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Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.

International audience; This paper addresses the pixelwise classification problem based on temporal profiles, which are encoded in two-dimensional representations based on Recurrence Plots, Gramian Angular/Difference Fields, and Markov Transition Field. We propose a multi-representational fusion scheme that exploits the complementary view provided by those time series representations , and different data-driven feature extractors and classifiers. We validate our ensemble scheme in the problem related to the classification of eucalyptus plantations in remote sensing images. Achieved results demonstrate that our proposal overcomes recently proposed baselines, and now represents the new state-of-the-art classification solution for the target dataset.

International audience; In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

The development of a sustainable wood energy chain is an essential part of ecological and energy transition in Reunion Island (Indian Ocean), where Acacia mearnsii is the main potential wood energy resource identified to date. In order to assess future wood biomass supply chain strategies, a major first issue is to gain knowledge of the spatial distribution of this species forest stands.In this study, we assessed the potential of very high spatial resolution multispectral imagery for mapping the main forest stands in a study area located the Western Highlands region, where Acacia mearnsii expands alongside Acacia heterophylla, an endemic forest species and Cryptomeria japonica, an exotic forest stand. A reference database including 150 samples of seven classes (Acacia mearnsii (mature and non-mature), Acacia heterophylla (mature and non-mature), Cryptomeria japonica, ‘herbaceous areas’, and ‘bare soils’) was used to classify a Pleiades image acquired in May 2020. Spectral and textural indices were used in an incremental classification procedure using a random classifier.The best results (Kappa = 0.84, global accuracy = 84%) were obtained for the classification using all spectral and textural bands. The resulting map enables analyzing the spatial distribution of the different forest stands.

The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval-SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of (NCombining double low line12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.

Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r2 of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.

La dégradation des terres liée aux activités humaines est considérée comme la cause directe des problèmes de sécurité alimentaire et des migrations des populations, et constitue de ce fait un enjeu majeur à l'échelle mondiale. Le Mozambique, qui dispose encore d'une importante ressource forestière dominée par les formations forestières du Miombo, est particulièrement touché par ce phénomène, avec près d'un quart de ses terres qui seraient déjà dégradées. Ce pays, comme d'autres pays africains, s'est fixé des objectifs ambitieux avec la restauration d'1 million d'hectares de terres dégradées dans les dix prochaines années. Cependant, il existe très peu de données quantitatives et spatialisées sur l'état des terres et sur la capacité de résilience du Miombo. L'objectif de cette thèse est d'analyser les dynamiques, les caractéristiques et les déterminants de l'évolution des paysages forestiers afin d'alimenter les stratégies de restauration des terres dégradées au Mozambique. Cette étude s'est particulièrement intéressée à i) comprendre les facteurs de dégradation des terres à l'échelle nationale, ii) évaluer la résilience de l'écosystème du Miombo dans les systèmes d'agriculture sur brûlis dans la région centrale du pays (Zambézie), et iii) proposer une approche pour identifier les zones potentielles de restauration des paysages forestiers à partir des connaissances acquises dans les deux premiers points. Dans un premier temps, les analyses de séries temporelles satellitaires (indices de végétation et variables climatiques) ont permis de documenter l'état des terres à l'échelle nationale et quantifier les facteurs de dégradation et régénération des terres. Les résultats montrent que 25 % du pays ont subi une baisse significative de la productivité de la végétation et qu'une grande partie de ces changements (61 %) est directement attribuée aux activités humaines telles que la dégradation des forêts et la déforestation, principalement liée à l'agriculture sur brûlis. Des inventaires floristiques, pédologiques et des enquêtes de terrain ont été menés en périphérie du Parc National de Gilé afin d'analyser les dynamiques de régénération forestière après défriche-brûlis et l'impact des cycles répétés de culture-jachère. Les résultats montrent que le Miombo a une grande capacité de régénération en termes de diversité d'espèces ligneuses et de propriétés du sol après une perturbation de faible intensité et de courte durée. La richesse spécifique, la diversité et les propriétés du sol se rétablissent après 20-25 ans d'abandon. Cependant, ces perturbations ont un effet à long terme sur la composition des espèces et la structure des peuplements. Il est également constaté un effet de l'intensification de l'agriculture lié aux pratiques d'agriculture sur brûlis qui modifient la composition spécifique, réduisent la diversité ligneuse et les stocks de carbone organique du sol. Ces connaissances issues des études par télédétection et d'inventaires de terrain ont été combinées dans une approche innovante permettant d'identifier les zones prioritaires de restauration et les options de gestion (restauration passive ou active), afin d'améliorer des fonctions du paysage (accumulation de carbone, connectivité des habitats) et renforcer la biodiversité. Cette approche s'appuie sur une analyse multicritère et le développement d'indicateurs spatialisés environnementaux et sur les trajectoires des agrosystèmes et à l'échelle régionale. L'approche a permis d'identifier une surface de 118 629 hectares, soit 11 % de la zone d'étude, à haut potentiel de restauration dont 64% (76 373 ha) nécessitant une restauration active. Cette thèse contribue à une meilleure compréhension des processus et des facteurs de dégradation de terres et de l'écologie du Miombo. Elle propose une approche innovante pour contribuer à l'élaboration de projets ou programmes de restauration des paysages forestiers au Mozambique.

