| publications-2071 |
Peer reviewed articles |
2022 |
Gary Free, Mariano Bresciani, Monica Pinardi, Steef Peters, Marnix Laanen, Rosalba Padula, Alessandra Cingolani, Fedra Charavgis, Claudia Giardino |
Shorter blooms expected with longer warm periods under climate change: an example from a shallow meso-eutrophic Mediterranean lake |
Hydrobiologia |
10.1007/s10750-021-04773-w |
AI & Machine Learning |
Natural Water Bodies |
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AbstractSatellite data from the Climate Change Initiative (CCI) lakes project were used to examine the influence of climate on chlorophyll-a (Chl-a). Nonparametric multiplicative regression and machine learning were used to explain Chl-a concentration trend and dynamics. The main parameters of importance were seasonality, interannual variation, lake level, water temperature, the North Atlantic Oscillation, and antecedent rainfall. No evidence was found for an earlier onset of the summer phytoplankton bloom related to the earlier onset of warmer temperatures. Instead, a curvilinear relationship between Chl-a and the temperature length of season above 20°C (LOS) was found with longer periods of warmer temperature leading to blooms of shorter duration. We suggest that a longer period of warmer temperatures in the summer may result in earlier uptake of nutrients or increased calcite precipitation resulting in a shortening of the duration of phytoplankton blooms. The current scenario of increasing LOS of temperature with climate change may lead to an alteration of phytoplankton phenological cycles resulting in blooms of shorter duration in lakes where nutrients become limiting. Satellite-derived information on lake temperature and Chl-a concentration proved essential in detecting trends at appropriate resolution over time. |
101004186 |
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| publications-2072 |
Peer reviewed articles |
2021 |
Marina Amadori, Lorenzo Giovannini, Marco Toffolon, Sebastiano Piccolroaz, Dino Zardi, Mariano Bresciani, Claudia Giardino, Giulia Luciani, Michael Kliphuis, Hans van Haren, Henk A Dijkstra |
Multi-scale evaluation of a 3D lake model forced by an atmospheric model against standard monitoring data |
Environmental Modelling & Software |
10.1016/j.envsoft.2021.105017 |
Simulation & Modeling |
Natural Water Bodies |
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No abstract available |
101004186 |
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| publications-2073 |
Peer reviewed articles |
2023 |
Khlif, M.; Escorihuela, M.J.; Chahbi Bellakanji, A.; Paolini, G.; Lili Chabaane, Z. |
Remotely Sensed Agriculture Drought Indices for Assessing the Impact on Cereal Yield |
Remote Sensing |
10.3390/rs15174298 |
Predictive Analytics |
Irrigation Systems |
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This study aims to analyze the potential of different drought indices for identifying drought periods and predicting cereal yield in two semi-arid regions, Lleida in Catalonia and Kairouan in Tunisia, which have similar Mediterranean climates but different agricultural practices. Four drought indices, namely the Soil Moisture Anomaly Index (SMAI), the Vegetation Anomaly Index (VAI), the Evapotranspiration Anomaly Index (EAI), and the Inverse Temperature Anomaly Index (ITAI), were calculated from remote sensing data. Drought periods were identified from 2010/2011 to 2021/2022 based on the aforementioned indices. A correlation study between drought indices and wheat and barley yields was performed in order to select the most informative index and month for yield prediction. In the rainfed cereal area of Lleida, the strongest correlation was found between the EAI and VAI with barley yield (0.91 and 0.83, respectively) at the time of cereal maturity in June. For wheat, the strongest correlation was found between the EAI and VAI (0.75 and 0.72, respectively) at the time of cereal maturity in July. However, the VAI, EAI, and SMAI showed the best performance as an earlier indicator in March with a correlation with barley yield of 0.72, 0.67, and 0.64, respectively; the lowest standard deviation was for the SMAI. For wheat yield, the best earlier indicator was the SMAI in March, showing the highest correlation (0.6) and the lowest standard deviation. For the irrigated cereal zone of Kairouan, the strongest correlation (0.9) and the lowest standard deviation are found between the EAI and cereal yield in April. In terms of advanced prediction, the VAI shows a high correlation in March (0.79) while the SMAI shows a slightly lower correlation in February (0.67) and a lower standard deviation. The results highlight the importance of the EAI and SMAI as key indicators for the estimation and early estimation (respectively) of cereal yield. |
823965 |
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| publications-2074 |
Peer reviewed articles |
2022 |
Mehrez Zribi; Vincent Dehaye; Karin Dassas; Pascal Fanise; Michel Le Page; Pierre Laluet; Aaron Boone |
