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Estimating crop evapotranspiration (ETc) is of primary importance for irrigation management. The model commonly used for this purpose is the FAO-56 approach which consists of accurately estimating the basal crop coefficient Kcb. Historically, Kcb is derived from optical indices such as NDVI giving its sensitivity to vegetation cover fraction and to the Leaf Area Index. Nevertheless, optical data are disturbed by the presence of clouds. In this context, the objective of this work is to investigate the potential use of all-weather radar data as a substitute of NDVI to derive Kcb. The study is conducted over two winter wheat fields (Field 1 and Field 2) in Morocco, monitored during two agricultural seasons 2016-2017 and 2017-2018. Each field is equipped with an eddy covariance station allowing the estimation of ETc every 30 minutes. In addition, a weather station was installed over an alfalfa plot near the study fields. First, the backscattering coefficient and the interferometric coherence ( ρ at VV polarization) are derived from Sentinel-1 data with a 6-day revisit time and a spatial resolution of 10 m. Second, empirical relationships have between established between Kcb, on one hand, and the interferometric coherence and the polarization ratio, on the other hand and the results are also compared to the classical Kcb-NDVI (derived from Sentinel-2) method. The results show that good statistical metrics are obtained between Kcb and NDVI (R=0.77 and RMSE=0.14 for Field 1). Similar results are obtained also using ρ (R=0.76, RMSE=0.18). Finally, the Kcb is estimated from the calibrated relationships on one season and then used to estimate ETc. The results demonstrate reasonable estimates of ETc on Field 1 (R=0.70, RMSE=0.75 mm/day and bias=-0.18 mm/days) using Kcb-ρ. By contrast, a significant overestimations is highlighted both with (bias=0.73 mm/day) and NDVI (bias=1.46 mm/day) over Field 2. Interestingly, the Kcb-ρ relationship is more consistent in the estimation of ETc when changing from one field to another. These outcomes open new perspectives for the estimation of ETc from radar data as a potential substitute of NDVI in case of persistent cloud cover.

In recent decades, climate change has led to a sharp increase in water demand. Particularly in agriculture, this has put a great strain on already scarce water resources, increased the need for irrigation water, and led to overuse of groundwater. Therefore, sustainable management of water resources while maintaining good agricultural yield by monitoring crop water status is necessary for sustainable and rational management of these resources, especially in arid and semi-arid regions. For this purpose, a detailed knowledge of the different processes describing the diurnal water cycle of plants in a large area is essential. However, micrometeorological or physiological experimental measurements and their partitioning are laborious to perform and not very representative of large areas.In this regard, remote sensing is a particularly suitable tool for monitoring agricultural areas because of its global and repeated observation. Several studies have highlighted the sensitivity of radar data to vegetation water content especially over the rainforest with spatial scatterometers that observe differences between morning and evening acquisitions. On the other hand, in situ radar experiments with high temporal frequency have made it possible to analyze radar responses over tropical and boreal forests.This study relates to a similar experiment conducted on an olive orchard located in the semi-arid Mediterranean region of Chichaoua in central Morocco. It allows the acquisition of in situ C-band radar measurements in crop fields, which are acquired continuously, from a tower-based radar system, with a time step of 15 minutes.The temporal evolution of the interferometric coherence r is analyzed on different baselines Dt, ranging from 15 minutes to 30 days, for the main physiological stages of the olive tree. Four different two-month periods, from December 2020 to November 2022, are chosen as the main physiological stages based on field observations.The obtained results of r, especially at 15-min min-steps, show a global behavior similar to that observed in tropical and boreal forests: high values (r ≈1) are observed during the night (weak wind, vegetation resting), then a decrease/increase during the day mainly anti-symetric to the wind cycle. As over boreal and tropical forest, a decrease in r is observed before the wind picks up, with is time coincident with sap flows and ETR variations, traducing its sensitivity to water plant content.Results show that over olive orchard, the r diurnal cycle is less marked than over boreal and tropical forests, due to lower ETR rates and certainly due to a significant soil contribution over this less dense vegetation layer. Furthermore, r values decrease when temporal baselines increase, but values are still meaningful for Dt = 6 days (r = 0.3 compared to 0.6 for Dt = 15 min. for the summer period), available with Sentinel-1 missions.The present study provides particularly interesting results confirming the sensitivity of C-band coherence to vegetation water status, especially in the early morning. Further work needs to be pursued to verify if we are able to detect the water stress of these plants in semi-arid areas such as Chichaoua through coherence.

Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions.

International audience; Intact tropical rainforests have been exposed to severe droughts in recent decades, which may threaten their integrity, their ability to sequester carbon, and their capacity to provide shelter for biodiversity. However, their response to droughts remains uncertain due to limited high-quality, long-term observations covering extensive areas. Here, we examined how the upper canopy of intact tropical rainforests has responded to drought events globally and during the past 3 decades. By developing a long pantropical time series (1992 to 2018) of monthly radar satellite observations, we show that repeated droughts caused a sustained decline in radar signal in 93%, 84%, and 88% of intact tropical rainforests in the Americas, Africa, and Asia, respectively. Sudden decreases in radar signal were detected around the 1997–1998, 2005, 2010, and 2015 droughts in tropical Americas; 1999–2000, 2004–2005, 2010–2011, and 2015 droughts in tropical Africa; and 1997–1998, 2006, and 2015 droughts in tropical Asia. Rainforests showed similar low resistance (the ability to maintain predrought condition when drought occurs) to severe droughts across continents, but American rainforests consistently showed the lowest resilience (the ability to return to predrought condition after the drought event). Moreover, while the resistance of intact tropical rainforests to drought is decreasing, albeit weakly in tropical Africa and Asia, forest resilience has not increased significantly. Our results therefore suggest the capacity of intact rainforests to withstand future droughts is limited. This has negative implications for climate change mitigation through forest-based climate solutions and the associated pledges made by countries under the Paris Agreement.

Natural disasters like floods are happening worldwide. Due to their negative impact on different social, economic and environmental aspects need to monitor and map these phenomena have increased. In fact, to access the zones affected by the flood, we use open source remote sensing (RS) images acquired by optical and radar sensors. Furthermore, we present a method using Sentinel-1 images; we suggest applying Ground Range Detected (GRD) images. For this purpose, pre-processed built and provided by the European Space Agency (ESA), preserved by free software Sentinel Application Platform (SNAP) for data extraction around appropriate demand. Moreover, the principal objective of this article is to assess the capability of Sentinel-1 Synthetic Aperture Radar (SAR) images in order to visualize flood areas in the Inaouene watershed located in north-eastern of Morocco. The origin of this natural hazard is the combination of natural and anthropogenic factors that makes the watershed vulnerable with a sub-annual frequency. The results of this work help decision-makers and managers in the field of natural risk management and land-use planning to implement a strategy and action plan for the protection of the populations and the environment against the negative impact of floods.

Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness, and can be acquired in all weather conditions, thus has been used in a range of earth science disciplines. However, there is no single global radar data set that spans more than two decades. This has limited the use of radar data for trend analysis over extended time intervals. We here provide the first long-term (since 1992), high resolution (~8.9 km) satellite radar backscatter data set over global land areas, the C-band Scatterometer (CScat) data set, by fusing signals from European Remote Sensing satellite (ERS, 1992–2001, C-band, 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009, Ku-band, 13.4 GHz), and the Advanced Scatterometer (ASCAT, since 2007, C-band, 5.255 GHz). The six-year data gap between C-band ERS and ASCAT was filled out by modelling an equivalent C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. Towards this purpose, we first rescaled the signals from different sensors, pixel by pixel, using a new signal rescaling method that is robust to limited overlapping observations among sensors. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals, by modelling the signal differences from climatic variables (i.e., monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, Root Mean Square Error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r ≥ 0.93, RMSE ≤ 0.16, rRMSE ≤0.37) and pixel levels (median r across pixels ≥ 0.80, median RMSE ≤ 0.38, median rRMSE ≤ 0.64), suggesting high accuracy for the data merging procedure. The merged radar signal was then validated with a continuous ERS-2 data set available between 1995 and 2011. ERS-2 stopped working in full mode after 2001 but observations are occasionally available for a subset of the pixels until 2011. Because the period of 1995–2011 fully overlaps with the working period of QSCAT (1999–2009), comparing the merged radar signal against the ERS-2 data in 1995–2011 is the most direct validation available. We found concordant monthly dynamics between the merged radar signals and the ERS-2 signals during 1995–2011, with Pearson r value ranging from 0.79 to 0.98 across regions. These results evidenced that our merged radar data have a consistent C-band signal dynamic. The CScat data set (https://doi.org/10.6084/m9.figshare.20407857, Tao et al. 2022a) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture. The data set will be updated on a regular basis.&emsp

