resolution, but it's challenging to differentiate water from wet areas and roads [Pena-Luque et al, 2021]. Neither of these sensors can retrieve the water elevation. In contrast, conventional altimetry has limited spatial coverage and is generally considered difficult to use in obtaining accurate water surface elevations in rivers less than 100-m wide [Calmant et al, 2006, 2008, 2016]. However, recent algorithmic advances [Boy et al, 2021, Egido et al, 2016], on the latest generation of nadir sensors (Delay Doppler Altimeters or SAR-altimeters) onboard Sentinel-3 and Sentinel-6 satellites showed that one can retrieve accurate water levels over small freshwater reservoirs.

SWOT observations offer a novel approach to retrieve water quantity data from space. It operates using a near-nadir Ka-band SAR Altimeter based on interferometry to measure the elevation of water pixels with a sampling of 10-60x22 m2. Although its revisit time is limited to at least twice per 21-day nominal cycle up to 78° latitude and its spatial resolution restricts its applicability for operational water management in irrigation and freshwater storage systems, SWOT presents new opportunities for understanding water management at the basin level. It can be used in combination with high-resolution imagery and real-time in situ measurements, and integrated into hydrological models for more effective water management.

Although the official mission specification designed SWOT for the retrieval of water surface elevation of 100-m wide rivers with 10-cm accuracy over 10-km reaches, a study by Gasnier et al, 2021, showed the potential of SWOT to observe narrow rivers. The first actual observations provided by SWOT in 2023 are publicly available on hydroweb.next and PO.DAAC websites. These confirm its potential to observe small hydrological targets well beyond the mission requirements.

In this study, we will present early results on human-made irrigation and freshwater storage systems, and discuss the current possibilities and limitations of SWOT satellite.

How to cite: Zawadzki, L., Gasnier, N., Fjortoft, R., Pena Luque, S., Desroches, D., Picot, N., and Barroso, T.: On the potential of monitoring small water structures with SWOT, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17205, https://doi.org/10.5194/egusphere-egu24-17205, 2024.

Posters on site: Thu, 18 Apr, 10:45–12:30 | Hall A

Display time: Thu, 18 Apr, 08:30–12:30

Chairpersons: Francesco Morari, kamal Labbassi

A.89

EGU24-3839

ECS

Assessing Global Climate Datasets for Small-Scale Agricultural Applications: The Case of Nemea, Greece

Evangelos Dosiadis, Aikaterini Katsogiannou, Evangelos Nikitakis, Eleni Valiantza, Stylianos Gerontidis, Konstantinos Soulis, and Dionissios Kalivas

In recent years, extensive research has been conducted to evaluate various surface-, satellite-, and reanalysis-based gridded datasets of climatic variables on a global scale. However, a noticeable gap exists in understanding their effectiveness and accuracy in agricultural applications, particularly in very small-scale areas. While these datasets have proven valuable for assessing global climate patterns, their translation to on-the-ground impacts, especially in agricultural landscapes, remains a challenge. The complexities of agricultural systems, including irrigation management, farming practices, and responses to extreme weather events, demand a closer examination of the suitability and precision of existing climate datasets for informed decision-making in the agricultural sector.

This study seeks to address this gap by focusing on the wine-making region of Nemea, Greece, providing valuable insights into the utility of global climate datasets in agricultural applications and especially irrigation management to streamline precision irrigation management in regions where data scarcity prevails. The primary objective is to explore the applicability of diverse global climate datasets in small-scale areas, emphasizing the unique challenges posed by the very high spatial variability in regions characterized by complex landscapes, very steep relief, and very small farms. The study delves into the intricacies of irrigation management, and the impact of extreme temperatures on vine stress.

The methodology employed involves leveraging a variety of open-source global climate datasets, which are subsequently evaluated for accuracy through validation against local meteorological stations data. A network of 10 agrometeorological stations located throughout the wine-making region of Nemea will be used. The key variables under scrutiny include the variables related to irrigation and crop management, i.e. precipitation, air temperature, air humidity, wind velocity, and solar radiation. The applied methodology includes the assessment of the characteristics of the available grided datasets; the evaluation of the grided datasets accuracy in general and for specific conditions (e.g. heatwaves, frost days, storms etc.); and the comparison of optimum irrigation schedules compiled using detailed meteorological data obtained by local agrometeorological stations for a five-year period with the corresponding schedules compiled using the gridded datasets under evaluation. The effects of gridded datasets inaccuracies on crops development, crop stress, and crop yield quality and quantity are also evaluated.