Airborne GNSS-R Polarimetric Multiincidence Data Analysis for Surface Soil Moisture Estimation Over an Agricultural Site |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
10.1109/jstars.2022.3208838 |
AI & Machine Learning |
Uncategorized |
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No abstract available |
823965 |
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| publications-2075 |
Peer reviewed articles |
2021 |
Salwa Belaqziz, SaĂŻd Khabba, Mohamed Hakim Kharrou, El Houssaine Bouras, Salah Er-Raki and Abdelghani Chehbouni |
Optimizing the Sowing Date to Improve Water Management and Wheat Yield in a Large Irrigation Scheme, through a Remote Sensing and an Evolution Strategy-Based Approach |
Remote Sensing |
10.3390/rs13183789 |
Simulation & Modeling |
Irrigation Systems |
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This study aims to investigate the effects of an optimized sowing calendar for wheat over a surface irrigation scheme in the semi-arid region of Haouz (Morocco) on irrigation water requirements, crop growth and development and on yield. For that, a scenario-based simulation approach based on the covariance matrix adaptation–evolution strategy (CMA-ES) was proposed to optimize both the spatiotemporal distribution of sowing dates and the irrigation schedules, and then evaluate wheat crop using the 2011–2012 growing season dataset. Six sowing scenarios were simulated and compared to identify the most optimal spatiotemporal sowing calendar. The obtained results showed that with reference to the existing sowing patterns, early sowing of wheat leads to higher yields compared to late sowing (from 7.40 to 5.32 t/ha). Compared with actual conditions in the study area, the spatial heterogeneity is highly reduced, which increased equity between farmers. The results also showed that the proportion of plots irrigated in time can be increased (from 40% to 82%) compared to both the actual irrigation schedules and to previous results of irrigation optimization, which did not take into consideration sowing dates optimization. Furthermore, considerable reduction of more than 40% of applied irrigation water can be achieved by optimizing sowing dates. Thus, the proposed approach in this study is relevant for irrigation managers and farmers since it provides an insight on the consequences of their agricultural practices regarding the wheat sowing calendar and irrigation scheduling and can be implemented to recommend the best practices to adopt. |
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| publications-2076 |
Peer reviewed articles |
2021 |
Nitu Ojha, Olivier Merlin, Abdehakim Amazirh, Nadia Ouaadi, Vincent Rivalland, Lionel Jarlan, Salah Er-Raki, Maria Jose Escorihuela |
A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data |
Sensors |
10.3390/s21217406 |
AI & Machine Learning |
Groundwater |
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Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration. |
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| publications-2077 |
Peer reviewed articles |
2020 |
Ouaadi, N., Jarlan, L., Ezzahar, J., Zribi, M., Khabba, S., Bouras, E., ... & Frison, P. L. |
Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sensing of Environment, 251, 112050. |
Remote Sensing of Environment |
10.1016/j.rse.2020.112050 |
AI & Machine Learning |
Uncategorized |
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No abstract available |
823965 |
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| publications-2078 |
Peer reviewed articles |
2022 |
Alhousseine Diarra, Lionel Jarlan,SaĂŻd Khabba, Michel Le Page, Salah Er-Raki, Riad Balaghi, Soufyane Charafi, Abdelghani Chehbouni andRafiq El Alami |
Medium-Resolution Mapping of Evapotranspiration at the Catchment Scale Based on Thermal Infrared MODIS Data and ERA-Interim Reanalysis over North Africa |
Remote Sensing |
10.3390/rs14205071 |
Data Management & Analytics |
River Basins |
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Accurate quantification of evapotranspiration (ET) at the watershed scale remains an important research challenge for managing water resources in arid and semiarid areas. In this study, daily latent heat flux (LE) maps at the kilometer scale were derived from the two-source energy budget (TSEB) model fed by the MODIS leaf area index (LAI), land surface temperature (LST) products, and meteorological data from ERA-Interim reanalysis from 2001 to 2015 on the Tensift catchment (center of Morocco). As a preliminary step, both ERA-Interim and predicted LE at the time of the satellite overpass are evaluated in comparison to a large database of in situ meteorological measurements and eddy covariance (EC) observations, respectively. ERA-Interim compared reasonably well to in situ measurements, but a positive bias on air temperature was highlighted because meteorological stations used for the evaluation were mainly installed on irrigated fields while the grid point of