Ear density ( D e ) and ear surface area ( S e ) in cereals are important traits for adaptation to low inputs and climate change. Here we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot. First, the YOLOv5 ear detection algorithm is applied to nadir RGB images to estimate D e . Second, an ear segmentation algorithm is applied to nadir and 45° RGB images to compute the ear gap fraction at different viewing angles. The Beer-Lambert law is then inverted to compute the ear area index (EAI) from the observed ear gap fraction. S e is finally derived as the ratio between EAI and D e . We applied the methodology to a panel of 10 commercial bread wheat varieties to analyse how both traits vary across 12 environments. The relative error obtained for awnless varieties is 12% (56 ears m-2) for D e and 18% (1.3 cm2) for S e . For awned varieties, ground-truth observations of S e were shown to be biased due to an overestimation of awns contribution, leading to an error of 41% (3.6 cm2). S e was strongly correlated with grain dry mass per ear at harvest (r 2 = 0.80 across genotypes and environments, r 2 per genotype ranged between 0.80 and 0.95) and E A I was strongly correlated with grain yield (r 2 = 0.83). These results indicate that both EAI and S e can be interesting non-destructive proxies for yield and grain dry mass per ear.

Canopy radiative transfer models (RTMs) are essential tools for characterizing the complex interactions between solar radiation and vegetation canopies. Vegetation canopies exhibit pronounced vertical heterogeneity in terms of their biophysical and optical properties at different growth stages, significantly influencing canopy reflectance. Existing multilayer canopy RTMs predominantly use the 4-stream theory and the adding method to calculate multiple scattering. In these models, the eigenvector decomposition method is used to solve the differential equations and derive the layer scattering matrices, which are then used to calculate multiple scattering. However, for a vertically heterogeneous canopy, deriving analytical solutions to the layer scattering matrices is difficult since each canopy layer exhibits distinct scattering characteristics. The aim of this study is to develop a new multilayer canopy RTM, CANOP, based on the spectral invariant theory and the adding method. The new model employed spectral invariants, instead of intractable layer scattering matrices, to link the scattering properties at the leaf and canopy levels and derive the adding operators for multilayer canopy. The spectral invariants enable a realistic and anisotropic representation of the top and bottom reflectances, as well as the upward and downward transmittances of multilayer canopy. The CANOP model was compared with the multilayer 4-stream and discrete anisotropic radiative transfer (DART) models, and the field-measured paddy rice vertical profile data. The CANOP model performs better than the 4-stream model in simulating multilayer canopy reflectance. It also shows good agreement with the measured layered paddy rice data, achieving high R2 (0.99), low RMSE (0.038) and bias (0.019) values. CANOP provides an accurate and efficient approach for multilayer canopy spectral modeling and can be used for multilayer canopy reflectance modeling and inversion of vegetation biophysical and biochemical parameters.

This study explores the influence of in-field maize plant architectural parameters (leaf inclination, curvature, orientation) and sowing patterns (plant density from 6 to 12 plts m-2, row spacing from 0.4 to 0.8 m) on canopy light conditions. A new three-dimensional (3D) maize architectural model-CORNIBU, integrated with a canopy light regime computation model- was able to describe phenotypic space with a relatively low number of input parameters. The reliability of CORNIBU to describe the actual variability of daily fIPAR (fraction of Intercepted PAR) depending on the sowing pattern and plant architecture was evaluated by generating digital canopies of five actual maize hybrids from a field experiment. The predicted daily fIPAR from CORNIBU digital canopies and the field-measured fIPAR from hemispherical photographs on actual maize canopies exhibited a significant and positive correlation (R2 similar to 0.6), when calibrating the leaf phyllotaxy parameter from nadir gap fraction. Then, an in silico experiment conducted with CORNIBU permitted to identify the architectural ideotypes maximizing canopy light interception ( fIPAR ) and canopy light distribution (f ILA, the fraction of Illuminated Leaf Area). This analysis highlighted a trade-off between fIPAR and f ILA, therefore any architectural ideotype cannot maximize both variables. Deeper light distribution would be achieved with more erectophile leaves and leaves orientation following an almost distichous phyllotaxy, whereas greater light interception would be achieved with more pronounced planophile leaves and random leaf orientation. The incorporation of photosynthetic light-responsive curves to estimate canopy daily photosynthesis provided additional insights to understand the trade-off between fIPAR and f ILA. Our findings indicate that the form of the hyperbolic function, i.e of the light-response curve, determines the optimal balance between fIPAR, f ILA and the resulting architectural ideotypes. Plant architectures with a higher light interception-planophile leaves- maximize daily canopy photosynthesis when the light-response function is more linear, whereas a more asymptotic curve determines that ideotypes where incident light is more uniformly distributed through the foliage depth-erectophile leaves- are those that optimize daily canopy photonsynthesis. Finally, our analysis highlights that squared sowing patterns (plant spacing within rows is close to row distance) benefit canopy-level photosynthesis by decreasing mutual shading between plants within the same row, as compared to traditional rectangular patterns where row distance is 4 to 8 times higher than plant spacing.