The results demonstrate the clear influence of spatial resolution on data accuracy. The study underscores the significance of selecting datasets with an optimal spatial resolution to enhance the precision of climatic variables in large-scale areas. This insight contributes to the broader discourse on the practicality and limitations of employing global climate datasets in small scale agricultural applications in regions characterized by complex landscapes. Insights on relevant downscaling and correction methodologies are provided.

How to cite: Dosiadis, E., Katsogiannou, A., Nikitakis, E., Valiantza, E., Gerontidis, S., Soulis, K., and Kalivas, D.: Assessing Global Climate Datasets for Small-Scale Agricultural Applications: The Case of Nemea, Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3839, https://doi.org/10.5194/egusphere-egu24-3839, 2024.

A.90

EGU24-13738

Rice Field Detection by Dual Polarization SAR Images

Kuo-Hsin Tseng and Jui-Han Yang

Paddy rice plays a significant role in Asian agriculture, particularly in Taiwan. However, monitoring parcel-level activities and quantifying potential yield during the two crop cycles present challenges. The application of remote sensing to track paddy phenology emerges as a valuable strategy for improving crop management and ensuring food security. Synthetic Aperture Radar (SAR) satellites, among various spaceborne sensors, provide timely and extensive information unaffected by cloud cover. This study aims to extract time series data on paddy-specific phenology using dual-polarized SAR data and subsequently map paddy rice parcels in Taiwan. The process involves three primary steps: (1) Identifying phenological curves in training sites based on the temporal behavior of SAR backscattering coefficients; (2) Utilizing signal decomposition to analyze periodic patterns; (3) Recognizing rice fields by identifying the start and end of each crop cycle in the time series; (4) Validating the results with in situ data. In our preliminary findings, the accuracy in certain townships in western Taiwan achieves a kappa value of >0.6, with an overall accuracy exceeding 0.8. Additionally, we aim to unveil potential connections among crop cycles, groundwater changes, and land subsidence.

How to cite: Tseng, K.-H. and Yang, J.-H.: Rice Field Detection by Dual Polarization SAR Images , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13738, https://doi.org/10.5194/egusphere-egu24-13738, 2024.

A.91

EGU24-14901

Exploring satellite soil moisture products for irrigation. A case study: Braila County, Romania

Zenaida Chitu, Daniela Trifan, Alin Ghiorghe, Cristian Stroia, Nicolae Popescu, Irina Ontel, Claudiu-Valeriu Angearu, Adrian Irasoc, and Giorgiana Luftner

Irrigated agriculture will be impacted by climate change as average temperatures and rainfall variability increase. This trend will continue in the future, according to numerical experiments with climate models, but how it develops will be strongly influenced by the anthropogenic emission levels of greenhouse gases. However, the effects of climate change have not been, and will not be, uniform across regions or over time because human-induced warming is superimposed on natural climate variability (IPCC, 2021).

Recent studies (Caian et al., 2023) focused on the projected changes in extreme agro-climatic indicators reveal that Southern Romania appears as a regional hot-spot of climate change because the projected changes are higher and more accelerated than other regions of the country. In this context farmers will need to improve crop water allocation for sustainable irrigation as a measure of climate change adaptation.

Irrigation is the largest consumer in the agriculture sector and the efficient use of water is crucial in the next decades. Monitoring soil moisture will improve water allocation in space and time in irrigated agriculture. Braila County has the largest irrigated areas in Romania and efficient water allocation will mitigate the environmental issues related to water scarcity and soil degradation by salinization and erosion. According to the Koeppen-Geiger classification, the climate of this area is warm temperate humid with hot summers (Cfa) (Cheval et al., 2023). The mean annual precipitation is 450 mm, while the mean annual potential evapotranspiration exceeds 800 mm. The agro-climatic conditions require the use of irrigation in order to avoid crop losses and to ensure high crop productivity.

In this study, we focus on investigating the feasibility of satellite soil moisture products (AMSR-2, ASCAT, SMOS and SMAP) to derive amount of water applied for irrigation and the applicability of this approach to climatic and irrigation conditions specific to Braila County, Romania.

This study has received funding from the European Union Agency for the Space Programme under the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101082189 (MAGDA project).

How to cite: Chitu, Z., Trifan, D., Ghiorghe, A., Stroia, C., Popescu, N., Ontel, I., Angearu, C.-V., Irasoc, A., and Luftner, G.: Exploring satellite soil moisture products for irrigation. A case study: Braila County, Romania, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14901, https://doi.org/10.5194/egusphere-egu24-14901, 2024.