This work deals with crop monitoring in a semi-arid environment, the Mediterranean region, where up to 80-90% of already over-exploited water resources are used for irrigation. To help sustainable use of water resources in agriculture, more efforts have to be made to water use through a better assessment of crop water stress, evapotranspiration, and soil moisture at the plot scale on large areas.This study is focused on the diurnal and seasonal variation of the backscatter coefficient and the interferometric coherence over an olive orchard, located in the Chichaoua region (central Morocco), that has an area of 2.4ha, irrigated by drip system, with olives trees of 20-years-old. The study site was equipped, since May-2019, with a radar device consisting of 7 C-band horn-antennas at the top of a 20m-high-tower, that collects measurements at 4-polarizations allowing radar acquisitions in high temporal frequency with a timestep of 15min. An Eddy-covariance system has been also installed for measuring energy balance and the physiological functioning of olives trees with sapflow and dendrometer sensors on olives trunks. The study site is visible at the intersection of three different sentinel-1 orbit passes allowing to have six acquisitions every 10days to compare them with in-situ radar measurements at different incidence angles.Both the backscattering coefficient s0 and the interferometric coherences are analyzed. At a seasonal scale, all s0 polarizations show a low temporal frequency profile with amplitude lower than 3dB. For VV-polarization, in 2021, it is quite constant, with a slight decrease of 1 dB during the summer, while for 2020, an increase of 2dB is observed at the end of the spring. At HV-polarization no particular seasonal behavior can be seen. On the contrary, a marked diurnal profile is observed at VV-polarization, which is closely correlated with plant activity, with daily amplitude varying between 3dB in winter to 5dB in summer. The diurnal s0 signature is low during night, increases from 6AM, reaches its maximum during 2 to 6PM, and then decreases to recover its low value around midnight.Concerning the interferometric coherence r, similar behavior to the one observed over tropical forest is noted. The r daily evolution shows a clear diurnal cycle, with amplitude varying between 0.3 in winter to 0.7 in summer. During this latter, r is high (~0.9) at night when the wind and vegetation activity are low, and begins to decrease at 7AM, with vegetation activity start, before the wind picks up at 8AM, reaching minimum value at 7PM (~0,4). It then follows a rapid increase to reach its maximum value (0.9) at midnight. A high sensitivity to rainy events is also noted, corresponding to very low r values. It is worth noticing than r estimated between measurements separated by 6 days, show similar diurnal profiles, with r lower amplitude (0.2 to 0.4), suggesting it can be characterized by Sentinel-1 morning and evening observations. Additional work as to be carried on to relate this radar diurnal cycle to water cycle, that would be good omen to monitor water stress from spaceborne C-band radar sensors.

Irrigated agriculture is the largest consumer of freshwater in the world, particularly in the South Mediterranean region, that already suffers from water shortages. For a rational and sustainable management of water resources, monitoring the water stress status of plants can contribute to an optimal use of irrigation.C-band radar data have shown great potential for monitoring soil and vegetation hydric conditions. Over forests, several studies have observed a diurnal cycle in the backscattering coefficient that can reach up to 1 dB between morning and evening measurements acquired by sun-synchronous satellites. This cycle is assumed to be related to the physiological functioning of trees, in particular to the diurnal cycle of the vegetation water content. A recent study also identified a diurnal cycle in the temporal coherence measured over tropical forests. The authors hypothesized that transpiration was the main factor in the decrease in coherence at dawn, especially since winds are almost zero at that time of day. While the diurnal cycle of radar data is well documented over trees, the behavior of annual crops is yet to be investigated. In this context, the objective of this work is to present a preliminary study of this behavior over wheat by assuming that water movement in the plant could lead to a daily cycle of the interferometric coherence and backscattering coefficient.An experiment funded by LMI TREMA and TOSCA/CNES has been conducted over a winter wheat field in Morocco since January 2020. The experimental setup consists of six C-band antennas installed at the top of a 20 m high tower. It allows the full polarization acquisition of the backscattering coefficient and the interferometric coherence with a 15 minutes time step. The field is also equipped with an eddy covariance and weather stations that allow half-hourly measurements of evapotranspiration and wind speed. In addition to automatic measurements, field campaigns are also carried out to measure soil moisture, surface roughness, vegetation above-ground biomass and cover fraction.The preliminary analysis of in situ radar acquisitions over the 2020 agricultural season reveals the existence of a diurnal cycle of the interferometric coherence whose amplitude increases with the development of vegetation. In particular, a drop in coherence was observed at dawn. This drop is concomitant with the increase in evapotranspiration, which may indicate that it could be due to the sapflow. On the other hand, low coherence values are recorded at the end of the afternoon, which may be related to wind peaks. For the backscattering coefficient, a good agreement is observed between the evolution of its daily average and the evolution of evapotranspiration. These results, which need to be consolidated, demonstrate the existence of important dependencies between the C-band response and the physiological functioning of wheat, which opens insights for the monitoring of crop water status using radar data acquired at sub-daily timescale. This rather highlights the interest of a future geostationary radar mission.