Leaf chlorophyll content (LCC) is a crucial parameter reflecting vegetation's photosynthetic activity. Many LCC inversion algorithms based on satellite and unmanned aerial vehicle (UAV) data have been developed in recent decades. The one-dimensional radiative transfer model, like PROSAIL (1D model), has been a classic tool for LCC inversion. In recent years, three-dimensional radiative transfer models (3D model) have been developed rapidly. However, studies on 3D models for LCC inversion are limited, and their impact on inversion accuracy across different sensor resolutions remains unclear. This study focuses on winter wheat and integrates the DART, AdelWheat, and PROSPECT models to construct the 3D-model-derived look-up table (LUT). The 3D-model-based LUT and 1D-model-based LUT were applied to Sentinel-2 (S2) and UAV data to retrieve LCC. Validation results demonstrate that the 3D-model-based algorithm significantly improves LCC inversion accuracy for both S2 and UAV images. For UAV data, the root mean square error (RMSE) decreases from 9.90 mu g/cm2 to 7.97 mu g/cm2, and the coefficient of determination (R2) improves from 0.70 to 0.79. For S2 data, the RMSE decreases from 12.40 mu g/cm2 to 8.68 mu g/cm2, while R2 increases from 0.66 to 0.85. Additionally, overestimation at low LAI levels and underestimation at high LCC levels are effectively reduced. The high accuracy achieved under varying LAI and LCC conditions allows the 3D model to capture temporal trends throughout the growing season better. The 3Dmodel-based LCC inversion algorithm can better utilize the high spatial resolution advantages, thereby playing a significant role in vegetation physiological monitoring and crop phenotyping.

Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 ​%. However, the precision for stems with 54 ​% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 mu g/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R2 of 0.72, RMSE 0.41) and FCOVER (R2 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management.

Most Orchards throughout the Mediterranean basin rely heavily on irrigation, a dependency increasing due to climate changes. Assessing the water requirement (WR) is crucial and depends on different factors, including orchard age, tree density per field, inter-row management. This study proposes new methods to evaluate these characteristics with remote sensing (RS). Various remote sensors providing high and very high spatial resolution images are investigated and their accuracy is assessed. The final objective is to assess WR using variables derived from remote sensing compared to data provided by water managers and from the FAO method. A typical Mediterranean watershed was selected in South-Eastern France, with orchards having various agricultural practices. Original methods were developed with Sentinel 2 (S2) data (2016–2023), 1 Pleiades image (2022) and the extraction of Google-satellite-hybrid images (GSH, 2017), and assessed using a large ground observation dataset (information on water use collected on 366 fields). Five orchards were monitored by capacitive sensors to assess the water balance. Irrigation durations ranged from 3–300 hours/year, with decision influenced by tree density and plot age. To identify young orchards, a thresholding approach on S2 derived NDVI effectively identified young orchards achieving a 98% accuracy rate. Grassed and non-grassed orchards were mapped using two methods, with a random forest classification using three spectral bands with 72% accuracy and a supervised approach yielding 81% accuracy for GSH and 57% for Pleiades. The performance depends on the acquisition date of images. A pattern detection algorithm applied to GSH and Pleaides determined tree density, showing a high correlation (r²=0.9) with observed data. These RS derived variables allowed to compute orchard water requirements at the watershed scale, ranging from 70 to 550 mm annually depending on management practices. The proposed methods can be extrapolated to other territories and are implemented using open access softwares.

