FIEUZAL Remy

Climate change and the demographic growth of the world's population are leading the agricultural world to adapt to meet these two major challenges. While agricultural land, which represents nearly one third of the world's land area, contributes significantly to global greenhouse gas emissions, it also offers the possibility of implementing climate change mitigation levers. In this context, the aim of this thesis is to increase our knowledge of the functioning of agricultural surfaces, to provide tools for assessing the contribution of cultivated surfaces to climate change, and to quantify the biogeochemical (C storage) and biogeophysical (albedo effect) effects of climate change mitigation through the use of intermediate crops. To meet these objectives, two modelling approaches were developed during this work. The first part of this thesis focused on the development of a spatialized modeling approach, allowing to provide estimates of production (biomass and yields), CO2 and water fluxes, these variables being used to quantify the carbon and water balances for field crop plots. To this end, the SAFYE-CO2 agro-meteorological model assimilating satellite products of vegetation index at high spatial and temporal resolutions was developed and applied to different crops (wheat, corn and sunflower) and intercrop vegetation (spontaneous regrowth, weeds, intermediate crops). This approach was validated on a network of plots in southwestern France, using a large number of satellite images and validation data on the Regional Spatial Observatory area. In particular, it has allowed to accurately estimate wheat, sunflower and corn productions, as well as CO2 and water fluxes on wheat and sunflower crops. Vegetation, which can develop on the plots during intercropping periods, was also taken into account in order to improve the estimation of CO2 and water fluxes. In particular, this made it possible to quantify the impact of intermediate crops on the C balance components of plots allocated to field crops in the study area. The second part aimed at developing a model of intermediate crops introduction at the European scale, in order to estimate the radiative forcing induced by the modification of the surface albedo generated by this practice. Thanks to medium resolution albedo products (1/20°), developed by the CNRM (and in collaboration with this laboratory), this modelling approach allowed to provide estimates of the albedo effect relative to intermediate crops. Several introduction scenarios were simulated to account for the impact of certain factors, such as snow or rain. These scenarios were used to highlight the potential negative impact of soil darkening induced by intermediate crops on the radiative forcing of cultivated areas in the long term (via the enrichment of soil organic matter). Finally, as any change in agricultural practices induces biogeochemical and biogeophysical effects on climate, an analysis of these coupled effects was conducted using these two modelling approaches. We conclude that once intercropping is implemented, the soil should be permanently covered so that the soil darkening effect does not result in the loss of other climatic benefits generated by this agricultural practice.

This article presents a monitoring of land cover/use by satellite images over an 11-year period (2006-2016), over a study site located in southwestern France near Toulouse. Time series of optical data are acquired by Spot and Landsat, which deliver images in multispectral mode with high spatial resolution (10-30 m). The detection of the different types of land cover/use (crops, grasslands, water, urban and wood) is produced every year. It is based on national reference geographical data and a random forest algorithm. The classifications are characterized by a high level of performance, with an average kappa of 0.83 (OA=0.85). The performance by land cover/use type is related to their representativeness, dates and number of acquisitions, and the resolution of satellite images. The results allow analyzing the evolution of the three main crops (wheat, sunflower and corn).

This paper aims at comparing satellite-derived and in situ yearly variations of grassland production, over nine farms located in France. During three successive years (2016, 2017, 2018), a dense experimental device has allowed to characterize the variability of grassland production, through a regular survey conducted on more than 169 plots. Ranging from 4.1 to 11.2 t.ha -1 , the yearly production were derived from several tens of thousands of measurements, and combined to derive yearly variations of production. Such unique dataset was compared to a grassland production index, derived from the combined use of medium resolution satellite images and meteorological data. A high level of accuracy is observed between the in situ and the satellite-derived yearly variations of grassland production, with a R 2 of 0.76 and a maximum deviation of 16.3%. Finally, a focus on the maps obtained at the national level makes it possible to analyze the context of the 3 studied years.

