HS6.9 | Innovative technologies using remote sensing data for water management applications
EDI PICO
Innovative technologies using remote sensing data for water management applications
Co-organized by ESSI3
Convener: Lluís Pesquer | Co-convener: Ann van Griensven
PICO
| Mon, 24 Apr, 14:00–15:45 (CEST)
 
PICO spot 4
Mon, 14:00
Remote sensing products have a high potential to contribute to monitoring and modelling of water resources. Nevertheless, their use by water managers is still limited due to lack of quality, resolution, trust, accessibility, or experience.
In this session, we look for new developments that support the use of remote sensing data for water management applications from local to global scales. We are looking for research to increase the quality of remote sensing products, such as higher resolution mapping of land use and/or agricultural practices or improved assessments of river discharge, lake and reservoir volumes, groundwater resources and drought monitoring/modelling. We are interested in quality assessment of remote sensing products through uncertainty analysis or evaluations using alternative sources of data. We also welcome contributions using a combination of different techniques (physically based models or artificial intelligence techniques) or a combination of different sources of data (remote sensing and in situ). Finally, we wish to attract presentations on developments of user-friendly platforms providing smooth access to remote sensing data for water applications.
We are particularly interested in applications of remote sensing to estimate the human water interactions such as dam operations and/or irrigations.

PICO: Mon, 24 Apr | PICO spot 4

Chairpersons: Lluís Pesquer, Ann van Griensven
14:00–14:05
14:05–14:15
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PICO4.1
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EGU23-5527
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solicited
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On-site presentation
Christophe Fatras, Alice Andral, and Jérémy Augot

Lakes and reservoirs monitoring is of sheer interest, as in-situ gauging station coverage is dwindling at a global scale. Water storage change show the impact of not only domestic consumption, low water maintenance in rivers or crop irrigation, but also the impact of climate change. In this frame, different approaches are explored in this study to be able to follow from space remote sensing data the lake storage change, either from water height or surface.

For the ESA CCI Lake Storage Change option, we focus on a few lakes distributed around the world to establish a methodology suitable at a global scale to different lake behaviors. In particular for highly varying water bodies, the automatic production and use of hypsometric curve is investigated This approach is yet suitable for volume variations only.

Complementary to this first approach, an image inpainting algorithm applied to digital elevation models around water bodies is developped to assess their total bathymetry (either for lake or reservoir) where the pixels to be reconstructed are the ones underneath the lake surface. The first results show encouraging estimations that may lead in the near future to the assessment of total water volume of lake and reservoir at a global scale. With the recent launch of SWOT that will provide an unprecedented coverage worldwide, the estimation of global water storage change has a bright future.

How to cite: Fatras, C., Andral, A., and Augot, J.: Assessing lake and reservoir storage change from remote sensing data at a global scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5527, https://doi.org/10.5194/egusphere-egu23-5527, 2023.

14:15–14:17
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PICO4.2
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EGU23-12090
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On-site presentation
Santiago Peña Luque, Gael Nicolas, Herve Yesou, Thomas Ledauphin, Sabrine Amzil, Jerome Maxant, Sylvain Ferrant, Manuela Grippa, Afredo Ribeiro, Jean Stéphane Bailly, Jerome Molenat, Fabien Puech, and Rafael Reis

Dams are strategic tools for countries and their management of water resources. Within the Space for Climate Observatory (SCO) initiative and SWOT Downstream program, the StockWater project aims to put in place a system for monitoring water volume in dams. It is based on satellite data, and a specific processing system, thereby facilitating the work of the public authorities in this area.

Water resources monitoring, including surface and groundwater, is a vital issue for governments and public institutions. Water resources are essential for society and economic activity (drinking water, irrigation, hydroelectricity, industry, flood control) and for natural and water ecosystems.

Generally, reservoir stock information is collected and held by the local reservoir managers (public or private). Regional and national authorities might access this information with a certain latency, which depends on national water policies. Central authorities are then confronted with two issues: long latencies to retrieve water stock information and sparse or inexistent information about small reservoirs.