The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard. High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period. We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross, between IJ119, a local genitor, and Divona, both in summer and in winter, using several methods: fresh pruning wood weight, exposed leaf area calculated from digital images, leaf chlorophyll concentration, and LiDAR-derived apparent volumes. Using high-density genetic information obtained by the genotyping by sequencing technology (GBS), we detected 6 regions of the grapevine genome [quantitative trait loci (QTL)] associated with the variations of the traits in the progeny. The detection of statistically significant QTLs, as well as correlations (R2) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine. Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high, above 0.66, and stable between growing seasons. These variables provided genetic models explaining up to 47% of the phenotypic variance, which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements. Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.

Most orchards within the Mediterranean basin tend to be irrigated. Accurate knowledge of their water requirements is essential due to both increasing droughts and water restrictions. However, determining the water needs of fruit crops can be challenging, as they depend on factors like soil, climate, and temporal variations in leaf development. Remote sensing at high spatial and temporal resolution offers the possibility to provide the information required for crop monitoring. This study proposes several methods for detecting agricultural patterns and variables affecting water requirements in orchards, such as tree age, inter-row grassiness, tree density. Three methods to assess water volumes applied to orchards were compared, with one method using variables derived from remote sensing. Various high-resolution remote sensing images were used: -Sentinel 2 data (2016-2023), 1 Pleiades image (2022) and the extraction of Google-satellite-hybrid images (GSH,2017). The methods were evaluated from a large dataset of ground observations including the boundaries of plots and land-use (1430 orchards, among them we collected accurate information on 366 fields). Surveys on the agricultural practices were carried out among 22 farmers on 749 fields. Using these farm-specific surveys, it was observed that the studied orchards were irrigated for durations varying between 3-300 hours/year, with farmers basing their irrigation decisions on tree density and plot age. Thresholding on the NDVI Sentinel 2 in the summer period allowed the identification of young orchards with an accuracy of 98%. With Pleiades and GSH, the blue band was used and two thresholds were defined to separate pixels with trees, grass or bare-soil. Supervised classification was then employed to separate grassed and non-grassed plots using three spectral bands of Sentinel 2. Classifications performed from GSH images gave more accurate results (81% well classified) compared with Sentinel 2 (79%) and Pleiades (57%) when identifying grassed plots, although results can vary with acquisition date. A pattern detection algorithm based on a Marked Point Process was applied to the GSH and Pleaides images that allowed the number of trees to be determined, yielding an r²=0.9 against ground-based accounting. Derived variables improved accuracy in computing water requirements for surveyed orchards at the watershed scale.

The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy. Leaves orientation is an important architectural trait determining maize canopies light interception. Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition. The goal of the present study is 2-fold: firstly, to propose and validate an automatic algorithm (Automatic Leaf Azimuth Estimation from Midrib detection [ALAEM]) based on leaves midrib detection in vertical red green blue (RGB) images to describe leaves orientation at the canopy level; and secondly, to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities (6 and 12 plants.m −2 ) and 2 row spacing (0.4 and 0.8 m) over 2 different sites in southern France. The ALAEM algorithm was validated against in situ annotations of leaves orientation, showing a satisfactory agreement (root mean square [RMSE] error = 0.1, R 2 = 0.35) in the proportion of leaves oriented perpendicular to rows direction across sowing patterns, genotypes, and sites. The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition. In both experiments, a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1 (6 plants.m −2 , 0.4 m row spacing) towards 8 (12 plants.m −2 , 0.8 m row spacing). Significant differences among the 5 cultivars were found, with 2 hybrids exhibiting, systematically, a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity. Differences in leaves orientation were also found between experiments in a squared sowing pattern (6 plants.m −2 , 0.4 m row spacing), indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.

The study focused on Mediterranean orchards and aimed to explore different remote sensing data (Sentinel 2 data (2016–2023), 1 Pleiades image (2022) and the extraction of Google-satellite-hybrid images (GSH,2017)) to compute key variables affecting water requirements such as tree age and density per plot, leaf development, the inter-row management. Surveys were conducted on 22 farms where accurate information on agricultural practices was collected. The results have shown that a thresholding on the NDVI Sentinel 2 in the summer period allowed the identification of young orchards with an accuracy of 98%. The analysis of temporal profiles of FAPAR allowed the identification of key phenological stages such as flowering and fruit set. Supervised classification was employed to separate grassed and non-grassed plots using three spectral bands of Sentinel 2. Classifications performed from GSH images gave more accurate results (81% well classified) compared with Sentinel 2 (79%) and Pleiades (57%) when identifying grassed plots. The methods presented in this study propose methods easily accessible based on free-to-download data, making them applicable in diverse orchard contexts.