Agricultural soils are the primary anthropogenic source of N2O emissions, one of the most important greenhouse gases, because of the use of nitrogen (N) fertilizers. The proposed method provides access to an inventory of potential N2O emissions (the term potential refers to possible but not yet actual) at a fine scale, with an annual update, without a heavy deployment linked to a collection of field measurements. The processing chain is applied to optical satellite images regularly acquired at a high spatial resolution during the 2006–2015 period, allowing a better spatial and temporal resolution of the estimates of potential N2O emissions from crops. The yearly potential N2O emissions inventory is estimated over a study site located in southwestern France, considering seven main seasonal crops (i.e., wheat, barley, rapeseed, corn, sunflower, sorghum and soybean). The first step of the study, that is the land use classification, is associated with accurate performances, with an overall accuracy superior to 0.81. Over the study area, the yearly potential budget of N2O emissions ranges from 97 to 113 tons, with an estimated relative error of less than 5.5%. Wheat, the main cultivated crop, is associated with the maximum cumulative emissions regardless of the considered year (with at least 48% of annual emissions), while maize, the third crop regarding to the allocated area (grown on less than 8% of the study site), has the second highest cumulative emissions. Finally, the analysis of a 10-year map of the potential N2O budget shows that the mainly observed crop rotation (i.e., alternating of wheat and sunflower) reaches potential emissions close to 16 kg N2O emitted per hectare, while the monoculture maize is associated with the maximum value (close to 28.9 kg per hectare).

Empirical, semi empirical or physical modeling approaches have been proposed to simulate the microwave signal that can be observed during periods of bare soil. The aim of this study is to evaluate the performance of a statistical approach (Random Forest algorithm) to estimate the backscattering coefficients in X-band. Regular satellite images arc acquired by TerraSAR-X, over an agricultural region located in southwestern France. The analyses take advantage of a ground dataset covering a wide range of soil conditions: smooth to rough surfaces (h(rms) 0.5-7.9 cm) monitored in dry to saturated conditions (2.4-35.3%). Once trained and validated on half the dataset, the statistical algorithms shows better performance than previous approaches based on semi empirical or physical models, with correlations above 0,83 and errors below 1.07 dB, The sensitivity of the statistical algorithm keeps consistent with others models, with a higher importance of top soil moisture than surface roughness or texture variables.

The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO2, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO2 fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model's output was compared to data acquired during two cropping seasons at the Aurade integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO2 flux (root mean square error (RMSE) = 0.97 gC center dot m(-2)center dot d(-1) and determination coefficient (R-2) = 0.83) and water flux (RMSE = 0.68 mm center dot d(-1) and R-2 = 0.79). The model's performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g center dot m(-2), respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets.

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Continuous N 2 O flux acquisition is crucial to enrich our knowledge of the complex mechanisms underlying the annual greenhouse gas budget and to refine their estimation. N 2 O flux measurement methodologies at high temporal resolution, i.e. micro-meteorology methodologies, are still scarce and may exacerbate the lack of important data, especially during the night if the required turbulent conditions are not met. The static and automated chamber methodologies also lead to numerous gaps in a time series due to low sampling frequency, hardware malfunctions, chambers removal during field operations or filtering of low-quality measurements. There is a strong need to define a generic and realistic N 2 O flux gap-filling methodology, especially since there is no consensus on the methodology to be used. In this study, we investigated the effect of using either the traditional linear interpolation methodology alone, either an Artificial Neural Networks (ANN) methodology alone or the combination of both on gap-filled daily N 2 O flux dynamics and annual budget. All three methodologies were tested on daily N 2 O flux time series measured with automated chambers over 5 years from 2012 to 2016 on a southwestern France crop site following a wheat-maize rotation. On average over the studied period, the results showed better statistical scores using the ANN methodology alone than using the linear interpolation methodology alone, with R² and RMSE of 0.84 and 12.4 gN ha −1 d −1 and of 0.68 and 17.4 gN ha −1 d −1 , respectively. However, whereas the use of ANN methodology reproduced well high measured N 2 O fluxes, it induced overestimation on low measured N 2 O fluxes where the use of the linear interpolation methodology was relevant. To overcome that issue and to take advantages of both methodologies we propose a new one which mixes both. On average, using the mixed methodology did not increase statistical scores compared to the ANN one, with a R² and a RMSE of 0.84 and 12.4 gN ha −1 d −1 respectively for both, but for periods with low measured N 2 O fluxes using the mixed methodology improved the statistical scores and the observed daily flux dynamic.

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The GLORIE Campaign was performed in June-July 2015 in order to investigate the sensitivity of airborne GNSS-R measurements to land parameters. In this paper we present the first results focusing on agricultural areas. For this purpose ground truth measurements of soil moisture, roughness, plant water content and plant height were measured over 20 agricultural plots of various crops (cereals, vegetables, bare soil). The correlation with GNSS reflectivity in LHCP polarization confirms noticeable sensitivity to soil moisture, and plant related parameters such as NDVI, vegetation water content and plant height.