ERA-Interim is representative of larger areas including bare (and hot) soil. Likewise, the predicted LE was in good agreement with the EC measurements gathered on the main crops of the region during 15 agricultural seasons with a correlation coefficient r = 0.70 and a reasonable bias of 30 W/m2. After extrapolating the instantaneous LE estimates to ET daily values, monthly ET was then assessed in comparison to monthly irrigation water amounts provided by the local agricultural office added to CRU precipitation dataset with a reasonable agreement; the relative error was more than 89% but the correlation coefficient r reached 0.80. Seasonal and interannual evapotranspiration was analyzed in relation to local climate and land use. Lastly, the potential use for improving the early prediction of grain yield, as well as detecting newly irrigated areas for arboriculture, is also discussed. The proposed method provides a relatively simple way for obtaining spatially distributed daily estimates of ET at the watershed scale, especially for not ungauged catchments. |
823965 |
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| publications-2079 |
Peer reviewed articles |
2020 |
Román-CascĂłn, Carlos; Lothon, Marie; Lohou, Fabienne; Ojha, Nitu; Merlin, Olivier; AragonĂ©s, David; González-Dugo, MarĂa P.; Andreu, Ana; Pellarin, Thierry; Brut, Aurore; Soriguer, RamĂłn C.; DĂaz-Delgado, Ricardo; Hartogensis, Oscar; YagĂĽe, Carlos |
Can We Use Satellite-Based Soil-Moisture Products at High Resolution to Investigate Land-Use Differences and Land–Atmosphere Interactions? A Case Study in the Savanna |
Remote Sensing |
10.3390/rs12111701 |
Data Management & Analytics |
Precipitation & Ecological Systems |
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The use of soil moisture (SM) measurements from satellites has grown in recent years, fostering the development of new products at high resolution. This opens the possibility of using them for certain applications that were normally carried out using in situ data. We investigated this hypothesis through two main analyses using two high-resolution satellite-based soil moisture (SBSM) products that combined microwave with thermal and optical data: (1) The Disaggregation based on Physical And Theoretical scale Change (DISPATCH) and, (2) The Soil Moisture Ocean Salinity-Barcelona Expert Center (SMOS-BEC Level 4). We used these products to analyse the SM differences among pixels with contrasting vegetation. This was done through the comparison of the SM measurements from satellites and the measurements simulated with a simple antecedent precipitation index (API) model, which did not account for the surface characteristics. Subsequently, the deviation of the SM from satellite with respect to the API model (bias) was analysed and compared for contrasting land use categories. We hypothesised that the differences in the biases of the varied categories could provide information regarding the water retention capacity associated with each type of vegetation. From the satellite measurements, we determined how the SM depended on the tree cover, i.e., the denser the tree cover, the higher the SM. However, in winter periods with light rain events, the tree canopy could dampen the moistening of the soil through interception and conducted higher SM in the open areas. This evolution of the SM differences that depended on the characteristics of each season was observed both from satellite and from in situ measurements taken beneath a tree and in grass on the savanna landscape. The agreement between both types of measurements highlighted the potential of the SBSM products to investigate the SM of each type of vegetation. We found that the results were clearer for DISPATCH, whose data was not smoothed spatially as it was in SMOS-BEC. We also tested whether the relationships between SM and evapotranspiration could be investigated using satellite data. The answer to this question was also positive but required removing the unrealistic high-frequency SM oscillations from the satellite data using a low pass filter. This improved the performance scores of the products and the agreement with the results from the in situ data. These results demonstrated the possibility of using SM data from satellites to substitute ground measurements for the study of land–atmosphere interactions, which encourages efforts to improve the quality and resolution of these measurements. |
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| publications-2080 |
Peer reviewed articles |
2023 |
Claire Pascal; Sylvain Ferrant; Nemesio Rodriguez-Fernandez; Yann Kerr; Adrien Selles; Olivier Merlin |
Indicator of Flood-Irrigated Crops From SMOS and SMAP Soil Moisture Products in Southern India |
IEEE Geoscience and Remote Sensing Letters |
10.1109/lgrs.2023.3267825 |
Data Management & Analytics |
Irrigation Systems |
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No abstract available |
823965 |
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