International audience; Sugarcane plays an important role in food and energy production in Brazil and worldwide. The large availability of satellite sensors and advanced techniques for processing data have improved the forecasting sugarcane yield on a local and global scale, but more work is needed on exploiting the synergy between remote sensing, meteorological and agronomic data. In this study, we combined such data sources to forecast sugarcane yield using a random forest (RF) algorithm on an extensive area of 50,000 ha, over four years. Images from Landsat satellites were processed to time series of surface reflectance and spectral indices. The approach focused on the development of predictive models which only used data acquired and accessible several months before the harvest. First, three RF models were calibrated with different predictors to forecast the sugarcane yield at harvest: using Landsat satellite images and meteorological data (RF1); agronomic and meteorological data (RF2); a combination of Landsat satellite images, agronomic and meteorological data (RF3). As a comparison, we also tested the influence of including knowledge on the future harvest date in the models RF2 and RF3 (RF4 and RF5). The average values of R-2 for RF1, RF2, and RF3 were 0.66, 0.50 and 0.74, respectively. The model with the highest values of R-2 (RF3) had a Root Mean Square Error (RMSE) of 9.9 ton ha(-1) on yield forecast, approximately 15% of the yield average. Including the harvest date improved the RF2 and RF3 models to reach R-2 = 0.69 and RMSE = 10.8 ton ha(-1) for RF4, and R-2 = 0.76 and RMSE of 9.4 ton ha(-1) for RF5. A blind forecasting test for the 2016 yields showed similar prediction than the forecast made by in situ field expertise. This result has the potential to assist management of sugarcane production.

International audience; The idea that tropical forest and savanna are alternative states is crucial to how we manage these biomes and predict their future under global change. Large-scale empirical evidence for alternative stable states is limited however and comes mostly from the multimodal distribution of structural aspects of vegetation. These approaches have been criticized, as structure alone cannot separate out wetter savannas from drier forests for example, and there are also technical challenges to mapping vegetation structure in unbiased ways. Here, we develop an alternative approach to delimit the climatic envelope of the two biomes in Africa, using tree species lists gathered for a large number of forest and savanna sites distributed across the continent. Our analyses confirm extensive climatic overlap of forest and savanna, supporting the alternative stable states hypothesis for Africa, and this result is corroborated by paleo-ecological evidence. Further, we find the two biomes to have highly divergent tree species compositions and to represent alternative compositional states. This allowed us to classify tree species as forest vs savanna specialists, with some generalist species that span both biomes. In conjunction with geo-referenced herbarium records, we mapped the forest and savanna distributions across Africa and quantified their environmental limits, which are primarily related to precipitation and seasonality, with a secondary contribution of fire. These results are important for the ongoing efforts to restore African ecosystems, which depend on accurate biome maps to set appropriate targets for the restored states, but also provide novel empirical evidence for broad scale bistability. Significance statement: We develop a biogeographic approach to analyzing the presence of alternative stable states in tropical biomes. Whilst forest-savanna bistability has been widely hypothesized and modeled, empirical evidence has remained scarce and controversial, and here, applying our method to Africa, we provide large scale evidence that there are alternative states in tree species composition of tropical vegetation. Furthermore, our results have produced novel and more accurate maps of the forest and savanna distributions in Africa, which takes into account differences in tree species composition, and a complex suite of determinants. This result is not only important for understanding the biogeography of the continent, but also to guide large-scaled tree planting and restoration efforts planned for the region.

