[Show abstract][Hide abstract] ABSTRACT: Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10-20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the "Sentinel-2 for Agriculture" project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented dataset, made of Sentinel-2 like time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the Joint Experiment for Crop Assessment and Monitoring network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future "Sentinel-2 for Agriculture" system.
[Show abstract][Hide abstract] ABSTRACT: Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic.
[Show abstract][Hide abstract] ABSTRACT: The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of the aerosol type. As a first step, this study compares the performances of two kinds of AOT estimation methods applied to FormoSat-2 and LandSat time series of images: a multi-spectral method that assumes a constant relationship between surface reflectance measurements and a multi-temporal method that assumes that the surface reflectances are stable with time. In a second step, these methods are combined to obtain more accurate and robust estimates. The estimated AOTs are compared to in situ measurements on several sites of the AERONET (Aerosol Robotic Network). The methods, based on either spectral or temporal criteria, provide accuracies better than 0.07 in most cases, but show degraded accuracies in some special cases, such as the absence of vegetation for the spectral method or a very quick variation of landscape for the temporal method. The combination of both methods in a new spectro-temporal method increases the robustness of the results in all cases.
[Show abstract][Hide abstract] ABSTRACT: Satellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and
Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR)
waveform and photon counting signals.
[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.
[Show abstract][Hide abstract] ABSTRACT: This work presents a methodology for the fast exploitation of the large volumes of high temporal and spectral resolution data that will be available with the future Earth Observation missions. A new approach integrating temperature and phenological information for the characterisation of land cover classes is given, as part of a fully automatic system for the generation of large area land cover maps. No selection of cloud-free dates, masking of unsuitable regions, or user interaction is needed. Analysis of its performance is undertaken, and future directions are identified.
[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 crop rotation and topography 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.
[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.
Full-text · Article · May 2013 · International Journal of Remote Sensing
[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.
Full-text · Article · Jan 2013 · Remote Sensing of Environment
[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.
[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.
[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.
[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.
[Show abstract][Hide abstract] ABSTRACT: For the last 10 years, CESBIO has been building a Regional Spatial Observatory (OSR), dedicated to coupled terrain and space observations in the southwest of France. The OSR relies on monthly acquisitions of optical images with a 10 meter resolution started in 2002 and on heavily instrumented sites layed out since 2004 (permanent measurements of water and carbon fluxes). This system was labelled "observation service" by CNRS/INSU in 2007, and became the ONES Kalideos OSR MiPy site by the end of 2009. The core site has the extent of a Spot image (50 * 50 km) and spans over very diverse landscapes, with large variations in pedology and topography and diverse land covers, land uses, agricultural practices and climates (with a strong gradient in summer water stress). Regarding remote sensing, this site has been used for the preparation of SMOS and now mainly contributes to support the launch of VENμS and Sentinel-2 satellite missions. Radar and thermal image use is growing and combined approaches are being developed. The research focuses on image quality enhancement, quantitative measurements and signal processing. Regarding thematic research, CESBIO focuses on the monitoring and modelling of major crops (cereals and oilseeds). The involvement of new scientific partners and local managers prompted the development of additional studies such as biodiversity, land planning and urban extension monitoring, environmental risks, forest health, grassland diversity and productivity. CESBIO and its partners are also starting new studies to make the most of a 10 year archive of data: this archive seems quite relevant to study the impact of the atypical climatic events observed in 2003 and 2011. Finally, the extrapolation of achieved results to the whole region Midi-Pyrénées or to the whole Pyrenees mountains has also started.
No preview · Article · Jan 2012 · Revue Francaise de Photogrammetrie et de Teledetection
[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.