The project proposes a global solution to monitor reservoir stock volumes based on frequent satellite measurements. This solution is based on reservoir water extent monitoring by imaging satellites (Sentinel 1&2) based in the Surfwater processing chain, which integrates a multitemporal approach to improve water masks. Furthermore, StockWater innovation relies on reservoir estimation of Area/Elevation/Volume relationships just from a DEM, even when acquired after the reservoir construction. 

Recent total volume estimations from DEM estimations have been qualified on hundreds reservoirs in France, ranging from 10 to 10000 hectares, providing errors lower than 20% on 77% of the reservoirs. About the general system assesment, Filling rates estimates yield an error lower than 8% on 75% of the measurements.

New versions are evaluated on Spain, France, India and Brazil and deployed on  Burkina-Faso and Tunisia over more than 100 reservoirs. Results are available here: https://www.sco-stockwater.org/  This system will also easily allow volume estimations from Elevation measurements (altimeters Jason, Sentinel3 with limited coverage or SWOT globally).

StockWater project, led by CNES and developed with CS-Group and SERTIT,  holds a partnership initiative with CESBIO, GET, LISAH and FUNCEME/Université Pernambuco laboratories and their local partners in Tunisia, Laos, Burkina-Faso, Brazil and India. StockWater is open to new countries willing to participate in future project expansions.

How to cite: Peña Luque, S., Nicolas, G., Yesou, H., Ledauphin, T., Amzil, S., Maxant, J., Ferrant, S., Grippa, M., Ribeiro, A., Bailly, J. S., Molenat, J., Puech, F., and Reis, R.: Stockwater - Advances in reservoir stock monitoring from space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12090, https://doi.org/10.5194/egusphere-egu23-12090, 2023.

14:17–14:19
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PICO4.3
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EGU23-7453
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ECS
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On-site presentation
Qiuyang Chen, Simon Mudd, Mikael Attal, and Steven Hancock

Due to the limited resolution of freely available digital elevation models (DEM), DEM-derived river products cannot provide accurate flow lines in inland and lowland areas. Stream burning is used to improve the accuracy of extracted flowlines in addition to other flow routing algorithms (from LSDTopotools). Using the latest DEM products (Copernicus 30m DEM and FAB 30m DEM) and other remote sensing data (Sentinel-1, Sentinel-2), the framework is tested on different geomorphological features (meanders, branches) and weather conditions (with and without clouds). The results show a significant improvement in the accuracy of the flow lines compared with existing global hydrography products.

How to cite: Chen, Q., Mudd, S., Attal, M., and Hancock, S.: Extract an accurate river network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7453, https://doi.org/10.5194/egusphere-egu23-7453, 2023.

14:19–14:21
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PICO4.4
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EGU23-7793
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ECS
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On-site presentation
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Nicolas Gasnier, Lionel Zawadzki, Flavien Gouillon, Bernard Specht, Pascal Cauquil, Santiago Pena Luque, Aurore Dupuis, Vincent Martin, Aurélie Sand, Thérese Barroso, Nicolas Picot, Aurélie Strzepek, and Philippe Maisongrande

The hydroweb.next platform is an open-data thematic hub for hydrology. It aims to foster new uses of remote sensing data for water applications by removing the main barriers: data formatting issues, dispersal of access points, and data processing costs,…


Hydroweb.next has been funded by the French government in the frame of Theia (Data and Services center for continental surfaces) and SWOT downstream (Surface Water and Ocean Topography satellite) programs. The hub brings together products from various providers such as Copernicus Land Services along with products from its own production centers. The production centers operate state-of-the-art algorithms that have been developed with scientists from Theia’s Scientific Expertise Centers: SurfWater for Surface Water Extent (SWE) from Sentinel-1 and Sentinel-2 images, Let It Snow for fractional snow cover and OBS2CO for water quality from Sentinel-2 images. As of June 2023, these 3 products will be made available with a 5 million square kilometer coverage. Products from SWOT and Trishna missions will also be distributed by hydroweb.next as they become available. 
In late 2023, SWOT data will include high-level user-oriented products such as river discharges and lake storage changes with global coverage. In 2025, Trishna products will include water quality, water skin temperature, and evapotranspiration. In situ data are also available to allow comparison with satellite data.