Vegetation indices (VI) describe vegetation structure and functioning but they are affected by illumination conditions (IC). Moreover, the fact that the effect of the IC on VI can be stronger than other biophysical or seasonal processes is under debate. Using Google Earth Engine and the latest Landsat Surface Reflectance level 1 data, we evaluated the temporal patterns of IC and two VI, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) in a mountainous tropical forest during the years 1984–2017. We evaluated IC and VI at different times, their relationship with the topography and the correlations between them. We show that IC is useful for understanding the patterns of variation between VI and IC at the pixel level using Landsat sensors. Our findings confirmed a strong correlation between EVI and IC and less between NDVI and IC. We found a significant increase in IC, EVI, and NDVI throughout time due to an improvement in the position of all Landsat sensors. Our results reinforce the need to consider IC to interpret VI over long periods using Landsat data in order to increase the precision of monitoring VI in irregular topography.

Despite the widely held assumption that trees negatively affect the local water budget in densely planted tree plantations, we still lack a clear understanding of the underlying processes by which canopy cover influences local soil water dynamics in more open, humid tropical ecosystems. In this study, we propose a new conceptual model that uses a combination of stable isotope and soil moisture measurements throughout the soil profile to assess potential mechanisms by which evaporation (of surface soil water and of canopy‐intercepted rainfall) affects the relationship between surface soil water isotopic enrichment (lc‐excess) and soil water content. Our conceptual model was derived from soil water data collected under deciduous and evergreen plants in a shade grown coffee agroforestry system in Costa Rica. Reduced soil moisture under shade trees during the “drier” season, coinciding when these trees were defoliated, was largely the result of increase soil water evaporation as indicated by the positive relationship between soil water content and lc‐excess of surface soil water. In contrast, the evergreen coffee shrubs had a higher leaf area index during the “drier” season, leading to enhanced rainfall interception and a negative relationship between lc‐excess and soil water content. During the wet season, there was no clear relationship between soil water content and between lc‐excess of surface soil water. Greater surface soil water under coffee during the dry season may, in part, explain greater preferential flow under coffee compared with under trees in conditions of low rainfall intensities. However, with increasing rainfall intensities during the wet season, there was no obvious difference in preferential flow between the two canopy covers. Results from this study indicate that our new conceptual model can be used to help disentangling the relative influence of canopy cover on local soil water isotopic composition and dynamics, yet also stresses the need for additional measurements to better resolve the underlying processes by which canopy structure influences local water dynamics.

International audience; Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km x 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNNapproach using S1 data.

This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approach calculates 7 vegetation indices that are combined in the form of multivariable regressions depending on the stage of rice growth: vegetative, reproductive or ripening. We model the relationship of these vegetation indices to estimate the biomass of a certain crop area. The methods are calibrated by using a minimum sampling area of 1 linear meter of the crop. Comprehensive experimental tests have been carried out over two different rice varieties under upland and lowland rice production systems. Results show that the proposed approach is able to estimate the biomass of large areas of the crop with an average correlation of 0.76 compared with the traditional manual destructive method. To our knowledge, this is the first work that uses a small sampling area of 1 linear meter to calibrate and validate NIR image-based estimations of biomass in rice crops.

International audience; Vegetation mapping of protected areas is a cornerstone of conservation worldwide. Established in 1928 and covering over 1.4 million hectares, Hwange National Park (HNP) is the largest natural reserve in Zimbabwe. In 1993, the sole comprehensive map of its vegetation to date was produced and since then it has been used in numerous research and conservation endeavours. Over the last two decades, however, the park's vegetation changed, safari areas and forest reserves were created at its edge and high-positional accuracy data on a suite of species were collected. To tend to contemporary mapping needs, in this article, we present the 2013-2014 vegetation structure map of HNP and its surroundings. It was produced by supervised classification of Landsat-8 Operational Land Imager (OLI) images, indices derived from these and the Landsat Tree Cover Continuous Field product. Its accuracy was assessed statistically using samples collected from high-resolution satellite imagery and basic ancillary field data. Of its total pixels, 83.2% were correctly classified. Mean omission and commission error were, respectively, 0.82 (0.74-0.90) and 0.82 (0.72-0.89), and this similarity held on a per class basis, indicating reliable area estimates. It was produced using only freely available imagery and software. Conservation implications: In addition to providing researchers and conservationists working within and around HNP with an updated vegetation map, aiming at an even broader audience, we provide a step-by-step approach for using modern freely available imagery and software for cost-effectively mapping HNP in future or other protected savannas across Africa.