The GLORIE Campaign was performed in June-July 2015 in order to investigate the sensitivity of airborne GNSS-R measurements to land parameters. In this paper we present the first results focusing on agricultural areas. For this purpose ground truth measurements of soil moisture, roughness, plant water content, leaf area index and plant height were measured over 20 agricultural plots of various crops (cereals, vegetables, bare soil). The correlation with GNSS reflectivity in LHCP polarization confirms noticeable sensitivity to soil moisture, and plant-related parameters especially vegetation cover height.

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Crop monitoring at a fine scale and crop yield estimation are critical from an environmental perspective because they provide essential information to combine increased food production and sustainable management of agricultural landscapes. The aim of this article is to estimate soybean yield using an agro-meteorological model controlled by optical and/or synthetic aperture radar (SAR) multipolarized satellite images. Satellite and ground data were collected over seven working farms. Optical and SAR images were acquired by Formosat-2, Spot-4, Spot-5, and Radarsat-2 satellites during the soybean vegetation cycle. A vegetation index (NDVI) was derived from the optical images, and backscattering coefficients and polarimetric indicators were computed from full quad-pol Radarsat-2 images. An angular normalization of SAR data was performed to minimize the incidence angle effects on SAR signals by using the complementarities provided by SAR and optical data. The best results are obtained when the model is controlled by both the leaf area index (LAI) derived from the optical vegetation index modified triangular vegetation index (MTVI2) or from the SAR backscattering coefficient {\sigma _{{^{\circ}}{textsc{vv}}}} ({text{LAI}}_{text{MTVI2}} or ( {text{LAI}}_{\sigma ^{\circ}{textsc{vv}}} ) and the dry biomass (DB) derived from the SAR Pauli matrix T33 ({text{DB}}_{{text{T}}33}) ({text{r}}^{2} gt 0.83) , demonstrating the complementary of optical and SAR data.

In the presence of Stéphane Le Foll, the European Agroforestry Seminar will take place from 23 to 25 May 2016. Organized by EURAF (European Agroforestry Association), it will bring together about 200 people from more than 20 nationalities. This meeting will be an opportunity to take stock of the research underway to develop agroforestry programs in many countries. Agroforestry, key point of the agro-ecological project. During this seminar, the Minister of Agriculture, Food and Forestry will present the agroforestry development plan, launched on December 17, 2015 at the national level. A key element in the agro-ecological project, this plan covers research, regulatory and financial aspects of agroforestry, training and advice, economic valuation of productions and international issues, and also includes an overseas component. This plan covers the period 2015-2020. It was developed with multiple partners of agroforestry in France (Ministry of the Environment, INRA, chambers of agriculture, associations, etc.). The Minister's participation in this European seminar is fully in line with the development project of agro-ecology in France, in which production systems combining trees and agriculture play an essential role. This is why international cooperation must be encouraged in order to achieve demonstrative results that will enable agroforestry to be better used in all forms of agriculture. EURAF, a major player in the development of agroforestry (European agroforestry association), aims to develop agroforestry at the European level in all its forms: intra-plot agroforestry, bocage, sylvo-pastoralism, meadow orchards, etc. It brings together members (individual members) from a wide variety of structures: administrative staff, members of associations, technical institutes, etc. Among the French members of EURAF, INRA, AFAC and AFAF will be present at this seminar.

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Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than 15 dB at all elevations above 45, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than 30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

Microclimate patterns in an agroforestry intercropped vineyard: First results . 3. European Agroforestry Conference (EURAF 2016).

This paper aims to evaluate the contribution of multitemporal polarimetric synthetic aperture radar (SAR) data for winter wheat and rapeseed crops parameters [height, leaf area index, and dry biomass (DB)] estimation, during their whole vegetation cycles in comparison to backscattering coefficients and optical data. Angular sensitivities and dynamics of polarimetric indicators were also analyzed following the growth stages of these two common crop types using, in total, 14 radar images (Radarsat-2), 16 optical images (Formosat-2, Spot-4/5), and numerous ground data. The results of this study show the importance of correcting the angular effect on SAR signals especially for copolarized signals and polarimetric indicators associated to single-bounce scattering mechanisms. The analysis of the temporal dynamic of polarimetric indicators has shown their high potential to detect crop growth changes. Moreover, this study shows the high interest of using SAR parameters (backscattering coefficients and polarimetric indicators) for crop parameters estimation during the whole vegetation cycle instead of optical vegetation index. They particularly revealed their high potential for rapeseed height and DB monitoring [i.e., Shannon entropy polarimetry (r2=0.70) and radar vegetation index (r2=0.80), respectively].