The products are distributed using STAC (Spatio-Temporal Asset Catalog) and WMS/WMTS (Web Mapping Services) protocols that follow the FAIR principles. This enables the direct reuse of the data by other services (e.g. UNESCO’s water quality portals).

The WebGIS interface is designed following a User-Centered Development approach. By involving users from various backgrounds such as Water Agencies, NGOs, industry, or academic research in stages of the project: surveys of user needs during interviews, features design involving users, ergonomics improvement through alpha testing, and quick consideration of user feedbacks through continuous integration and deployment. The interface allows searching relevant data using keywords, geophysical variables, and space-time restrictions. It also allows visualizing the products, their temporal evolution, and multitemporal synthesis. Finally, it allows downloading, harvesting, or streaming data, either through the interface or python APIs.

How to cite: Gasnier, N., Zawadzki, L., Gouillon, F., Specht, B., Cauquil, P., Pena Luque, S., Dupuis, A., Martin, V., Sand, A., Barroso, T., Picot, N., Strzepek, A., and Maisongrande, P.: hydroweb.next, an open-data WebGIS platform to bring state-of-the-art products derived from satellite remote sensing to hydrology users, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7793, https://doi.org/10.5194/egusphere-egu23-7793, 2023.

14:21–14:23
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PICO4.5
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EGU23-8586
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On-site presentation
Ester Prat, Lluís Pesquer, Amanda Batlle, Evangelos Spyrakos, and Silvy Thant

This work presents the characterization and performance evaluation of the existing Copernicus EO products for the monitoring and modelling of water bodies dynamics purposes. This study is carried on the Water-ForCE project (https://waterforce.eu/). Water-ForCE (“Water scenarios For Copernicus Exploitation”) is an European H2020 project to develop a Roadmap for Copernicus Inland Water Services, aiming to better integrate the entire inland water cycle within the Copernicus Services.

From the requirements collected in the project through dedicated working group meetings and stakeholders’ consultation, a list of water quality and water quantity variables which are used in water modelling were identified. All of them were related to five types of modelling into water management: biogeochemical models, hydrodynamic models, river models, crop or pasture growth models and landscape water balance models. The availability of water related products in the Copernicus portfolio was analysed by checking and updating the previous water quantity and water quality project inventories.

This work studies their spatial coverage, data discovery and access, file data formats, validation reports, uncertainty indicators and spatial and temporal resolutions and their utility in the mentioned types of water modelling was analysed. Finally, recommendations on improvements of the existing Copernicus products were made based on the cross analysis between the existing features and user needs.

Main conclusions of the work point to the lack of bathymetry and evapotranspiration as well as some specific water quality products, the need of finer spatial resolution for chlorophyll-a in coastal zones and for soil moisture, surface water and snowmelt products, higher temporal resolution for river discharge and groundwater and water quality variables, the need of validation, increase the coherence between in-situ and remote sensing observations and to provide more quality and uncertainty information. Other demands are to uniform marine and lake products, a global (or at least Pan European) coverage for some local/regional products, improvements in data access and data delivery of new formats, and continuous and consistent long-term archives of vegetation and land cover products.

How to cite: Prat, E., Pesquer, L., Batlle, A., Spyrakos, E., and Thant, S.: Characterization of Copernicus EO products for water modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8586, https://doi.org/10.5194/egusphere-egu23-8586, 2023.