Dans cet article, nous analysons la possibilité d’amélioration de la classification des cultures dans un périmètre irrigué du nord du Maroc en se basant sur la combinaison des données multi-temporelles de deux satellites (Sentinel-1 et Sentinel-2) avec l’inclusion de neuf indices. Le périmètre concerné (Loukkos), en plus de sa position stratégique, se caractérise par un climat méditerranéen avec une forte valeur écologique. Il présente une intense activité agricole avec une grande diversité des cultures dont le fonctionnement pourrait être affecté par le changement climatique. Afin de quantifier les besoins en eau, nous avons utilisé les séries d’images satellitaires acquises pour la période du 09/2017 au 08/2018. Les cartes produites pour trois niveaux de classification illustrent notre approche. L’étude a montré que les 10 canaux optiques, à 10 et 20 m de résolution spatiale, des données acquises par Sentinel-2 permettent d'obtenir de bonnes performances, avec un indice de kappa > 85% pour les sous-classes et une précision globale > 86%. Ces performances sont supérieures à celles obtenues avec des données radar acquises par Sentinel-1, avec des écarts de F-score inférieurs de 9% en moyenne, et pouvant aller jusqu'à 29% (sur le chêne-liège/Niveau SSC). Ni l'ajout d'indices radiométriques optiques, ni la combinaison des données optiques et radar n'apportent d'amélioration significative aux performances obtenues avec les données Sentinel-2. Afin d’exploiter les données obtenues, les travaux à venir se focaliseraient sur l’étude des profils temporels de chaque type de culture.Mots-clés : Sentinel-1, Sentinel-2, Classification supervisée, Forêt aléatoire, Cultures, Loukkos

La télédétection est un outil particulièrement adapté pour le suivi de l'occupation des sols en général ; il est aussi particulièrement utilisé pour le suivi de la déforestation. De par l'envoi des satellites Sentinel-1 et Sentinel-2 via le programme Copernicus, la communauté dispose maintenant de données gratuites, avec des périodes de revisites temporelles réduites, permettant au plus grand nombre de suivre efficacement la dynamique d'occupation d'une zone d'étude. Ce papier présente une méthode de suivi de l'occupation du sol se basant sur la combinaison de deux approches s'appuyant sur des outils logiciels Open Source (QGIS, Orfeo ToolBox, python). Nous combinons tout d'abord le suivi de l'occupation du sol à une échelle de temps annuelle utilisant des données Sentinel-1 et Sentinel-2 puis une approche de suivi des changements liés à la déforestation à une échelle de temps bi-mensuelle à partir des données Sentinel-1. Les résultats obtenus démontrent la bonne synergie de ces approches, qui permettent d'utiliser de façon complémentaire les données optiques et radar. En vue de rendre accessible la méthode proposée, tous les outils Open source utilisés sont disponibles sur ce lien : http://remotesensing4all.net/index.php/2018/09/11/kit-dutilisation-des-donnees-radar-sentinel-1-lors-de-latelier-radar-du-foss4g-fr-2018-2/.

