[Show abstract][Hide abstract] ABSTRACT: The recent and forthcoming availability of high resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the perspective offered by improving the crop growth dynamic simulation using the distributed agro-hydrological model, Topography based Nitrogen transfer and Transformation (TNT2), using LAI map series derived from 105 Formosat-2 (F2) images during the period 2006–2010. The TNT2 model (Beaujouan et al., 2002), calibrated with discharge and in-stream nitrate fluxes for the period 1985-2001, was tested on the 2006–2010 dataset (climate, land use, agricultural practices, discharge and nitrate fluxes at the outlet). A priori agricultural practices obtained from an extensive field survey such as seeding date, crop cultivar, and fertilizer amount were used as input variables. Continuous values of LAI as a function of cumulative daily temperature were obtained at the crop field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics with a priori input parameters showed an temporal shift with observed LAI profiles irregularly distributed in space (between field crops) and time (between years). By re-setting seeding date at the crop field level, we proposed an optimization method to minimize efficiently this temporal shift and better fit the crop growth against the spatial observations as well as crop production. This optimization of simulated LAI has a negligible impact on water budget at the catchment scale (1 mm yr−1 in average) but a noticeable impact on in-stream nitrogen fluxes (around 12%) which is of interest considering nitrate stream contamination issues and TNT2 model objectives. This study demonstrates the contribution of forthcoming high spatial and temporal resolution products of Sentinel-2 satellite mission in improving agro-hydrological modeling by constraining the spatial representation of crop productivity.
Hydrology and Earth System Sciences Discussions 07/2014; 11:7689-7732.
[Show abstract][Hide abstract] ABSTRACT: This article presents the MISTIGRI project of a microsatellite developed by the French space agency Centre National d'Etudes Spatiales (CNES) in cooperation with Spain (Image Processing Laboratory of the University of Valencia and Centro para el Desarrollo Tecnológico Industrial (CDTI)). MISTIGRI is a mission that has the originality of combining a high spatial resolution (∼50 m) with a daily revisit in the thermal infrared (TIR). MISTIGRI is an experimental mission devoted to demonstrate the potential of such TIR data for future operational missions. The scientific goals and expected applications of the mission are described: they encompass the monitoring of (i) agricultural areas and related hydrological processes, (ii) urban areas, and (iii) coastal areas and continental waters. Then, the specifications on spatial resolution, revisit frequency, overpass time, and spectral configuration are justified. The strategy of the mission is based on the combination with a network of long-term experimental sites. It will also make possible observing some areas facing rapid climatic change. The choice of the orbit is presented. Finally, we give rapid overviews of both the instrumental concept and the proposed mission architecture.
International Journal of Remote Sensing 05/2013; 34(9-10):3437. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Validation is mandatory to quantify the reliability of satellite biophysical products that are now routinely generated by a range of sensors. This paper presents the VALERI project dedicated to the validation of the products derived from medium resolution satellite sensors (www. avignon.inra.fr/valeri/). It describes the sites used, and the methodology developed to get the high spatial resolution map of the biophysical variables considered, i.e. LAI, fAPAR and fCover that can be estimated from ground level gap fraction measurements.
Sites were selected to represent , with the other validation projects, the large variation of biomes and conditions observed over the Earth’s surface. Each site is about 3×3 km² in size and should be flat and relatively homogeneous at the medium resolution scale. For each site, the methodology used to generate the high spatial resolution biophysical variable maps is described. It is mainly based on concurrent use of local ground measurements and a high spatial resolution satellite image, generally SPOT-HRV. Local ground measurements should be representative of an elementary sampling unit (ESU) that has approximately the same size as a SPOT-HRV multispectral pixel. The ground measurements mainly consist of gap fraction measurements achieved with LAI-2000 measurements or hemispherical photographs. The ESUs are selected over the whole 3×3 km² site in order to sample the vegetation types observed and to allow the derivation of variograms. A transfer function is subsequently established over the ESUs to relate the ground measurements of the biophysical variables with those of the high spatial resolution satellite image. Finally, co-kriging is applied to generate the high spatial resolution map of the biophysical variables over the 3×3 km² area.
The methodology presented in this paper can serve as a basis for validating medium resolution satellite products. Finally these methodological aspects are discussed and conclusions drawn on the limitations and prospects of before mentioned validation activity.