International audience; Mapping winter vegetation quality is a challenging problem in remote sensing. This is due to cloud coverage in winter periods, leading to a more intensive use of radar rather than optical images. The aim of this letter is to provide a better understanding of the capabilities of Sentinel-1 radar images for winter vegetation quality mapping through the use of deep learning techniques. Analysis is carried out on a multitemporal Sentinel-1 data over an area around Charentes-Maritimes, France. This data set was processed in order to produce an intensity radar data stack from October 2016 to February 2017. Two deep recurrent neural network (RNN)-based classifiers were employed. Our work revealed that the results of the proposed RNN models clearly outperformed classical machine learning approaches (support vector machine and random forest).

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative, is to resort to weather-insensitive synthetic aperture radar (SAR) images. However, in many real-world applications, critical decisions are made based on some informative spectral features, such as water, vegetation or soil indices, which cannot be extracted from SAR images. In the absence of optical sources, these data must be estimated. The current practice is to perform linear interpolation between data available at temporally close time instants. In this work, we propose to estimate missing spectral features through data fusion and deep-learning. Several sources of information are taken into account - optical sequences, SAR sequences, DEM - so as to exploit both temporal and cross-sensor dependencies. Based on these data, and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May to November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baselines methods according to all performance indicators.

International audience; 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]

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 $({\text{DB}}_{{\text{T}}33}) ({\text{r}}^{2} > 0.83)$ , demonstrating the complementary of optical and SAR data.

Crop water requirements are commonly estimated with the FAO-56 methodology based upon a two-step approach: first a reference evapotranspiration (ET0) is calculated from weather variables with the Penman–Monteith equation, then ET0 is multiplied by a tabulated crop-specific coefficient (Kc) to determine the water requirement (ETc) of a given crop under standard conditions. This method has been challenged to the benefit of a one-step approach, where crop evapotranspiration is directly calculated from a Penman–Monteith equation, its surface resistance replacing the crop coefficient. Whereas the transformation of the two-step approach into a one-step approach has been well documented when a single crop coefficient (Kc) is used, the case of dual crop coefficients (Kcb for the crop and Ke for the soil) has not been treated yet. The present paper examines this specific case. Using a full two-layer model as a reference, it is shown that the FAO-56 dual crop coefficient approach can be translated into a one-step approach based upon a modified combination equation. This equation has the basic form of the Penman–Monteith equation but its surface resistance is calculated as the parallel sum of a foliage resistance (replacing Kcb) and a soil surface resistance (replacing Ke). We also show that the foliage resistance, which depends on leaf stomatal resistance and leaf area, can be inferred from the basal crop coefficient (Kcb) in a way similar to the Matt–Shuttleworth method.

Highlight This research successfully used image-based spectral indices acquired in the field to assess variability of response to drought in a tree mapping population and to detect the related genetic determinisms., Genetic studies of response to water deficit in adult trees are limited by low throughput of the usual phenotyping methods in the field. Here, we aimed at overcoming this bottleneck, applying a new methodology using airborne multispectral imagery and in planta measurements to compare a high number of individuals. An apple tree population, grafted on the same rootstock, was submitted to contrasting summer water regimes over two years. Aerial images acquired in visible, near- and thermal-infrared at three dates each year allowed calculation of vegetation and water stress indices. Tree vigour and fruit production were also assessed. Linear mixed models were built accounting for date and year effects on several variables and including the differential response of genotypes between control and drought conditions. Broad-sense heritability of most variables was high and 18 quantitative trait loci (QTLs) independent of the dates were detected on nine linkage groups of the consensus apple genetic map. For vegetation and stress indices, QTLs were related to the means, the intra-crown heterogeneity, and differences induced by water regimes. Most QTLs explained 15−20% of variance. Airborne multispectral imaging proved relevant to acquire simultaneous information on a whole tree population and to decipher genetic determinisms involved in response to water deficit.