Dans cet article, nous évaluons les performances de classification de trois algorithmes non paramétriques (kNN, RF et SVM) en utilisant les données multi-temporelles de trois satellites (Sentinel-1, Alos-Palsar-2 et Sentinel-2) et de leurs combinaisons. La zone d'étude choisie se caractérise par un climat méditerranéen subhumide et une topographie très accidentée qui rend la classification d’occupation du sol particulièrement difficile. En outre, elle contient une aire protégée nommée Jbel Moussa et présente une diversité biologique exceptionnelle. Afin de suivre le couvert végétal de cette dernière, nous avons acquis et prétraités les images satellitaires optiques et radar pour la période du 1er janvier au 31 décembre 2017. Ensuite, nous avons combiné les trois satellites, soit douze scénarios produits. Des cartes de classifications illustrent notre approche. Un total de trente-six classifications a été obtenu, en se basant sur sept classes : eau, bâtiment et infrastructures, sol nu, végétation peu dense, prairies, forêt peu dense et forêt dense. Les résultats ont montré que pour tous les scénarios, la précision globale la plus élevée a été produite par RF (53,03%-93,06%), suivie de kNN (49,16%-89,63%), tandis que SVM (47,86%-86,08%) a produit la précision de classification la plus faible. L'étude a également montré une similitude entre les performances de la combinaison des trois satellites et celles de Sentinel-2 seul. Les estimations de la superficie pour les différentes classes vont de 0,85 km2 (0,11% de la zone d'étude) à 326,84 km2 (41,31% de la zone d'étude)

C-band radar remote sensing is a suitable tool for monitoring agricultural areas on a large scale, providing access to information on vegetation such as plant biomass, or on the surface water content of the soil. Recent studies suggest that the water state and the physiological functioning of trees influence radar response leading to marked daily profiles of both radar backscattering coefficient and temporal coherence. The objective of this paper is to make a preliminary comparison between the temporal evolution of Sentinel-1 radar data and in situ radar measurements over a Mediterranean olive orchard located in Morocco. In situ radar data consist in quad polarizations measurements realized from a 20m high tower, every 15 minutes, for the period extending from May 2019 to October 2020.

C-band radar observations are well known to have great potentials for the monitoring of crop hydric conditions. Recent studies suggested that the observed difference of backscattering coefficient ( $\sigma_{o}$ ) between ascending and descending pass over tropical forest could be related to the physiological functioning of the trees. Likewise, the water movement within annual crops could lead to a daily cycle of both $\sigma_{o}$ and temporal coherence ( $\rho$ ). The objective of this paper is to present the preliminary results of an experiment carried out on a winter wheat field in Morocco that was instrumented with six C-band antennas during 2020 growing season. The preliminary results showed strong daily cycles of $\rho$ and $\sigma_{o}$ that are analyzed in relation to wind speed, surface soil moisture and evapotranspiration. This work open insights for the monitoring of the crop hydric status using C-band radar data acquired by Sentinel-1 and by potential future radar geostationary missions.

C-band radar observations have shown a high sensitivity to the water status of vegetation, including forests and crops. Several studies conducted mainly on forests have observed daily changes of the backscattering coefficients between ascending and descending orbits and have suggested that these differences are related to the diurnal cycle of vegetation water content. Likewise, the water movement within annual crops could be associated to change of the phase centre locations leading to a daily cycle of the interferometric coherence as well that has already been observed on tropical forest using C-band in situ acquisitions. In this context, an experimental setup composed of 4C-band antennas targeting an irrigated wheat field was installed at the top of a 20 m tower near Chichaoua (Morocco) from January to June 2020. The collected data includes measurements of the backscattering coefficient at both cross- and parallel polarizations and the interferometric coherence with a 15 mns time step. The field is also equipped with an eddy-correlation station for half hourly measurements of convective fluxes, soil moisture and temperature profiles. Simultaneously, measurement of above-ground biomass, leaf area index, canopy height and surface roughness are also carried out every 15-daysduring the agricultural season. The preliminary results of the experiment reveal the existence of strong correlation between the daily evolution of interferometric coherence and the physiological activity of wheat at dawn while the changes observed in the afternoon are ratherrelated to the wind peaks. For the backscattering coefficient, a good agreement is observed between the evolution of its daily average and the evolution of evapotranspiration. These open insights for the monitoring of the crops water status using radar dataacquired at sub-daily timescale.