Remote Sensing of Environment 01/2013; · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper we present a procedure for the segmentation of high resolution image time series of cropland areas. We use a Land Parcel Information System which gives us the parcel boundaries, but some of the parcels need to be split in several fields since they contain several crops. The procedure is based in a template matching approach which uses single crop parcels in order to generate reference signatures for the different crop classes and a similarity metric to match every pixel of the mixed parcel to the corresponding crop reference signature.
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
[Show abstract][Hide abstract] ABSTRACT: The recent availability of high spatial and temporal resolution (HSTR) remote sensing data (Formosat-2, and future missions of Venμs and Sentinel-2) offers new opportunities for crop monitoring. In this context, we investigated the perspective offered by coupling a simple algorithm for yield estimate (SAFY) with the Formosat-2 data to estimate crop production over large areas. With a limited number of input parameters, the SAFY model enables the simulation of time series of green area index (GAI) and dry aboveground biomass (DAM). From 2006 to 2009, 95 Formosat-2 images (8 m, 1 day revisit) were acquired for a 24 × 24 km² area southwest of Toulouse, France. This study focused on two summer crops: irrigated maize (Zea mays) and sunflower (Helianthus annuus). Green area index (GAI) time series were deduced from Formosat-2 NDVI time series and were used to calibrate six major parameters of the SAFY model. Four of those parameters (partition-to-leaf and senescence function parameters) were calibrated per crop type based on the very dense 2006 Formosat-2 data set. The retrieved values of these parameters were consistent with the in situ observations and a literature review. Two of the major parameters of the SAFY model (emergence day and effective light-use efficiency) were calibrated per field relative to crop management practices. The estimated effective light-use efficiency values highlighted the distinction between the C4 (maize) and C3 (sunflower) plants, and were linked to the reduction of the photosynthesis rate due to water stress. The model was able to reproduce a large set of GAI temporal shapes, which were related to various phenological behaviours and to crop type. The biomass was well estimated (relative error of 28%), especially considering that biomass measurements were not used for the calibration. The grain yields were also simulated using harvest index coefficients and were compared with grain yield statistics from the French Agricultural Statistics for the department of Haute-Garonne. The inter-annual variation in the simulated grain yields of sunflower was consistent with the reported variation. For maize, significant discrepancies were observed with the reported statistics.
Remote Sensing of Environment 01/2012; 124:844-857. · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The generation of land-cover maps for agriculture is a recurrent problem in remote sensing. There exist many efficient algorithms, but they often need well selected images during specific periods, which delays the map availability to the end of the season. In this work, we propose to introduce prior knowledge about the crop rotation in order to both improve the classification and obtain an accurate map early in the year. We use a Bayesian Network to model the crop rotation and we introduce the output of the model into a Support Vector Machine classifier to generate a land-cover map. We evaluate the overall improvement and the effect on several crops.
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
[Show abstract][Hide abstract] ABSTRACT: Efficient unsupervised production of large-area land cover maps with the volumes of data to be generated by the forthcoming Earth observation missions is challenging in terms of computation costs and data variability. As a solution, introduction of non-spectral knowledge for data reduction and selection is proposed here. Analysis of intra-strata variability and inter-strata correlation for different stratified sampling approaches is presented, and valuable variables for both stratification and classification are identified.
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
[Show abstract][Hide abstract] ABSTRACT: A DART based data processing chain was developed for assessing the potential of a high spatial resolution geostationary satellite. It simulates time series of TOA and BOA radiance values and their spatial variability at any satellite spatial resolution, for any experimental, instrumental and view configuration (e.g., geosynchronous orbit). Account of sources of noise (e.g., atmosphere, sensor, anisotropy of surface optical properties,...) gives the radiance SNR (i.e., domain of validity). First results with the desert and tree savannah sites are very encouraging.