A.92

EGU24-15215

SAR imagery and deep learning techniques for reservoir monitoring in Korea

Wanyub Kim, Doyoung Kim, Yeji Kim, HyunOk Kim, and Minha Choi

Agricultural reservoirs are key structures for water supply on the Korean Peninsula, where the water resources are concentrated seasonally. Monitoring of agricultural reservoirs is essential for efficient management of available water resources. However, in the case of the Korea, there are many unmeasured reservoirs without observation facilities, so it is difficult to monitor available water at a regional scale. Remote sensing-based reservoir monitoring that can observe the water surface in a wide area is essential. In the case of Synthetic Aperture Radar (SAR) image, continuous water body detection is possible regardless of weather conditions. Recently, water body detection research using AI techniques has been actively conducted to improve accuracy. In this study, water body detection was performed on an agricultural reservoir using Sentinel-1 SAR image and AI-based U-net, HR-Net, and Swin-Transformer techniques. The water/non-water binary classification images from the Sentinel-2 satellite were used for validation. In addition, time series validation was performed using in-situ reservoir storage and evaluated the performance of each deep learning techniques. If SAR image with high spatial and temporal resolution can be utilized in the future, it is expected that more efficient management of available water resources will be possible.

Keywords: Sentinel-1, SAR, Deep learning, Water body detection, Reservoir

Acknowlegment: This work was supported by the “Development of Application Technologies and Supporting System for Microsatellite Constellation”project through the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021M1A3A4A11032019). 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」

How to cite: Kim, W., Kim, D., Kim, Y., Kim, H., and Choi, M.: SAR imagery and deep learning techniques for reservoir monitoring in Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15215, https://doi.org/10.5194/egusphere-egu24-15215, 2024.

A.93

EGU24-17492

WSN-Based Irrigation Scheduling Model for Sugarcane Crops

Yogesh Kushwaha, Rajib Panigrahi, and Ashish Pandey

The critical demand for freshwater resources worldwide necessitates their efficient utilization. The agricultural sector is one of the major consumers of fresh water. However, in traditional irrigation techniques, about 60% of the water is wasted, resulting in low water use irrigation efficiency. Practical sensor-based methods are desperately needed to determine the soil water status for adequate irrigation scheduling. Using cutting-edge solutions to improve irrigation management is essential to water resource conservation. Wireless sensor networks (WSN) are an innovative technology advancing agriculture toward greater efficacy and sustainability. This research focused on developing a WSN-based irrigation system to minimize water losses under actual field conditions. The designed system was integrated with Fr4 capacitive-based soil moisture and DS18B20 soil temperature sensors, specifically evaluated for managing irrigation in loamy soil for sugarcane cultivation. The sensors were strategically installed at depths of 15 cm, 30 cm, and 45 cm below the surface of the soil. Throughout the crop's growth season, these sensors continuously measure the soil parameters (soil moisture content, soil temperature) and wirelessly transfer them to a cloud server through the ZigBee protocol to facilitate remote accessibility. The data was easily accessible online via a web service. An analytical approach utilizing a weighted average method was employed to interpret the soil moisture data collected from the three depths. This technique accurately depicted the soil water condition in the crop's root zone.

Furthermore, by setting a threshold according to the sensor's soil water content, the system may precisely initiate irrigation operations when needed. Overall, the WSN-based irrigation management system aims to improve productivity, reduce water waste, and increase the overall sustainability of agricultural operations. The efficacy of the developed system was field validated in terms of cost, efficiency, and ease of replicating before being delivered for societal use. With cloud-based data analysis and monitoring, users/farmers can access the irrigation system from anywhere and monitor it online. The experimental findings indicate that this irrigation management system utilize less water along with high water use efficiency.

Keywords: Wireless Sensor Network (WSN); Soil Moisture Sensors; Irrigation Scheduling.

How to cite: Kushwaha, Y., Panigrahi, R., and Pandey, A.: WSN-Based Irrigation Scheduling Model for Sugarcane Crops, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17492, https://doi.org/10.5194/egusphere-egu24-17492, 2024.

A.94

EGU24-18407

Advancing Water Management in Water-Scarce Regions: A Collaborative Approach for Early Assessment of Irrigation Needs in the Capitanata Consortium (Apulia region).