In the south Mediterranean region already facing water scarcity, up to 80% of available water is used by irrigated agriculture. This work focuses on the analysis of the C-band response of a tree crop with in situ data acquired with a time step of 15 mns in the final objective of developing water stress detection approaches based on radar data. Focus is put on the daily cycle of the radar-backscattering coefficient and of the interferometric coherence. The site is located in the Chichaoua region (Morocco) was equipped in May 2019 with 6 C-band radar antennas installed on a 20 m tower. In parallel, automatic acquisitions at a half hourly time step of latent and sensible heat fluxes, sapflow, soil moisture and temperature profile together with manual measurements of LAI, soil roughness and above ground biomass every 15 days were carried out. The preliminary results show a strong daily cycle of the interferometric coherence with a significant drop of the coherence during daytime. The coherence loss at dawn occurred concurrently with the start of the sapflow while minimum values were observed in the afternoon when wind speed is maximum. A significant daily cycle of the backscattering coefficient is also prominent. The amplitude of the daily cycle decreased from the dormancy period in winter from up to 2dB to less than 1dB in summer when physiologic activity of the trees is at its maximum. These first results open perspectives for the monitoring of the hydric status of crops within the frame of future radar missions in geostationary orbit.

International audience; French Guiana forests cover 8 million hectares. With 98% of emerged land covered by forests, French Guiana is the area with the highest proportion of forest cover in the world. These forests are home to an exceptionally rich and diverse wealth of biodiversity that is both vulnerable and under threat due to high levels of pressure from human activity. As part of the French territory, French Guiana benefits from determined and continuous national efforts in the preservation of biodiversity and the environmental functionalities of ecosystems. The loss and fragmentation of forest cover caused by gold mining (legal and illegal), smallholder agriculture and forest exploitation, are considered as small-scale disturbances, although representing strong effects to vulnerable natural habitats, landscapes, and local populations. To monitor forest management programs and combat illegal deforestation and forest opening near-real time alerts system based on remote sensing data are required. For this large territory under frequent cloud cover, Synthetic-Aperture Radar (SAR) data appear to be the best adapted. In this paper, a method for forest alerts in a near-real time context based on Sentinel-1 data over the whole of French Guiana (83,534 km2) was developed and evaluated. The assessment was conducted for 2 years between 2016 and 2018 and includes comparisons with reference data provided by French Guiana forest organizations and comparisons with the existing University of Maryland Global Land Analysis and Discovery Forest Alerts datasets based on Landsat data. The reference datasets include 1,867 plots covering 2,124.5 ha of gold mining, smallholder agriculture and forest exploitation. The validation results showed high user accuracies (96.2%) and producer accuracies (81.5%) for forest loss detection, with the latter much higher than for optical forest alerts (36.4%). The forest alerts maps were also compared in terms of detection timing, showing systematic temporal delays of up to one year in the optical method compared to the SAR method. These results highlight the benefits of SAR over optical imagery for forest alerts detection in French Guiana. Finally, the potential of the SAR method applied to tropical forests is discussed. The SAR-based map of this study is available on http://cesbiomass.net/.

Radar data at C-band has shown great potential for the monitoring of soil and canopy hydric conditions of wheat crops. In this study, the C-band Sentinel-1 time series including the backscattering coefficients u(0) at VV and VH polarization, the polarization ratio (PR) and the interferometric coherence p are first analyzed with the support of experimental data gathered on three plots of irrigated winter wheat located in the Haouz plain in the center of Morocco covering five growing seasons. The results showed that p and PR are tightly related to the canopy development. p is also sensitive to soil preparation. By contrast, u(0) was found to be widely linked to changes in surface soil moisture (SSM) during the first growth stages when Leaf Area Index remains moderate (< 1.5 m(2)/m(2)). In addition, drastic changes in the crop geometry associated to heading had a strong impact on the C-band u(0), in particular for VH polarization. The coupled water cloud and Oh models (WCM) were then calibrated and validated on the study sites. The comparison between the predicted and observed u0 yielded a root mean square error (RMSE) values ranging from 1.50 dB to 2.02 dB for VV and between 1.74 dB to 2.52 dB for VH with significant differences occurring in the second part of the season after heading. Finally, new approaches based on the inversion of the WCM for SSM retrieval over wheat fields were proposed using Sentinel-1 radar data only. To this objective, the dry above-ground biomass (AGB) and the vegetation water content (VWC) were retrieved from the interferometric coherence and the PR. The relationships were then used as the vegetation descriptor in the WCM. The best retrieval results were obtained using the relationship between pVV and the AGB (R and RMSE of 0.82, 0.05 m3/m3 respectively and no bias). The new retrieval approaches were then applied to a large database covering a rainfed field in Morocco and 18 plots of rainfed and irrigated wheat of the Kairouan plain (Tunisia) and compared to other classical techniques of SSM retrieval including simple linear relationships between SSM and u0. The method based on the WCM and the pVV-AGB relationships also provided with slightly better results than the others on the validation database (r = 0.75, RMSE = 0.06 m(3)/m(3) and bias = 0.01 m(3)/m(3) over the 18 plots of Tunisia) but the simple linear relationships performed also reasonably well (r = 0.62, RMSE = 0.07, bias = -0.01 in Tunisia for instance). This study opens perspectives for high resolution soil moisture mapping from Sentinel-1 data over south Mediterranean wheat crops and in fine, for irrigation scheduling and retrieval through the assimilation of these new products in an evapotranspiration model.

