Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

The Theia Snow collection routinely provides high-resolution maps of the snow-covered area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide, including the main mountain regions in western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Theia Snow collection contains four classes: snow, no snow, cloud and no data. We present the algorithm to generate the snow products and provide an evaluation of the accuracy of Sentinel-2 snow products using in situ snow depth measurements, higher-resolution snow maps and visual control. The results suggest that the snow is accurately detected in the Theia snow collection and that the snow detection is more accurate than the Sen2Cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed on average 5&thinsp;d after the image acquisition as raster and vector files via the Theia portal (https://doi.org/10.24400/329360/F7Q52MNK).</p

A surface albedo product at high spatial resolution from a combination of sentinel-2 and Landsat-8 data: the role of surface radiative forcing from agriculture areas as a major contribution to an abatement of carbon emission.

International audienceThe surface albedo is an Essential Climate Variable (ECV) that needs to be generated on a regular basis in order to ensure continuous estimates as a contribution of the radiation budget to water and carbon balance. First of all, the removal of atmospheric effects must be properly handled. Thus, the consistency of the MAJA method for Sentinel-2 and Landsat-8 is clearly an asset. The present developments are intended to generate a time evolving surface albedo product at the enhanced resolution of 10m to foster an advanced research in agricultural area. Further, surface albedo product will be considered to estimate the radiation forcing due to the surface since for instance maintaining permanently vegetation, instead of having episodes of bare soil, would contribute to cool the atmosphere notably, thereby reducing carbon emission.Hence, the method – applied to become operational since inherited from Copernicus Global Land Service - makes use of the well-established approach based on a semi-empirical BRDF kernel-driven model. Such model is applied to Level 2a data sets of Sentinel-2 and Landsat-8. BRDF coefficients can serve to perform a normalization of the data and also to compute the spectral albedo in weighing the angular integrated kernels. Narrow to broadband albedo conversion rely on PROSAIL model ditto. Surface albedo products are generated with a quality control and an uncertainty assessment.A composite period of several weeks must be considered to gather sufficient observations in order to build a BRDF product from which the surface albedo is derived. The potential impact due to the frequency of revisit offered by Sentinel2-A & -B plus Landsat-8 is assessed through a time evolution analysis. For time being, the surface albedo product is refreshed during synthesis periods about 10 days or less based on an update of its intensity. Demonstrative results are shown for selected time periods of 2018. The validation is carried on for two anchor ICOS-like (Integrated Carbon Observation System) stations operated by CESBIO and located near Toulouse, which are covered by crops (maize, wheat, and sunflower, merely)

VENμS: Mission Specificities, Products Features and In-orbit Absolute Calibration

Earth observation satellites like Sentinel-2 or Landsat 8 have already demonstrated the importance of a global coverage associated with high resolution (about 10 m) for regional and country scales applications. These applications, such as detailed land-cover mapping, agri-environment policies, water management, vegetation primary productivity and yield estimates, are crucial for defining global change mitigation or adaptation policies. To prepare the future earth observations systems, users raised one question about the increasing of the revisit period in order to limit the impact of cloud-coverage on the applications and to capture rapid phenomena. In this context, VENµS products offer an undeniable added value to explore the benefit of expanding the time rate of high resolution acquisition in visible and near infrared spectral bands. VENµS is a joint space system venture of Israeli and French governments for Earth observation (EO). The scientific mission focuses on vegetation and land surface monitoring. VENµS was launched on August 1st, 2017. It provides 5 and 10 m resolution images in 12 shortwave spectral bands every two days over a set of 110 scientific sites, with constant viewing angle and overpass time. This article presents the objectives of the mission, its main characteristics and available products. A special focus is made on the in-orbit absolute calibration, based on vicarious techniques, including specific capabilities such as calibration using Moon images. The process of inter-calibration with Sentinel-2 through simultaneous nadir observation will be explained, and the results detailed. VENµS data are freely available to everybody for peaceful and non-commercial uses on the French Theia land data center: http://www.theia-land.fr. Continuous observations will be performed all along the scientific mission duration, until mid-2020