In the context of climate change, agricultural managers have the imperative to combine sufficient productivity with durability of the resources. Many studies have shown the interest of recent satellite missions as suitable tools for agricultural surveys. Nevertheless, they are not predictive methods. A system able to detect summer crops as early as possible is important in order to obtain valuable information for a better water management strategy. The detection of summer crops before the beginning of the irrigation period is therefore our objective. The study area is located near Toulouse (southwestern France), and is a region of mixed farming with a wide variety of irrigated and non-irrigated crops. Using the reference data for the years concerned, a set of fixed thresholds are applied to a vegetation index (the Normalized Difference Vegetation Index, NDVI) for each agricultural season of multi-spectral satellite optical imagery acquired at decametric spatial resolutions from 2006 to 2013. The performance (i.e., accuracy) is contrasted according to the agricultural practices, the development states of the different crops and the number of acquisition dates (one to three in the results presented here). The detection of summer crops reaches 64% to 88% with a single date, 80% to 88% with two dates and 90% to 99% with three dates. The robustness of this method is tested for several years (showing an impact of meteorological conditions on the actual choice of images), several sensors and several resolutions.

Regional analysis of water use efficiency (WUE) is a relevant method for diagnosing the performance of irrigation systems in water-limited environments. In this study, we investigated the potential of FORMOSAT-2 images to provide spatial estimates of WUE over irrigated wheat crops cultivated within the semi-arid Yaqui Valley, in the northwest of Mexico. FORMOSAT-2 provided us with a unique dataset of 36 images at a high resolution (8 m) encompassing the wheat growing season from November 2007 to May 2008. Time series of green leaf area index were derived from these satellite images and used to calibrate a simple crop/water balance model. The method was applied over an 8 × 8 km2 irrigated area on up to 530 wheat fields. It allowed us to accurately reproduce the time courses of Leaf Area Index and dry aboveground biomass, as well as evapotranspiration and soil moisture. In a second step, we analyzed the variations of WUE as the ratio of accumulated dry aboveground biomass to seasonal evapotranspiration. Despite the study area being rather small and homogeneous (soil, climate), we observed a large range in wheat biomass production, from 5 to 15 t·ha−1, which was primarily related to the timing of plant emergence. In contrast, the seasonal evapotranspiration only varied from 350 to 450 mm, with no evident link with sowing practices. A significant gain in crop water productivity was found for the fields sown the earliest (maximal WUE around 3.5 kg·m−3) compared to those sown the latest (minimal WUE around 1.5 kg·m−3). These results demonstrated the value of the FORMOSAT-2 images to provide spatial estimates of crop production and water consumption. The detailed information provided by such high space and time resolution imaging systems is highly valuable to identify agricultural practices that could enlarge crop water productivity.

This paper aims to compare the crop yield retrieval performances, obtained by assimilating the leaf area index derived from multi-temporal satellite signatures (i.e. reflectances and backscattering coefficients) into an agro-meteorological model. The study is based on the Multispectral Crop Monitoring experimental campaign, conducted in 2010 by the CESBIO laboratory. During the agricultural season of sunflower, regular satellite images were quasi-synchronously acquired by 6 sensors (Formosat-2, Spot-4/5, TerraSAR-X, Radarsat-2 and Alos), over a region located in the south west of France. Calibration and validation steps take advantage of the dense network of monitored fields. Among the wide range of the tested image configurations (multi-frequency and multi-polarization), promising results are offered by optical and co-polarized C-band (i.e. HH and VV) data for yield estimate, with correlation superior to 0.74.