High spatial and temporal resolution products of Sentinel-1 are used for surface soil moisture (SSM) mapping over wheat fields in semi-arid areas. Within these regions, monitoring the water-use is a critical aspect for optimizing the management of the limited water resources via irrigation monitoring. SSM is one of the principal quantities affecting microwave remote sensing. This sensitivity has been exploited to estimate SSM from radar data, which has the advantages of providing data independent of illumination and weather conditions. In addition, with the use of Sentinel-1 products, the spatial and temporal resolution is greatly improved. Within this context, the main objective of this work is estimate SSM over wheat fields using an approach based on the use of C-band Sentinel-1 radar data only. Over the study site, field measurement are collected during 2016-2017 and 2017-2018 growing seasons over two fields of winter wheat with drip irrigation located in the Haouz plain in the center of Morocco. Data of other sites in Morocco and Tunisia are taken for validation purposes. The validation database contains a total number of 20 plots divided between irrigated and rainfed wheat plots. Two different information extracted from Sentinel-1 products are used: the backscattering coefficient and the interferometric coherence. A total number of 408 GRD and 419 SLC images were processed for computing the backscattering coefficient and the interferometric coherence, respectively. The analysis of Sentinel-1 time series over the study site show that coherence is sensitive to the development of wheat, while the backscatter coefficient is widely linked to changes in surface soil moisture. Later on, the Water Cloud Model coupled with the Oh et al, 1992 model were used for better understand the backscattering mechanism of wheat canopies. The coupled model is calibrated and validated over the study site and it proved to goodly enough reproduce the Sentinel-1 backscatter with RMSE ranging from 1.5 to 2.52 dB for VV and VH using biomass as a descriptor of wheat. On the other side, the analysis show that coherence is well correlated to biomass. Thus, the calibrated model is used in an inversion algorithm to retrieve SSM using the Sentinel-1 backscatter and coherence as inputs. The results of inversion show that the proposed new approach is able to retrieve the surface soil moisture at 35.2° for VV, with R=0.82, RMSE=0.05m3/m3 and no bias. Using the validation database of Morocco and Tunisia, R is always greater than 0.7 and RMSE and bias are less than 0.008 m3/m3 and 0.03 m3/m3, respectively even that the incidence angle is higher (40°). In order to assess its quality, the approach is compared to four SSM retrieval methods that use radar and optical data in empirical and semi-empirical approaches. Results indicate that the proposed approach shows an improvement of SSM retrieval between 17% and 42% compared to other methods. Finally, the validated new approach is used for SSM mapping, with a spatial resolution of 10*10 m, over irrigated perimeters of wheat in Morocco.

This paper addresses the question of temporal decorrelation at C and L-band in the case of olive tree plantations, considering the very recent tower-based experiments in Morocco, as well as the preliminary results conducted in preparation phases. Based on the expertise gained from the TropiScat-1&2 campaigns, the presented experiments also consist in the acquisition of dense time series using a Vector Network Analyser (VNA) connected to several antennas, herein dedicated to L and C-band data. Likewise, this experiment benefit from auxiliary measurements (meteorological and in-situ data) in order to better understand the processes behind temporal decorrelation, which remains a key issue for future repeat-pass, tandem or geostationary missions. Our first results highlight the importance of the acquisition time during the day, and also rise the challenge on how to disentangle the combined effects of water content variations and wind induced displacements driven by solar convection.