| publications-2791 |
Peer reviewed articles |
2017 |
Marek Stibal, James A. Bradley, Jason E. Box |
Ecological Modeling of the Supraglacial Ecosystem: A Process-based Perspective |
Frontiers in Earth Science |
10.3389/feart.2017.00052 |
Uncategorized |
Uncategorized |
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No abstract available |
657533 |
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| publications-2792 |
Peer reviewed articles |
2019 |
Abdelhakim Amazirh, Olivier Merlin, Salah Er-Raki |
Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data |
ISPRS Journal of Photogrammetry and Remote Sensing |
10.1016/j.isprsjprs.2019.02.004 |
Uncategorized |
Uncategorized |
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No abstract available |
645642 |
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| publications-2793 |
Peer reviewed articles |
2018 |
Abdelhakim Amazirh, Olivier Merlin, Salah Er-Raki, Qi Gao, Vincent Rivalland, Yoann Malbeteau, Said Khabba, Maria José Escorihuela |
Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil |
Remote Sensing of Environment |
10.1016/j.rse.2018.04.013 |
Uncategorized |
Uncategorized |
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No abstract available |
645642 |
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| publications-2794 |
Peer reviewed articles |
2018 |
Olivier Merlin, Luis Olivera-Guerra, Bouchra Aït Hssaine, Abdelhakim Amazirh, Zoubair Rafi, Jamal Ezzahar, Pierre Gentine, Said Khabba, Simon Gascoin, Salah Er-Raki |
A phenomenological model of soil evaporative efficiency using surface soil moisture and temperature data |
Agricultural and Forest Meteorology |
10.1016/j.agrformet.2018.04.010 |
Uncategorized |
Natural Water Bodies |
|
No abstract available |
645642 |
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| publications-2795 |
Peer reviewed articles |
2019 |
Zoubair Rafi, Olivier Merlin, Valérie Le Dantec, Saïd Khabba, Patrick Mordelet, Salah Er-Raki, Abdelhakim Amazirh, Luis Olivera-Guerra, Bouchra Ait Hssaine, Vincent Simonneaux, Jamal Ezzahar, Francesc Ferrer |
Partitioning evapotranspiration of a drip-irrigated wheat crop: Inter-comparing eddy covariance-, sap flow-, lysimeter- and FAO-based methods |
Agricultural and Forest Meteorology |
10.1016/j.agrformet.2018.11.031 |
Uncategorized |
Wastewater Treatment Plants |
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No abstract available |
645642 |
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| publications-2796 |
Peer reviewed articles |
2018 |
Qi Gao, Mehrez Zribi, Maria Escorihuela, Nicolas Baghdadi, Pere Segui |
Irrigation Mapping Using Sentinel-1 Time Series at Field Scale |
Remote Sensing |
10.3390/rs10091495 |
Uncategorized |
Uncategorized |
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The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change. |
645642 |
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| publications-2797 |
Peer reviewed articles |
2018 |
Mireia Fontanet, Daniel Fernà ndez-Garcia, Francesc Ferrer |
The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields |
Hydrology and Earth System Sciences |
10.5194/hess-22-5889-2018 |
Data Management & Analytics |
Natural Water Bodies |
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Abstract. Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1 km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site. |
645642 |
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| publications-2798 |
Peer reviewed articles |
2018 |
Luis Olivera-Guerra, Olivier Merlin, Salah Er-Raki, Saïd Khabba, Maria Jose Escorihuela |
Estimating the water budget components of irrigated crops: Combining the FAO-56 dual crop coefficient with surface temperature and vegetation index data |
Agricultural Water Management |
10.1016/j.agwat.2018.06.014 |
Uncategorized |
Natural Water Bodies |
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No abstract available |
645642 |
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| publications-2799 |
Peer reviewed articles |
2018 |
Bouchra Hssaine, Jamal Ezzahar, Lionel Jarlan, Olivier Merlin, Said Khabba, Aurore Brut, Salah Er-Raki, Jamal Elfarkh, Bernard Cappelaere, Ghani Chehbouni |
Combining a Two Source Energy Balance Model Driven by MODIS and MSG-SEVIRI Products with an Aggregation Approach to Estimate Turbulent Fluxes over Sparse and Heterogeneous Vegetation in Sahel Region (Niger) |
Remote Sensing |
10.3390/rs10060974 |
Uncategorized |
Natural Water Bodies |
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Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere interactions. This study considered the issue of using a thermal-based two-source energy model (TSEB) driven by MODIS (Moderate resolution Imaging Spectroradiometer) and MSG (Meteosat Second Generation) observations in conjunction with an aggregation scheme to derive area-averaged H and LE over a heterogeneous watershed in Niamey, Niger (Wankama catchment). Data collected in the context of the African Monsoon Multidisciplinary Analysis (AMMA) program, including a scintillometry campaign, were used to test the proposed approach. The model predictions of area-averaged turbulent fluxes were compared to data acquired by a Large Aperture Scintillometer (LAS) set up over a transect about 3.2 km-long and spanning three vegetation types (millet, fallow and degraded shrubs). First, H and LE fluxes were estimated at the MSG-SEVIRI grid scale by neglecting explicitly the subpixel heterogeneity. Moreover, the impact of upscaling the model’s inputs was investigated using in-situ input data and three aggregation schemes of increasing complexity based on MODIS products: a simple averaging of inputs at the MODIS resolution scale, another simple averaging scheme that considers scintillometer footprint extent, and the weighted average of inputs based on the footprint weighting function. The H and LE simulated using the footprint weighted method were more accurate than for the two other aggregation rules despite the heterogeneity of the landscape. The statistical values are: correlation coefficient (R) = 0.71, root mean square error (RMSE) = 63 W/m2 and mean bias error (MBE) = −23 W/m2 for H and an R = 0.82, RMSE = 88 W/m2 and MBE = 45 W/m2 for LE. This study opens perspectives for the monitoring of convective and evaporative fluxes over heterogeneous landscape based on medium resolution satellite products. |
645642 |
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| publications-2800 |
Peer reviewed articles |
2017 |
Y. Malbéteau, O. Merlin, S. Gascoin, J.P. Gastellu, C. Mattar, L. Olivera-Guerra, S. Khabba, L. Jarlan |
Normalizing land surface temperature data for elevation and illumination effects in mountainous areas: A case study using ASTER data over a steep-sided valley in Morocco |
Remote Sensing of Environment |
10.1016/j.rse.2016.11.010 |
Uncategorized |
Natural Water Bodies |
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No abstract available |
645642 |
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