The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 μm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R²) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² superior to 0.76 and Root mean square errors inferior to 8.3% m3·m−3 in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

Artificial sources emitting in the protected part of the L-band are contaminating the retrievals of the soil moisture and ocean salinity (SMOS) satellite launched by the European Space Agency (ESA) in November 2009. Detecting and pinpointing such sources is crucial for the improvement of SMOS science products as well as for the identification of the emitters. In this contribution, we present a method to obtain snapshot-wise information about sources of radio-frequency interference (RFI). The localization accuracy of this method is also assessed for observed RFI sources. We also show that RFI localizations constitute a useful data set for assessing the pointing performance of the satellite, and present how it is possible, using the results of this method, to identify and estimate two systematic errors in the geo-location of the satellite field of view. The potential causes and the approaches to mitigate both these errors are discussed.

This paper is concerned with the estimation of wheat and rapeseed crops parameters (height, leaf area index and dry biomass), during their whole vegetation cycle, using satellite time series both acquired in optical and microwave domains. Crop monitoring at a fine scale represents an important stake from an environmental point of view as it provides essential information to combine increase of production and sustainable management of agricultural landscapes. The aim of this paper is to compare the potential of optical and SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation. Satellite (Formosat-2, Spot-4/5 and Radarsat-2) and ground data were acquired during the MCM 10 experiment conducted by the CESBIO laboratory in 2010. A vegetation index was derived from the optical images: the NDVI and backscattering coefficients and polarimetric parameters were computed from Radarsat-2 images. Results of this study show the high interest of using SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation during the whole vegetation cycle instead of using optical vegetation index. Polarimetric parameters do not improve wheat parameters estimation (e.g. backscattering coefficient sigma-nought VV corresponds to the best parameter for wheat height estimation (r2 = 0.60)) but show their high potential for rapeseed height and dry biomass monitoring (i.e. Shannon Entropy polarimetry (SEp . r2 = 0.70) and Radar Vegetation Index (RVI . r2 = 0.80) respectively).

The aim of this article is to evaluate the potential of optical and multi-polarization SAR images for soybean yield estimation by their assimilations into a simple agro-meteorological model. Satellite and ground data were acquired over two sites during the MCM'10 experiment. Optical and radar images were provided by Formosat-2, Spot-4, Spot-5 and Radarsat-2 satellites during the whole vegetation cycle of soybean. Results show that the assimilation of optical or SAR offer similar performances for the estimation of crop parameters (i.e. LAI and dry biomass) and crop yield (rRMSE = 18% in the worst case). Concerning SAR data, results highlighted the interest of using backscattering coefficients acquired at VV polarization (rRMSE = 2%).

This paper aims to establish and to analyze the temporal reflectance signatures of sunflower according to optical and radar satellite images. The study is performed in the south west of France, and takes advantage of the MCM'10 experiment (Multispectral Crop Monitoring), conducted in 2010 by the CESBIO laboratory. Images are provided by 6 satellites sensors (Formosat-2, Spot-4 and -5, TerraSAR-X, Radarsat-2 and Alos). The proposed method consists in correcting the angular effect of radar signal, and in analyzing the different temporal signatures depending on the phenological cycle of the sunflower (at parcel and landscape scales). Results highlight the importance of the radar angular normalization and show the importance of multi-frequency approaches in the context of Sentinel-1, TerraSAR-X and Alos-2 missions. Among the wide range of tested radar signal combinations, the C- and L-bands appear more adapted to monitor sunflower, and further estimate its biophysical parameters.

This paper presents a technique developed for the retrieval of the orientation of crop rows, over anthropic lands dedicated to agriculture in order to further improve estimate of crop production and soil erosion management. Five crop types are considered: wheat, barley, rapeseed, sunflower, corn and hemp. The study is part of the multi-sensor crop-monitoring experiment, conducted in 2010 throughout the agricultural season (MCM'10) over an area located in southwestern France, near Toulouse. The proposed methodology is based on the use of satellite images acquired by Formosat-2, at high spatial resolution in panchromatic and multispectral modes (with spatial resolution of 2 and 8 m, respectively). Orientations are derived and evaluated for each image and for each plot, using directional spatial filters (45 and 135 ) and mathematical morphology algorithms. ''Single-date'' and ''multi-temporal'' approaches are considered. The single-date analyses confirm the good performances of the proposed method, but emphasize the limitation of the approach for estimating the crop row orientation over the whole landscape with only one date. The multi-date analyses allow (1) determining the most suitable agricultural period for the detection of the row orientations, and (2) extending the estimation to the entire footprint of the study area. For the winter crops (wheat, barley and rapeseed), best results are obtained with images acquired just after harvest, when surfaces are covered by stubbles or during the period of deep tillage (0.27 > R2 > 0.99 and 7.15 > RMSE > 43.02 ). For the summer crops (sunflower, corn and hemp), results are strongly crop and date dependents (0 > R2 > 0.96, 10.22 > RMSE > 80 ), with a well-marked impact of flowering, irrigation equipment and/or maximum crop development. Last, the extent of the method to the whole studied zone allows mapping 90% of the crop row orientations (more than 45,000 ha) with an error inferior to 40 , associated to a confidence index ranging from 1 to 5 for each agricultural plot.