14:23–14:25
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PICO4.6
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EGU23-12217
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ECS
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On-site presentation
minsun Kang and minha Choi

Drought within a short time, termed flash drought (FD), severely affects terrestrial ecosystems and water resources. Water use efficiency (WUE) is an essential parameter in understanding the relationship between the water and carbon cycles. However, little is known about the response of WUE to FDs in the Korea peninsula. Therefore, this study identified FD events in Korea using the evapotranspiration (ET) based Standardized Evaporative Stress Index (SESI) and rate of intensification (RI) using soil moisture at flux tower site. Results showed that FD events detected similar patterns in both SESI and RI. At the regional scale, we identified Korea FD frequency and duration via anomalies in SESI using MODerate Resolution Imaging Spectroradiometer (MODIS). Results showed that Korea suffered from 61.3% of FD events for 20 years. The regions with the most FD events were primarily found within the north and east, where the main landcover type is forest, and long FD events (over 30 days) were detected in the northeastern study region. In addition, the effects of FD events on WUE were different based on FD magnitude and landcover types. The changes in WUE response to moderate FD events were obviously driven by the GPP, and the WUE in cropland was observed the highest sensitivity to FD magnitude. To analyze FD impacts on cropland in detail, we focus on monitoring the crop response to FD using microwave remote sensing data such as Synthetic Aperture Radar (SAR) which will be helpful to detect FD effects on crops in a higher resolution.

How to cite: Kang, M. and Choi, M.: Assessing the impacts of flash drought on terrestrial ecosystem based on satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12217, https://doi.org/10.5194/egusphere-egu23-12217, 2023.

14:25–14:27
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PICO4.7
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EGU23-9207
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ECS
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On-site presentation
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Rene Castillo

Emergency planning is the act of preparing for emergencies to reduce losses, human and environmental. The planning process is never complete, threats change and the tools we utilize to address them advance. Drought occurrences within North America and the magnitude of drought impacts reveal the persistent vulnerability of the United States to drought, specifically in the indigenous community. Until recently, drought management was largely response oriented, with little to or no attention to mitigation and preparedness. In 2002, the Navajo Nation developed a drought contingency plan but within the past 20 years no adaptation has occurred. With the increase in adverse impacts of climate change in recent years, an emergent need to revise the drought plan to place more emphasis on mitigation has been expressed by the Water Management Branch of the Navajo Nation. Quantification of the main components of drought mitigation and planning include the assessment of who and what is vulnerable and why. Historically, drought mitigation efforts were restricted by data availability, financial capabilities, and data acquisition. The current contingency plan utilizes the Standardized Precipitation Index (SPI) on a 6-month time scale alone. Yet current research shows that drought is a complex natural hazard where no singular index can adequately capture the impacts across the main categories of drought. As the definition of drought is variable across place, time, and discipline, the addition of diverse indices could provide more insight for the development of a further detailed and tailored drought contingency plan. As such, it has been found that assessing all categories of drought could improve the Navajo Nation’s drought contingency plan by exposing new concepts not yet considered in mitigation efforts. Adding to the currently utilized index that is based off the sole parameter of precipitation, evaluated here is how temperature, humidity, snow cover, vegetation health, and stream flow. These additional factors are able to compare the meteorological drought vulnerability and severity assessment with the Standardized Precipitation Index (SPI) through the development of a web app to display multivariate indices. Hydrological drought should best match meteorological drought both spatially and temporally with agricultural and socioeconomical drought varying the most from the Standardized Precipitation Index (SPI) used for the Navajo Nation Drought Contingency Plan (2002). By applying diverse indices and social data to the Navajo Nation and developing maps across Google Earth Engine and GIS platforms, gaps in risk and vulnerability assessments can be addressed for preparation and mitigation efforts. Predictors for differing categories (hydrological, agricultural, and socioeconomic) may not predict the same important indicator(s) as the meteorological SPI, further establishing a need for multi-index integration and future drought research. This study identifies a methodology for remote and spatial, GIS-based assessment of drought indexing and vulnerability assessment across the Navajo Nation to address broader water management. The identification of insightful drought indices and drought vulnerability is an essential step in addressing the risks and vulnerabilities across the Navajo Nation and may lead to better informed mitigation-oriented drought management for tribal governments, both Navajo and within North America.

How to cite: Castillo, R.: Lessons From Navajo Nation Water Resources, Utilizing Earth Observations to Monitor Drought, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9207, https://doi.org/10.5194/egusphere-egu23-9207, 2023.