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
[Show abstract][Hide abstract] ABSTRACT: Analytical expressions of evaporative efficiency over bare soil (defined as the ratio of actual to potential soil evaporation) have been limited to soil layers with a fixed depth and/or to specific atmospheric conditions. To fill the gap, a new analytical model is developed for arbitrary soil thicknesses and varying boundary layer conditions. The soil evaporative efficiency is written [0.5 – 0.5 cos(πθL/ θmax)]^P with θL being the water content in the soil layer of thickness L, θmax the soil moisture at saturation and P a function of L and potential soil evaporation. This formulation predicts soil evaporative efficiency in both energy-driven and moisture-driven conditions, which correspond to P < 0.5 and P > 0.5 respectively. For P = 0.5, an equilibrium state is identified when retention forces in the soil compensate the evaporative demand above the soil surface. The approach is applied to in situ measurements of actual evaporation, potential evaporation and soil moisture at five different depths (5, 10, 30 and 60/100 cm) collected in summer at two sites in southwestern France. It is found that (i) soil evaporative efficiency cannot be considered as a function of soil moisture only, since it also depends on potential evaporation, (ii) retention forces in the soil increase in reaction to an increase of potential evaporation and (iii) the model is able to accurately predict soil evaporation process for soil layers with an arbitrary thickness up to 100 cm. This new model representation is expected to facilitate the coupling of land surface models with multi-sensor (multi-sensing-depth) remote sensing data.
[Show abstract][Hide abstract] ABSTRACT: In the coming years, several optical space-borne systems with high resolution, high temporal frequency revisit and constant viewing angles will be launched. The availability of these data opens the opportunity for the development of new applications which require to closely monitor the temporal trajectory of the characteristics of land surfaces. However, due to cloud cover and even to some rapid changes, a higher temporal resolution may be needed for some applications. One of the ways to improve the temporal resolution for these satellites is to merge their data with higher temporal resolution systems. For now, these other systems will fatally have a lower spatial resolution or a limited field of view. The goal of our work is to assess the usefulness of image fusion techniques for the joint use of Proba-V/Sentinel-3 data and Venus/Sentinel-2 images for land-cover monitoring. We are interested in the generation of land-cover maps and time profiles of surface reflectances with a spatial resolution of 10 to 30 m. with an update frequency of about 10 days.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a general framework for the simulation of remote sensing image time series with spatial, textural, spectral and temporal realistic characteristics. The main goal of this work is to be able to produce data which is representative of the kind of images which will be acquired by future space Earth observation missions as for instance VENμS, Sentinel2 or LDCM. This simulated data will be used for time series image analysis algorithm development and validation.
2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, Vancouver, BC, Canada, July 24-29, 2011; 01/2011
[Show abstract][Hide abstract] ABSTRACT: This work aims to investigate the capabilities of TERRASAR-X SAR (Synthetic Aperture Radar) and FORMOSAT-2 high resolution and multi-spectral data to estimate the surface soil moisture content of bare agricultural soils. Emphasis is put on the use of time series of TerraSAR Strip Map mode images acquired during the autumn 2009, between the 28 of September and the 11 of November, that matches with the period where agricultural fields are mainly bare (more than 40% of the area is composed of bare soils). The analysis is firstly performed by extracting the bare soil surfaces from FORMOSAT-2 NDVI data. Then, X-band backscattering coefficients acquired at high incidence angle (43° ) over bare soils are compared with field measurements collected over local sites located in the south-west of Toulouse (43° 29'36'N, 01° 14'14'E) in France. Field data consists of Surface Soil Moisture (SSM) and surface soil roughness respectively recorded by a Theta Probe sensor and a 2-meters long profilometer. Measurements are performed along several meters transects depending of the field size (at least several hectares). Changes in soil practices (plough, sown...) and spatial tillage orientation are also monitored in time. Results show the low sensitivity of the radar backscattering coefficient (43° ) to the tillage orientation. Signal variation lower than 0.5dB is observed when considering a relative angle view ranged between 0 and 90° (?satellite view angle - ?field tillage orientation). A well marked correlation between radar data and SSM measurements (r2 =0.75) is then observed whatever the soil practices, which is strongly important since C-band or L-band data do not allow significant SSM estimations without considering soil roughness corrections. Surface soil moisture is accurately estimated at local scale (rmse about 5%) thanks to the high spatial resolution images and to the small wavelength data (about 3 cm) less sensitive to soil roughness changes. Finally, soil moisture maps are processed, and well indicate the spatial variability of soil moisture over bare agricultural fields. Spatial analyses are performed by considering soil practices and soil properties. These results suggest that high resolution X-band images could be used to derive multi-temporal SSM maps over agricultural bare soils, by neglecting the tillage orientation contrary to analyses performed by C- and L-band data. The following work consists to 1) assimilate the high resolution soil moisture maps in physical models to improve evapotranspiration estimates at regional scale 2) combine X-band data with C- and L-band data in order to estimate soil moisture over vegetated fields.
[Show abstract][Hide abstract] ABSTRACT: 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.