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The work of this thesis is part of the South-West project, whose main objective is to contribute to the understanding and modeling of the functioning of continental surfaces at the landscape scale. This work aims to improve the monitoring and analysis capacities of highly anthropized surfaces: agrosystems. Both actors and spectators of climate change, these surfaces are also dedicated to food production. The issue is therefore to reconcile sustainability of resources and sufficient level of production, by identifying tools such as remote sensing useful for decision making at scales ranging from the plot to the territory. In this context, synthetic aperture radars (SAR) embedded in satellites have the double advantage of being sensitive to different parameters of continental surfaces (related to the soil or vegetation), and the ability to observe in cloudy conditions (unlike sensors operating in the visible). Since the 1990s, various studies based on images acquired with SAR technology have shown the interest of microwave data for monitoring continental surfaces. In recent years, the emergence of satellite missions in the X and L frequency bands has enriched the study possibilities previously limited to the C band. These sensor-satellite pairs now provide high spatial resolution products (up to one meter), with weekly revisit possibilities, necessary criteria for monitoring heterogeneous areas associated with strong temporal dynamics The work carried out in this thesis aims to establish the complementarity between radar (TerraSAR-X, Radarsat-2 and Alos, in the X, C and L spectral bands) and optical (Formosat-2, Spot-4/5) data acquired by satellites for monitoring agrosystems. The first is the implementation of an experimental campaign based on the acquisition of a set of data (satellite and field), necessary for the development of new approaches for landscape analysis. The monitored area, characterized by a strong anthropization, is located 50 km southwest of Toulouse. The satellite images include three radar time series (X, C and L bands), plus optical acquisitions (Formosat-2, Spot-4/5). With a total of a hundred images acquired in the microwave, the area common to the various scenes covers an area of 10×10 km ². At the same time, the field measurement protocols have made it possible to consider independently the two key elements of the surface: the soil and the crop. In addition to the meteorological stations installed within the framework of the site, qualitative and quantitative measurements were carried out in a synchronous way with the satellite acquisitions, on a total of 387 plots. Five crops are mainly studied: wheat, rapeseed, sunflower, maize and soybean. - The temporal signatures of each crop are then established at each satellite acquisition wavelength (optical and radar) through an original approach of angular normalization of radar signals (combination of radar and optical information). The results obtained during the phenological cycle of winter crops (wheat and rapeseed) and summer crops (maize, soybean and sunflower) clearly show the complementarity of the multi-sensor approaches, and the specificity of the radar signals (in relation to the polarization states and frequencies considered). Two biophysical parameters related to vegetation are finally estimated (LAI and height), the microwave data showing both a high sensitivity and good performances. - The electromagnetic modelling on bare soil has first allowed to evaluate different formalisms, namely: the models of Dubois and Oh (1992 and 2004) having as common characteristics a simplified description of the processes. They are confronted with a model based on physical foundations, the IEM model (Integral Equation Model). The application of the models in the different spectral bands (X, C and L), shows very heterogeneous results, the best performances being obtained in X band, with the model of Oh 1992. Subsequently, the improvement of the models takes advantage of the analysis of the residuals (with respect to the input variables), in order to reduce the observed dispersion. The models tested are optimized and validated using a residuals approach. A strong improvement is observed for most of the models. The results highlight the interest of multi-sensor data for the monitoring of surfaces dedicated to agriculture. In the near future, space missions such as Tandem-X, Sentinel-1/-2, Radarsat Constellation or Alos-2 should provide access to these data, and thus clarify the results obtained in this thesis.