European Integrated Project Ultra Low CO2 Steelmaking (ULCOS-contract) [515960]; CNPq [306561/2007]; EucFlux project; French Ministry of Foreign AffairsThis study was funded by the European Integrated Project Ultra Low CO2 Steelmaking (ULCOS-contract no. 515960), by CNPq 306561/2007, and by the EucFlux project. We thank International Paper of Brasil and in particular Jose Mario Ferreira, Augusto Miguel Nascimento Lima and Sebastiao Oliveira, for providing data and technical help for field campaigns and the French Ministry of Foreign Affairs for their financial support. We are also very grateful to Osvaldo Cabral (EMBRAPA), Marcos Ligo (EMBRAPA), Elcio Reis (IPBr), Cristiane Camargo Zani (ESALQ), Giampiero Bini Cano (Instituto Botanico), Renato Meulman Leite da Silva (ESALQ), and Reinaldo Camargo (ESALQ) for their help during field work.3; The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.

L'équipe Cemagref en charge des études GIS peuplier a choisi de s'impliquer dans la voie de la télédétection depuis 2010, il s'agit d'une méthode indirecte de qualification des couverts végétaux pour laquelle le Cemagref possède une expertise développée par les équipes de la maison de la télédétection au Cemagref de Montpellier. La présente étude s'intéresse à l'ensemble des données captées sur les 5 spectres lumineux et la mesure de température au niveau non seulement des parcelles unitaires mais également au niveau clonal. Les possibilités de traitement et d'exploitation des images captées en télédétection sont très étendues, l'objectif de notre démarche était d'évaluer le potentiel de cette technique pour quantifier et qualifier les atteintes de l'agent pathogène Marssonina brunnea sur différents clones de peuplier et de procéder in fine à un classement de ces derniers en fonction de leur réponse spectrale à l'infection. Nous avons choisi dans cette première étude de n'utiliser en priorité que des méthodes classiques et éprouvées avec les calculs d'index afin d'apprécier rapidement un premier niveau de potentiel de ce nouvel outil au service de l'évaluation de l'impact d'un agent pathogène foliaire des variétés de peuplier. Les premiers résultats acquis sont très positifs et encourageants, ils démontrent clairement le potentiel de la télédétection à très haute résolution pour l'examen détaillé de dispositifs d'une aire très inférieure à 1 hectare. Ce potentiel est aujourd'hui validé par la production d'images qui discriminent parfaitement les zones traitées et non traitées, les différentes zones homogènes de sol, les matériels testés et le clone inoculateur, ainsi que le comportement individuel de certains clones comme Fritzi-Pauley dont la signature télédétection est très spécifique. Cette étude démontre également que nous pouvons extraire des données numériques qui sont significativement corrélées avec une réalité biologique telle que les symptômes foliaires constatés par comptages d'acervules. Par contre les résultats de télédétection ne sont que faiblement corrélés avec la production de biomasse mesurée sur le terrain, toutefois on observe sur près de 50% des clones testés une bonne à très bonne cohérence entre les données issues de la télédétection, la note de symptômes foliaires et la production de biomasse. Nous pensons que le différentiel entre données télédétection et quantité de biomasse constaté sur 50% des clones a deux explications probables, d'une part ce dispositif est installé à très forte densité et les phénomènes de concurrence entre clones testés et clone inoculateur sont très importants ce qui a peut être provoqué, en combinaison avec des phénologies décalées, par exemple, un effet négatif du traitement fongicide sur certains clones concurrencés par le clone Magistère inoculateur. D'autre part, les données issues de la télédétection ne sont que qualitatives car prélevées sur une surface constante par échantillon alors que l'on voit très nettement sur les représentations graphiques des différents index de végétation de grandes différences de taille de houppiers qui pourraient être ainsi exploitées en approche quantitative seules ou en combinaison avec l'approche qualitative pour une mise en corrélation avec la biomasse mesurée.