14:27–14:29
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PICO4.8
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EGU23-10597
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ECS
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On-site presentation
Zonghan Ma, Bingfang Wu, Sheng Chang, Nana Yan, and Weiwei Zhu

The short-term prediction of soil moisture variation is a decisive indicator of irrigation scheduling and crop management in agriculture. Traditional soil water dynamic models require complex descriptions of water movement and multiple parameters to calibrate for specific fields, which limit the model’s capability of generalization. Machine learning methods based on large sample datasets can automatically learn the most accurate way of predicting soil moisture with numerous related input variables. However, it could be time consuming in training and model optimization to improve performance. Combining the advantages of both methods, we designed a new soil moisture prediction neural network guided by the water transport driving mechanism. The water balance principle is used to limit the training process with remote sensing-based field-scale evapotranspiration, meteorological rainfall and primary soil water changes calculated from a simplified soil water model. By adding the physics layer to neural network, the demand for large datasets and the requirements of training and optimization are reduced. The prediction of soil moisture is at a half-monthly scale, and we tested the model during the winter wheat growing period. The results show that it requires less training capability to achieve high accuracy. Physics-guided neural networks could act as a better framework for parameter prediction in further researches.

How to cite: Ma, Z., Wu, B., Chang, S., Yan, N., and Zhu, W.: Developing a physics-guided neural network to predict soil moisture with remote sensing evapotranspiration and weather forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10597, https://doi.org/10.5194/egusphere-egu23-10597, 2023.

14:29–14:31
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PICO4.9
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EGU23-14319
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ECS
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On-site presentation
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Amanda Batlle, Lluís Pesquer, Cristina Domingo-Marimon, Nuria Hernández-Mora, Nikoletta Ropero, Ester Prat, Annelies Broekman, Lucia De Stefano, Miquel Ninyerola, and Micha Werner Werner

Water availability is a limiting factor for many human activities and natural ecosystems processes. Monitoring of water resources, as well as the impacts of water scarcity on human and natural ecosystems, is key for defining adapted water management strategies. Currently, different European and Worldwide organisations are providing several climate services (CS) based on output datasets from weather forecast and climate projection models. To ensure the translation of these CS to actionable knowledge at a local scale, it has been required the tailoring and downscaling of data to fit the user requirements expressed by selected stakeholders representing different relevant sectors. This is one of the main goals of H2020 I-CISK project (https://icisk.eu/) which includes this study carried out at the Guadalquivir River Basin (RB) (small part of Guadiana RB), in the northern part of Andalusia, South of Spain. It is one of the seven established living labs (LL) in I-CISK. This LL is particularly vulnerable to drought impacts.

The present work aims at evaluating the contribution of remote sensing data as an explanatory variable of the spatial pattern of precipitation, a key meteorological variable of water resources models. This characterization is a necessary preliminary step to understand the local relationships between climatic variables and others (topography, vegetation response, etc…) in order to subsequently apply known correlations to downscale  weather forecasting and climate projection models to the spatial resolution required by the user community.

The method is based on the generation of multiple regressions with residual interpolation using weather stations' monthly precipitation data as the dependent variable and a set of independent variables at 250 m spatial resolution such as, squared distance to the Mediterranean Sea and to the Atlantic Ocean, elevation, cosine of aspect,  a set of remote sensing indexes (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI)), synthetic versions of these indexes and corresponding anomalies.

The NDVI used is generated by monthly aggregation of 16‐day MODIS composite products of MOD13Q1. NDWI has also been calculated from MOD13Q1 surface reflectance products. Synthetic NDVI and NDWI have been generated replacing the original pixel values by the neighbouring vegetation NDVI at the locations of gauge stations where land cover is categorized as impervious surface.  NDVI and NDWI anomalies are calculated based on the climatological monthly mean from the 2000-2021 MODIS data time series. Regressions include independent variables time lags of 0, +1, +2 and +3 months after with respect to the date of precipitation variable.

