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| Description de la publication | This radar remote sensing database is composed of processed Sentinel-1 products in addition to field measurements of soil and vegetation (wheat). The data are collected during three years (2016-2019) on three irrigated wheat fields in the center of Morocco. The GRDH and SLC Sentinel-1 products are processed using SNAP platform to extract the backscattering coefficient and the interferometric coherence. NDVI (normalized difference vegetation index) is computed from Sentinel-2 products, available on Theia website. The soil measurements consist of surface roughness and surface and root zone soil moisture. Vegetation variables include canopy height, leaf area index, canopy cover fraction, vegetation water content and above-ground fresh and dry biomass. Meteorological data collected during the period 2016-2019 are also provided in addition to the dates and amounts of irrigation applied. |
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| Auteur(s) par ordre alphabétique |
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| DOI | https://doi.org/10.23708/8D6WQC |
| Date de publication | 2022-03-09 |
| Documents associés au data paper |
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| Données de relevés de terrain utilisées |
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| Jeux de données de sites instrumentés utilisés | |
| Jeux de données d'images utilisés | - |
| Description de la publication | Nitrous oxide (N2O) emissions were measured and compared on 2 typical crop rotations of a grain farm and a dairy farm with feed cropping, over 5 years (from 2012 to 2016) in southwestern France. The annual N2O emissions of the 5 typical rotational crops of the region (summer crops: irrigated maize and sunflower; winter crops: winter wheat, rapeseed and barley) varied from 0.95 ± 0.88 to 7.96 ± 1.73 kgN ha-1, with the highest values observed on the dairy farm plot and for summer crops. N2O emissions were analysed on a daily, monthly, seasonal and annual basis, and correlated with their main direct or indirect drivers, i. e. water and nitrogen (mineral or organic) supply amount, rotational crops, vegetation covering and tillage. We observed a marked seasonal pattern of N2O emission peaks. On average, more than 50% of N2O emissions occurred during spring for summer crops, and more than 40% occurred in winter for winter crops. We have identified agricultural practices that increase N2O emissions. In particular, our results show that when the soil is left bare or with limited crop development, spring mineralization of organic N residues (from previous crop or winter cover crop) results in N losses, partly as emissions of N2O, which are detrimental to agronomic performance (low NUE). We also conducted an agronomic assessment of annual N2O emissions versus nitrogen surplus and nitrogen use efficiency (NUE), which lead us to discuss agricultural practices that may mitigate N2O emissions while optimizing agronomic and economic performance of crops. Indeed, we point out that N surplus and N fate may be controlled through the right timing of sowing, cover crop, irrigation and fertilization. |
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| Auteur(s) par ordre alphabétique |
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| DOI | https://doi.org/10.1016/j.agrformet.2022.109093 |
| Date de publication | 2022-08-15 |
| Documents associés au data paper |
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| Données de relevés de terrain utilisées |
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| Jeux de données de sites instrumentés utilisés | |
| Jeux de données d'images utilisés | - |
| Description de la publication | In this article, we propose an approach based on Gaussian processes (GPs) for large-scale land cover pixel-based classification with Sentinel-2 satellite image time series (SITS). We used a sparse approximation of the posterior combined with variational inference to learn the GP’s parameters. We applied stochastic gradient descent and GPU computing to optimize our GP models on massive datasets. The proposed GP model can be trained with hundreds of thousands of samples, compared to a few thousands for traditional GP methods. Moreover, we included the spatial information by adding the geographic coordinates into the GP’s covariance function to efficiently exploit the spatio-spectro-temporal structure of the SITS. We ran experiments with Sentinel-2 SITS of the full year 2018 over an area of 200000 km2 (about 2 billion pixels) in the south of France, which is representative of an operational setting. Adding the spatial information significantly improved the results in terms of classification accuracy. With spatial information, GP models have an overall accuracy of 79.8. They are more than three points above random forest (the method used for current operational systems) and more than one point above a multilayer perceptron. Compared to a transformer-based model (which provides state-of-the-art results in the literature, but is not applied in operational systems), GP models are only one point below. |
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| Auteur(s) par ordre alphabétique |
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| DOI | 10.1109/TGRS.2023.3234527 |
| Date de publication | 2023-01-05 |
| Documents associés au data paper |
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| Données de relevés de terrain utilisées | - |
| Jeux de données de sites instrumentés utilisés | - |
| Jeux de données d'images utilisés |
| Description de la publication | This article presents a monitoring of land cover/use by satellite images over an 11-year period (2006-2016), over a study site located in southwestern France near Toulouse. Time series of optical data are acquired by Spot and Landsat, which deliver images in multispectral mode with high spatial resolution (10-30 m). The detection of the different types of land cover/use (crops, grasslands, water, urban and wood) is produced every year. It is based on national reference geographical data and a random forest algorithm. The classifications are characterized by a high level of performance, with an average kappa of 0.83 (OA = 0.85). The performance by land cover/use type is related to their representativeness, dates and number of acquisitions, and the resolution of satellite images. The results allow analyzing the evolution of the three main crops (wheat,sunflower and corn). |
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| Auteur(s) par ordre alphabétique |
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| DOI | |
| Date de publication | 2022-12-12 |
| Documents associés au data paper |
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| Données de relevés de terrain utilisées | - |
| Jeux de données de sites instrumentés utilisés | - |
| Jeux de données d'images utilisés |
| Description de la publication | In this paper, three datasets are described. The first dataset is a complete set of GNSS-R (GNSS-R: Global Navigation Satellite System – Reflectometry) airborne data. This dataset has been generated with the data acquired with the GLObal navigation satellite system Reflectometry Instrument (GLORI) developed at Centre d’Etudes Spatiales de la Biosphère (CESBIO), during the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign in north-eastern Spain during the summer of 2021. The two other datasets (available in the SIE) are ground truth sets of measurements which have been acquired simultaneously with the flights. The in-situ measurements dataset consists in soil measurements (surface soil moisture, surface roughness, Leaf Area Index (LAI)) over 24 reference fields. The land use dataset provides a land use map (along with 385 ground truth plots) over the studied site for GLORI data evaluation. |
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| Auteur(s) par ordre alphabétique |
, , , , , , , |
| DOI | https://doi.org/10.1016/j.dib.2023.109850 |
| Date de publication | 2023-11-26 |
| Documents associés au data paper |
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| Données de relevés de terrain utilisées |
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| Jeux de données de sites instrumentés utilisés | - |
| Jeux de données d'images utilisés | - |