The work of this thesis is part of the South-West project, whose main objective is to contribute to the understanding and modeling of the functioning of continental surfaces at the landscape scale. This work aims to improve the monitoring and analysis capacities of highly anthropized surfaces: agrosystems. Both actors and spectators of climate change, these surfaces are also dedicated to food production. In this context, synthetic aperture radar (SAR) embedded in satellites has the double advantage of being sensitive to different parameters of continental surfaces (in relation to the soil or vegetation), and the ability to observe in cloudy conditions (unlike sensors operating in visible light). Since the 1990s, various studies based on images acquired with SAR technology have shown the interest of microwave data for monitoring continental surfaces. In recent years, the emergence of satellite missions in the X and L frequency bands has enriched the study possibilities previously limited to the C band. These sensor-satellite pairs now provide high spatial resolution products (up to one meter), with weekly revisit possibilities, necessary criteria for monitoring heterogeneous areas associated with strong temporal dynamicsThe work carried out in this thesis aims to establish the complementarity between radar (TerraSAR-X, Radarsat-2 and Alos, in the X, C and L spectral bands) and optical (Formosat-2, Spot-4/5) data acquired by satellites for monitoring agrosystems. The first is the implementation of an experimental campaign based on the acquisition of a set of data (satellite and field), necessary for the development of new approaches for landscape analysis. The monitored area, characterized by a strong anthropization, is located 50 km southwest of Toulouse. The satellite images include three radar time series (X, C and L bands), plus optical acquisitions (Formosat-2, Spot-4/5). With a total of a hundred images acquired in the microwave, the area common to the various scenes covers an area of 10×10 km ². At the same time, the field measurement protocols have made it possible to consider independently the two key elements of the surface: the soil and the crop. In addition to the meteorological stations installed within the framework of the site, qualitative and quantitative measurements were carried out in a synchronous way with the satellite acquisitions, on a total of 387 plots. The temporal signatures of each crop were then established at each satellite acquisition wavelength (optical and radar) using an original approach of angular normalization of radar signals (combination of radar and optical information). The results obtained during the phenological cycle of winter crops (wheat and rapeseed) and summer crops (maize, soybean and sunflower) clearly show the complementarity of the multi-sensor approaches, and the specificity of the radar signals (in relation to the polarization states and frequencies considered). Two biophysical parameters related to the vegetation are finally estimated (LAI and height), the microwave data showing both an important sensitivity and good performances.1 The electromagnetic modelling on bare soil has first allowed to evaluate different formalisms, namely: the models of Dubois and Oh (1992 and 2004) having as common characteristics a simplified description of the processes. They are confronted with a model based on physical foundations, the IEM model (Integral Equation Model). The application of the models in the different spectral bands (X, C and L), shows very heterogeneous results, the best performances being obtained in X band, with the model of Oh 1992. Subsequently, the improvement of the models takes advantage of the analysis of the residuals (with respect to the input variables), in order to reduce the observed dispersion. The models tested are optimized and validated using a residuals approach. The results highlight the interest of multi-sensor data for the monitoring of surfaces dedicated to agriculture. In the near future, space missions such as Tandem-X, Sentinel-1/-2, Radarsat Constellation or Alos-2 should provide access to these data, and thus clarify the results obtained in this thesis.

SMOS (Soil Moisture and Ocean Salinity) data have now been available for over two years and, as part of the validation process, comparing this new dataset to already existing global datasets of soil moisture is possible. In this study, SMOS soil moisture product was evaluated globally by using the triple collocation method. This statistical method is based on the comparison of three datasets and produces global error maps by statistically inter-comparing their variations. Only the variable part of the errors are considered here, the bias errors are not treated by triple collocation. This method was applied to the following datasets: SMOS Level 2 product, two soil moisture products derived from AMSR-E (Advanced Microwave Scanning Radiometer)-LPRM (Land Parameter Retrieval Model) and NSIDC (National Snow and Ice Data Center), ASCAT (Advanced Scatterometer) and ECMWF (European Center for Medium range Weather Forecasting). The resulting errors are not absolute since they depend on the choice of the datasets. However this study showed that the spatial structure of the SMOS was independent of the combination and pointed out the same areas where SMOS performed well and where it did not. This global SMOS error map was then linked to other global parameters such as soil texture, RFI (Radio Frequency Interference) occurrence probabilities and land cover in order to identify their influences in the SMOS error. Globally the presence of forest in the field of view of the radiometer seemed to have the greatest influence on SMOS error (56.8%) whereas RFI represented 1.7% according to the analysis of variance from a multiple linear regression model. These percentages were not identical for all the continents and some discrepancies in the proportion of the influence were highlighted: soil texture was the main influence over Europe whereas RFI had the largest influence over Asia.