Remote Sensing of Environment 01/2010; · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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 unique capabilities of the FORMOSAT-2 instrument to monitor the seasonality of wheat canopies. Topsoil moisture estimates are scattered at the timing of plant emergence and during plant senescence. Estimates are much more accurate from tillering to grain filling stages with an absolute error about 9% (0.09 m3 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).
Hydrology and Earth System Sciences 01/2010; · 3.59 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The aim of this study is to propose methods to improve crop and water management in Mediterranean regions. At landscape scale, there is a very large spatial variability of agricultural practices, particularly for grasslands irrigated by flooding. These grasslands are harvested three times per year and produce high quality hay, but their productions decreased significantly these last few years because of the water scarcity. It is therefore important to assess the real water requirement for crops in order to predict productions in the case of agricultural practice modifications. Until now, the spatial variability of agricultural practices was obtained through surveys from farmers, but this method was tedious to describe an entire region. Thus, the specific aim of the study is to propose a new approach based on: 1) the feasibility of using optical remote sensing data acquired at high spatio-temporal resolutions for agricultural practice monitoring and, 2) the evaluation of a crop model, forced with this data, for estimating water balance and crop yield. We developed a methodology based on the combined use of FORMOSAT-2 images and STICS crop model to estimate production, evapotranspiration and drainage of irrigated grasslands in "the Crau" region in the South Eastern France. Numerous surveys and ground measurements were performed during an experiment conducted in 2006. Simple algorithms were developed to retrieve the dynamic of Leaf Area Index (LAI) for each plot and the main agricultural practices such as mowing and irrigation dates. This information was then used to parameterize STICS, applied at region scale to estimate the spatial variability of water budget associated with the biomass productions. Results are displayed at the farm scale. Satisfactory results were obtained when compared to ground measurements. The method for extrapolation to other regions or crops is discussed as regard to data available.
Hydrology and Earth System Sciences Discussions. 01/2010;
[Show abstract][Hide abstract] ABSTRACT: Over lands, the cloud detection on remote sensing images is not an easy task, because of the frequent difficulty to distinguish clouds from the underlying landscape, even at a high resolution. Up to now, most high resolution images have been distributed without an associated cloud mask. This situation should change in the near future, thanks to two new satellite missions that will provide optical images combining 3 features: high spatial resolution, high revisit frequency and constant viewing angles. The VENµS (French and Israeli cooperation) mission should be launched in 2012 and the European SENTINEL-2 mission in 2013. Fortunately, two existing satellite missions, FORMOSAT-2 and LANDSAT, enable to simulate the future data of these sensors.Multi-temporal imagery at constant viewing angles provides a new way to discriminate clouded and unclouded pixels, using the relative stability of the earth surface reflectances compared to the quick variations of the reflectance of pixels affected by clouds. In this study, we have used time series of images from FORMOSAT-2 and LANDSAT to develop and test a Multi-Temporal Cloud Detection (MTCD) method. This algorithm combines a detection of a sudden increase of reflectance in the blue wavelength on a pixel by pixel basis, and a test of the linear correlation of pixel neighborhoods taken from couples of images acquired successively.MTCD cloud masks are compared with cloud cover assessments obtained from FORMOSAT-2 and LANDSAT data catalogs. The results show that the MTCD method provides a better discrimination of clouded and unclouded pixels than the usual methods based on thresholds applied to reflectances or reflectance ratios. This method will be used within VENµS level 2 processing and will be proposed for SENTINEL-2 level 2 processing.
Remote Sensing of Environment 01/2010; 114(8):1747-1755. · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: VENμS is an Earth observation demonstration mission developed in cooperation between FRANCE and ISRAEL. This dual mission gathers both a scientific mission (earth imaging) and a technological mission (Hall Effect Thruster). The main scientific goal of VENμS is to acquire data over land in order to improve the understanding and modeling of land surface and vegetation processes, and to develop new applications such as water balance, crop yield and carbon fluxes assessments. The uncommon features of the VENμS scientific are: 2 day revisit, high resolution (5.3m), spectral richness (12 bands), and constant viewing angles from the satellite at constant sun lighting angles. This unique combination will allow the development of new image processing methods. The satellite also flies a technological mission which aims at qualifying an electric propulsion technology and demonstrating its mission enhancement for low altitude missions and LEO orbits transfer.
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, July 25-30, 2010, Honolulu, Hawaii, USA, Proceedings; 01/2010