Preliminary results of single year’s analysis show that including remote sensing data  to the analysis results in a better spatial characterization, obtaining higher correlations in the regressions, which are strongly dependent on seasonality. There is no clear pattern of which index (version and anomaly) is the best contributor and there is also no clear result for the response time lag between precipitation and the indices, although +2 months seems to be the most relevant. Future work will use a full time series analysis to obtain more information on these patterns.

How to cite: Batlle, A., Pesquer, L., Domingo-Marimon, C., Hernández-Mora, N., Ropero, N., Prat, E., Broekman, A., De Stefano, L., Ninyerola, M., and Werner, M. W.: Improvements on the monthly precipitation spatial pattern characterization using a set of remote sensing products., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14319, https://doi.org/10.5194/egusphere-egu23-14319, 2023.

14:31–14:33
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PICO4.10
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EGU23-15535
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On-site presentation
Bakimchandra Oinam, Vicky Anand, Rajkumari Neetu Sana, and Silke Wieprecht

The application of remote sensing can aid the decision makers and the researchers in the field of water resources for the effective monitoring of water quality in a water sparse region.  The monitoring of water quality in a wetland dominated by the heterogeneous biomass becomes more intricate. This research study was carried out in Loktak Lake, a Ramsar site nestled in the Indo-Myanmar range between the time intervals February 2022 to December 2022. In order to carry out this study, high and very high resolution multispectral satellite imageries were used. The physical water quality parameters namely electrical conductivity, total suspended solids, pH, turbidity, and nitrates were considered for the assessment. The results of this study clearly indicate a strong correlation between the field-measured parameters and reflectance. The prediction algorithms were generally the best fit to derive the water quality parameters. The model performance indices indicates good performance of the model with correlation coefficient greater than 0.80. The outcomes of this study emphasize the use of high and very high multi-spectral satellite imageries for the monitoring of water bodies with complex dynamics.

How to cite: Oinam, B., Anand, V., Sana, R. N., and Wieprecht, S.: Satellite remote sensing based approach for water quality monitoring in a data sparse region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15535, https://doi.org/10.5194/egusphere-egu23-15535, 2023.

14:33–14:35
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PICO4.11
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EGU23-9469
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ECS
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Virtual presentation
Halima Taia, Edyta Wozniak, Abdes Samed Bernoussi, and Mina Amharref

In agriculture, water and fertilizer are two limiting elements of plant growth. Indeed, the lack or the excess of one of them disturbs the yields in terms of quality and quantity. Optimal irrigation/fertilization and precisely dosed nutrient supply allow fast growing plants to reach their full potential, offering much larger and better quality yields. The use of remote sensing through satellites images becomes necessary in the case of a large area. To manage properly the use of water and fertilizers in a region it is necessary to know the spatial distribution of crops. So first, we have to discriminate crops. Next the control of doses and plant growth rate must be performed.

In this paper we present a tool for smart management of the water irrigation and fertilizer using remote sensing data and mathematical algorithms by considering crops as a dynamical system.

We give some mathematical algorithms to discriminate dynamical systems (crops) and after we consider the problem of detection of the impact of irrigation and fertilization on the crop through spectral signatures. For this, we consider the problem of detecting the effects of nitrogen and irrigation on the mint  by spectroscopy and we compare the obtained results with other obtained measures for rosemary  without fertilizer. For our case study, we choose potted mint as a plant that grows very fast and we apply our spectral measurement protocol to answer the following problem: Can we detect the effect of water and nitrogen by observing the growth of a given crop using the spectrometer? The results will be used for our tool to manage irrigation and fertilizer.

Keywords: irrigation, fertilizer, crop growth, remote sensing, dynamical systems

This work is the result of a research project: Alkhawarizmi/2020/11: Tool for intelligent management of irrigation water and forest heritage, funded by MESRSFC, CNRST and ADD, Morocco

How to cite: Taia, H., Wozniak, E., Bernoussi, A. S., and Amharref, M.: Toward a Smart Tool for Irrigation Systems management Using remote sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9469, https://doi.org/10.5194/egusphere-egu23-9469, 2023.

14:35–15:45