Vito Iacobellis and the THETIS

In water-scarce regions, effective water resource management is crucial for sustainable agriculture. Scientists and decision-makers are working to address issues of resource conservation and agricultural productivity, with a growing interest in coupling hydrological and crop models. The current trend of interest seems to be limited to the improvement of crop system performance and environmental impact assessment, but attention also needs to be paid to sustainable crop production and water management concerns. Driven by these needs, in the framework of I4DP-SCIENCE program, the Italian Space Agency (ASI) supports the ambitious collaborative project THETIS (Earth Observation for the Early forecasT of Irrigation needS; Agreement n. 2023-52-HH.0), involving the National Research Council – Institute for Electromagnetic Sensing of the Environment (CNR-IREA), the Council for Agricultural Research and Economics (CREA), the Polytechnic of Bari, the University of Bari, and the Reclamation Consortium of the Capitanata, Foggia, Italy. The project focuses on the early assessment and forecasting of irrigation needs in the “Fortore” irrigation district in the Apulian Tavoliere (Southern Italy).

THETIS aims to develop a Spatial Decision Support System (SDSS) integrating hydrologic and crop growth models with advanced Earth Observation (EO) products, Artificial Intelligence (AI) and a WEBGIS interface to provide basin-scale information for the efficient planning of irrigation resources for three different use cases (i.e., early forecasting, irrigation start and mid-season estimation) for different target crops.

The project architecture significantly relies on the use of EO derived products obtained through the integrated use of Synthetic Aperture Radar (SAR), multispectral and hyperspectral data. They serve the purpose of describing land surface processes and represent crucial parameters for hydrological and crop growth model constraints.

Specifically, the calibration of the hydrological model spans from summer 2021 to autumn 2022. The subsequent phase will include a validation phase (year 2023) and an operational phase to estimate water use for the upcoming irrigation season. The validation of the model outputs includes the comparison of the estimated water demand with the actual irrigation volumes applied by the Consortium.

A primary focus of the proposed architecture lies in generating time-series estimates of root zone soil moisture, essential for defining the initial conditions in the crop growth model AquaCrop which plays a pivotal role in managing the water balance at the field scale in the areas relevant to irrigation needs assessment. To achieve this goal, the project aims to integrate, for the first time, a revised version of the well-known daily basin-scale hydrological model DREAM with the physically based Soil Moisture Accounting and Routing (SMAR) model.

This work concerns data selection and assessment for calibration and validation phases of the basin-scale hydrological model and the SMAR model. They include sparse daily field measurements and satellite data retrievals. Although field monitoring remains essential, preliminary results regarding the use of satellite-derived and downscaled products for a proper model calibration are encouraging. The proposed approach shows promise for providing insights into soil dynamics for operational implementation, supporting advances in sustainable agricultural techniques and rational water resource management in semi-arid environments.

How to cite: Iacobellis, V. and the THETIS: Advancing Water Management in Water-Scarce Regions: A Collaborative Approach for Early Assessment of Irrigation Needs in the Capitanata Consortium (Apulia region). , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18407, https://doi.org/10.5194/egusphere-egu24-18407, 2024.

A.95

EGU24-19723

ECS

Influence of irrigation on soil moisture and evaporation based on Sentinel 1 backscatter observations and an evaporation retrieval model

Baris Oztas, Oscar Baez Villanueva, Irina Yu. Petrova, Olivier Bonte, Jacopo Dari, Bernhard Raml, Mariette Vreugdenhil, Wolfgang Wagner, and Diego Miralles

Irrigation stands out as a primary driver influencing water dynamics over agricultural regions. Its estimation in time and space is complex, and satellite observations are only indirectly related to irrigation. Conveniently, Sentinel 1 SAR observations are sensitive soil moisture dynamics and irrigation, and can be used to estimate these dynamics at high resolution. The influence of irrigation on transpiration is however even more complicated to unravel from space observations. Current evaporation retrieval models are not designed to represent the influence of irrigation. However, the current availability of Sentinel 1 observations represents an opportunity to fill this gap.
In this presentation, the Global Land Evaporation Amsterdam Model (GLEAM) will be adapted to assimilate Sentinel 1 backscatter, using the Ebro river basin in Spain as a study case. While GLEAM's coarse resolution has to date hindered its application in the context of agricultural management, recent efforts during the Digital Twin Earth ESA initiative have yielded a GLEAM version at 1km resolution over the Mediterranean region that will be used in the context of this study. Here, we aim to leverage the high-resolution (1-km) GLEAM and explore its coupling to the Water Cloud Model to enable the forward data assimilation of Sentinel 1 backscatter. Several data assimilation techniques, such as Ensemble Kalman Filter, will be applied, seeking to find a method to estimate evaporation and soil moisture in irrigated land that can be transferable to basins where irrigation volumes are not available.