International audience; We investigated space-use patterns of the West African savanna buffalo (Syncerus caffer brachyceros), a little-studied subspecies occurring at the northern limit of the African buffalo's geographical range. This buffalo generally ranges in small herds (about 45 individuals) and has a low body mass (approximately 400 kg) relative to the Cape buffalo (S. c. caffer). We monitored the movements of 7 breeding herds in W Regional Park (Burkina Faso, Benin, Niger) using global positioning system collars and activity data loggers. Habitat selection was analyzed at both large (interseasonal) and small (intraseasonal) scales in a context where resources are segregated spatially at some times of year. Both biotic (primary production and vegetation types) and abiotic (timing of rainfall and surface water) covariates, and the extent to which neighboring herds shared space, were considered. In the dry season buffalo herds ranged close (within 5.3 +/- 2.0 km, mean +/- SD) to segments of permanent rivers. At the onset of the monsoon all herds but 1 (which had year-round access to suitable resources) performed a large (35 +/- 10 km) directional movement in response to a large-scale gradient of primary production. Spatiotemporal dynamics of forage and water resources thus jointly stimulated interseasonal directional movements and shaped large (335 +/- 167 km(2)) annual home ranges. Furthermore, the establishment of home ranges in the wet season appears to be conditioned by a threshold (about 10%) in the availability of perennial grasses. Habitat-selection analysis at intraseasonal scale also underlines the key role played by perennial grasses for buffaloes. The spatial arrangements of home ranges of neighboring herds also suggest that interherd behavioral avoidance is a high-level constraint on foraging processes. The ability of the African buffalo to cope with contrasting environmental conditions throughout most sub-Saharan ecosystems highlights the high behavioral plasticity of this species.

National audience; Le Cemagref a investi plus lourdement en 2010/2011 sur l'analyse des données issues de télédétection. Dans un premier temps, le parti a été pris d'évaluer des méthodes d'analyses classiques et éprouvées afin d'apprécier rapidement un premier niveau de potentiel de ce nouvel outil au service de l'évaluation des variétés forestières. Les premiers résultats acquis sont très positifs et encourageants, ils permettent par exemple de pratiquer un relevé de la mortalité, de calculer, analyser et représenter des index de végétation aux niveaux dispositif ou placeau et même au niveau individuel si la dimension de l'arbre est suffisante. De plus, nos résultats mettent en évidence des corrélations assez robustes entre les données dendrométriques acquises au sol, des mesures phénotypiques et les valeurs d'index de végétation issues de la télédétection. Enfin, la télédétection se révèle être un outil très efficace de qualification objective des sites d'essais avec en particulier la mise en évidence de leurs hétérogénéités. Les premiers résultats obtenus sur trois des douze dispositifs pour lesquels nous disposons de photos aériennes démontrent clairement le potentiel de la télédétection à très haute résolution pour l'examen détaillé de dispositifs d'évaluation des Variétés Forestières Améliorées.La télédétection par voie aérienne, méthode à rapport qualité prix intéressant, pourrait être également utilisée comme premier niveau d'examen de dispositifs faiblement documentés. Les travaux à venir s'orienteront vers l'analyse d'autres dispositifs de typologies ou essences différentes ; nous procéderons également à l'évaluation de techniques d'analyse avancées comme la segmentation automatique pour recueillir des données et des représentations graphiques encore plus probantes.

Fine scale maps of vegetation biophysical variables are useful status indicators for monitoring and managing national parks and endangered habitats. Here, we assess in a comparative way four different retrieval methods for estimating leaf area index (LAI) in grassland: two radiative transfer model (RTM) inversion methods (one based on look-up-tables (LUT) and one based on predictive equations) and two statistical modelling methods (one partly, the other entirely based on in situ data). For prediction, spectral data were used that had been acquired over Majella National Park in Italy by the airborne hyperspectral HyMap instrument. To assess the performance of the four investigated models, the normalized root mean squared error (nRMSE) and coefficient of determination (R2) between estimates and in situ LAI measurements are reported (n = 41). Using a jackknife approach, we also quantified the accuracy and robustness of empirical models as a function of the size of the available calibration data set. The results of the study demonstrate that the LUT-based RTM inversion yields higher accuracies for LAI estimation (R2 = 0.91, nRMSE = 0.18) as compared to RTM inversions based on predictive equations (R2 = 0.79, nRMSE = 0.38). The two statistical methods yield accuracies similar to the LUT method. However, as expected, the accuracy and robustness of the statistical models decrease when the size of the calibration database is reduced to fewer samples. The results of this study are of interest for the remote sensing community developing improved inversion schemes for spaceborne hyperspectral sensors applicable to different vegetation types. The examples provided in this paper may also serve as illustrations for the drawbacks and advantages of physical and empirical models. (Resume d'auteur)