Correction of aerosol effects on multi-temporal images acquired with constant viewing angles : application to Formosat-2 images

This paper presents a new method developed for the atmospheric correction of the images that will be acquired by the Ven mu s satellite after its launch expected in early 2010. Every two days, the Ven mu s mission will provide 10 m resolution images of 50 sites, in 12 narrow spectral bands ranging from 415 run to 910 nm. The sun-synchronous Ven mu s orbit will have a 2-day repeat cycle, and the images of a given site will always be acquired from the same place, at the same local hour, with constant observation angles. Thanks to these characteristics, the directional effects will be considerably reduced since only the solar angles will slowly vary with time. The algorithm that will be implemented for the atmospheric correction of Ven mu s data is being developed using both radiative transfer simulations and the actual data acquired by the Formosat-2 satellite. Because of its one-day sun-synchronous repeat cycle, Formosat-2 acquires images with a sun-viewing geometry close to the one Ven mu s will offer. With this geometry, reflectance time series are free from directional effects on the short term, a feature which reduces the number of unknowns to retrieve. The atmospheric corrections algorithm exploits this feature and the two following assumptions: - Aerosol optical properties vary quickly with time but slowly with location. - Surface reflectances vary quickly with location but slowly with time. Consequently, the top of atmosphere reflectance short term variations (10 to 15 days) are mainly due to the variations of aerosol optical properties, and it is thus possible to use these variations to characterise the atmospheric aerosols and to retrieve surface reflectances. This paper first describes the aerosol inversion method we developed and its results when applied to simulations. In the second part, we show the first tests of the method against three data sets acquired by Formosat-2 images with constant observation angles. Aeronet sun photometers measurements were available on all sites. Formosat-2 estimates of optical thickness compare favourably with Aeronet in situ measurements, leading to a noticeable improvement of the smoothness of time series of surface reflectances after atmospheric correction

Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops

The objective of this study is to get a better understanding of radar signal over irrigated wheat fields and to assess the potentialities of radar observations for the monitoring of soil moisture. Emphasis is put on the use of high spatial and temporal resolution satellite data (Envisat/ASAR and Formosat-2). Time series of images were collected over the Yaqui irrigated area (Mexico) throughout one agricultural season from December 2007 to May 2008, together with measurements of soil and vegetation characteristics and agricultural practices. The comprehensive analysis of these data indicates that the sensitivity of the radar signal to vegetation is masked by the variability of soil conditions. On-going irrigated areas can be detected all over the wheat growing season. The empirical algorithm developed for the retrieval of topsoil moisture from Envisat/ASAR images takes advantage of the Formosat-2 instrument capabilities to monitor the seasonality of wheat canopies. This monitoring is performed using dense time series of images acquired by Formosat-2 to set up the SAFY vegetation model. Topsoil moisture estimates are not reliable at the timing of plant emergence and during plant senescence. Estimates are accurate from tillering to grain filling stages with an absolute error about 9% (0.09 m&lt;sup&gt;3&lt;/sup&gt; m&lt;sup&gt;&amp;minus;3&lt;/sup&gt;, 35% in relative value). This result is attractive since topsoil moisture is estimated at a high spatial resolution (i.e. over subfields of about 5 ha) for a large range of biomass water content (from 5 and 65 t ha&lt;sup&gt;&amp;minus;1&lt;/sup&gt; independently from the viewing angle of ASAR acquisition (incidence angles IS1 to IS6)

Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops

The objective of this study is to get a better understanding of radar signal over irrigated wheat fields and to assess the potentialities of radar observations for the monitoring of soil moisture. Emphasis is put on the use of high spatial and temporal resolution satellite data (Envisat/ASAR and Formosat-2). Time series of images were collected over the Yaqui irrigated area (Mexico) throughout one agricultural season from December 2007 to May 2008, together with measurements of soil and vegetation characteristics and agricultural practices. The comprehensive analysis of these data indicates that the sensitivity of the radar signal to vegetation is masked by the variability of soil conditions. On-going irrigated areas can be detected all over the wheat growing season. The empirical algorithm developed for the retrieval of topsoil moisture from Envisat/ASAR images takes advantage of the Formosat-2 instrument capabilities to monitor the seasonality of wheat canopies. This monitoring is performed using dense time series of images acquired by Formosat-2 to set up the SAFY vegetation model. Topsoil moisture estimates are not reliable at the timing of plant emergence and during plant senescence. Estimates are accurate from tillering to grain filling stages with an absolute error about 9% (0.09 m(3) m(-3), 35% in relative value). This result is attractive since topsoil moisture is estimated at a high spatial resolution (i.e. over subfields of about 5 ha) for a large range of biomass water content (from 5 and 65 t ha(-1)) independently from the viewing angle of ASAR acquisition (incidence angles IS1 to IS6)

VENμS Mission Evolutions and Radiometric Performances During VM5 In-orbit Test Phase

VENμS (Vegetation and Environment New micro (μ) Satellite) is a micro satellite launched in 2017 by the Israeli Space Agency (ISA) and the French Centre National d’Etudes Spatiales (CNES). VENμS is a research satellite that embarks two very different devices, an electric Hall Effect thruster, and a multispectral optical camera. This article focuses on the multispectral camera. Since March 2022, VENμS has begun the final phase of its mission called VM5. At the beginning of this project, it had been planned to divide the mission in 3 phases specially to use the small thrusters, which are dedicated to a technological mission. Regarding to the highly satisfactory results of the first scientific phase VM1, it has been decided to create two new phases to finally reach an orbit at 560 km. This new orbit allows attaining increased characteristics such as a 1-day revisit cycle, a ground resolution of about 4m and a swath of around 20 km. This article presents the different phases of the mission, its main characteristics and available products. A special focus is made on the radiometric calibration during VM5 in-orbit test phase. These activities include equalization (dark and nonuniformity coefficients), absolute calibration using desert and Moon images and performance assessment such as SNR or FPN. The results of each part are detailed. VENμS data of the VM1 phase are freely available to everybody for peaceful and non-commercial uses on the French Theia land data center: http://www.theia-land.fr

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

The temporal frequency of the thermal data provided by current spaceborne high-resolution imagery systems is inadequate for agricultural applications. As an alternative to the lack of high-resolution observations, kilometric thermal data can be disaggregated using a green (photosynthetically active) vegetation index e.g. NDVI (Normalized Difference Vegetation Index) collected at high resolution. Nevertheless, this approach is only valid in the conditions where vegetation temperature is approximately uniform. To extend the validity domain of the classical approach, a new methodology is developed by representing the temperature difference between photosynthetically and non-photosynthetically active vegetation. In practice, both photosynthetically and non-photosynthetically active vegetation fractions are derived from a time series of Formosat-2 shortwave data, and then included in the disaggregation procedure. The approach is tested over a 16 km by 10 km irrigated cropping area in Mexico during a whole agricultural season. Kilometric MODIS (MODerate resolution Imaging Spectroradiometer) surface temperature is disaggregated at 100 m resolution, and disaggregated temperature is subsequently compared against concurrent ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data. Statistical results indicate that the new methodology is more robust than the classical one, and is always more accurate when fractional non-photosynthetically active vegetation cover is larger than 0.10. The mean correlation coefficient and slope between disaggregated and ASTER temperature is increased from 0.75 to 0.81 and from 0.60 to 0.77, respectively. The approach is also tested using the MODIS data re-sampled at 2 km resolution. Aggregation reduces errors in MODIS data and consequently increases the disaggregation accuracy