How to cite: Oztas, B., Villanueva, O. B., Petrova, I. Yu., Bonte, O., Dari, J., Raml, B., Vreugdenhil, M., Wagner, W., and Miralles, D.: Influence of irrigation on soil moisture and evaporation based on Sentinel 1 backscatter observations and an evaporation retrieval model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19723, https://doi.org/10.5194/egusphere-egu24-19723, 2024.

A.96

EGU24-20015

Evaluation of Crop Water Stress Using Drone Images and Numerical Weather Prediction Model Data

Jae-Hyun Ryu, Hoyong Ahn, and Kyung-Do Lee

Water conditions in soil are measured with soil moisture sensors such as tensiometer and time-domain reflectometry. However, installed soil moisture sensors may not fully represent the entire cultivation area due to factors such as topography, meteorological conditions, and irrigation systems.The purpose in this study is to identify spatial variations of crop growth and moisture conditions using drone images and weather data. The drone, equipped with multi-spectral, hyper-spectral, and infrared cameras, captured images, and precipitation information up to 3 days later was automatically collected from numerical weather prediction model. Thermal images of crops and soil responded immediately depending on the presence or absence of irrigation. In irrigated crops, leaf temperature decreased due to transpiration. The hyper-spectral images, including short-wave infrared wavelengths, proved sensitive to soil water conditions. However, reflectance-based water indices showed no immediate differences for crops unless soil moisture fell below the wilting point. There was a difference in crop growth depending on the level of irrigation, which was clearly revealed in the vegetation index. Crop growth was poor in areas where irrigation was low. When soil moisture sensor values decrease and no rainfall is expected in the near future, drone images can be utilized to identify specific areas experiencing crop moisture stress. This suggests the potential for drones to support irrigation decision-making.

Acknowledgments: This research was funded by the Rural Development Administration, grant number RS-2022-RD009999.

How to cite: Ryu, J.-H., Ahn, H., and Lee, K.-D.: Evaluation of Crop Water Stress Using Drone Images and Numerical Weather Prediction Model Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20015, https://doi.org/10.5194/egusphere-egu24-20015, 2024.

A.97

EGU24-21860

ECS

Satellite-based energy models to estimate crop yield. An automatic approach at the regional scale

Davide Gabrieli, Chiara Corbari, Francesco Pirotti, Samuele Trestini, Pietro Teatini, and Francesco Morari

The recent climate dynamics characterized by unpredictability and a series of extreme events pose challenges to society at various levels, particularly threatening agricultural production. The development of increasingly sophisticated models and computers combined with remote sensing techniques can serve as a means to safeguard the agricultural domain.

The aim of this work is to develop a computational tool, named CROPORBIT, designed to operate at a regional scale for estimating crop yield. The capabilities of this tool have a significant positive impact on water management, crop health monitoring, and quantifying damage from extreme meteorological events, such as high temperatures.

CROPORBIT combined the radiative model METRIC with a Photosynthetically Active Radiation-based model. Essential inputs for the tool include Landsat 8 and 9 satellite imagery and daily meteorological data retrieved from the regional network stations.

The tool performs a multi-temporal analysis of crop growth, involving the interpolation of ET, stress coefficient, and dry biomass accumulation maps, which are then transformed into crop yield maps by applying a harvest index coefficient.

CROPORBIT underwent validation in a series of soybean and corn fields situated in the low-lying plain of the Veneto Region, where crop yield maps were recorded by combine harvesters.

The preliminary results have shown that CROPORBIT can predict the average crop yield with a good approximation while it was less performing in capturing the field yield variability. The main issues have proven to be the scarcity of clear-sky conditions imagery and the estimation of the harvest index variability.

This research establishes the foundation for future investigations, emphasizing the need for improvements in spatial and time resolution. Enhancements in these aspects may lead to improved outcomes in terms of both accuracy and spatial variability.

How to cite: Gabrieli, D., Corbari, C., Pirotti, F., Trestini, S., Teatini, P., and Morari, F.: Satellite-based energy models to estimate crop yield. An automatic approach at the regional scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21860, https://doi.org/10.5194/egusphere-egu24-21860, 2024.