In the Mediterranean zone, the available water resources are subject to increasing tensions between users and call for a better assessment of the water consumption and use over territories, especially for agriculture. Sentinel missions now provide free remote sensing data with a high spatial and temporal resolution delivering regular information over large areas both on crop development and on the water status of various land surfaces. This study makes use of Sentinel data for two main purposes : -i) to evaluate the accuracy of new soil moisture products obtained from Sentinel 1 & 2 delivered from the THEIA platform by using soil moisture data from the monitoring of an agricultural plot located in Avignon (France) where measurement are available from several years with different sensor types; -ii) to assess the potentialities of Sentinel 1 and 2 for monitoring soil moisture/irrigation in irrigated cherry orchards in the Ouveze basin (France). Results show that THEIA derived soil moisture values are significantly correlated with in situ measurements (at 5cm depth in soil), but with variation of the relationship between years, not linked to variation in soil roughness, leading to dispersion when all years are pooled (r² ~0.25-0.36) and under-estimation of higher water contents (>0.3 m3/m3). A normalization of signal data with the yearly amplitude could improve this correlation (r²=0.36-0.55). In relation with aim (ii), temporal profiles of spectral indices obtained for several orchards with Sentinel 2 allowed to clearly identify the trees’ phenology and the impact of the inter-row management. First results also showed a medium but significant correlation (r²=0.36) between the VV polarization extracted from Sentinel 1 data at the highest incidence angle and soil moisture measured under irrigated cherries orchards. These soil measurements, combined with spectral data on a longer time interval, available for two main irrigation practices (drip and microsprinkler) will allow for a better understanding of the input of irrigation water and its use by trees at the plot scale, useful for future modeling approaches of the water balance in orchards in relation with irrigation.

The objective of this study is to evaluate the performances of a semi-empirical approach based on the Bayesian theory to retrieve Green Area Index (GAI) from multiple decametric satellites. It is designed to overcome some limitations in existing Radiative Transfer Model (RTM) inversion methods, including the high dimensionality of the inverse problem, the convergence problem due to possible equifinality, and the dependence of some RTM variables on the crop-specific architecture. The PROSAIL model is first inverted in a calibration step using the Hamiltonian Monte Carlo (HMC) algorithm over a global dataset of ground GAI measurements (for maize, wheat, and rice) and the corresponding reflectance observations from Landsat-8, Sentinel-2, and Quickbird to derive crop-specific distributions of PROSAIL input variables. These distributions were then used as prior information to predict GAI over an independent set of reflectance observations. Results show that the full Bayesian approach provides close estimates of GAI to ground truth, with respective Root Mean Square Error (RMSE) of 1.01, 1.33, and 0.97 for maize, wheat, and rice (R2=0.67, 0.76 and 0.63, respectively). The performances are better than those approaches generally reported using radiative transfer models that are non-crop-specific, like the SNAP algorithm for Sentinel-2, but are slightly behind the purely empirical models based on machine learning. However, the proposed approach provides an explicit insight of the joint distribution of PROSAIL variables that are valid for any satellite platform. This constitutes a major advantage against purely empirical models, as it enables to fully exploit large observational datasets from multiple sensors and generalize to other platforms.

Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentation of RGB images into three classes (background, green, and senescent vegetation). This is achieved in two steps: A U-net model is first trained on a very large dataset to separate whole vegetation from background. The green and senescent vegetation pixels are then separated using SVM, a shallow machine learning technique, trained over a selection of pixels extracted from images. The performances of the SegVeg approach is then compared to a 3-class U-net model trained using weak supervision over RGB images segmented with SegVeg as groundtruth masks. Results show that the SegVeg approach allows to segment accurately the three classes. However, some confusion is observed mainly between the background and senescent vegetation, particularly over the dark and bright regions of the images. The U-net model achieves similar performances, with slight degradation over the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent. Finally, the models are used to predict the fraction of three classes over whole images or regularly spaced grid-pixels. Results show that green fraction is very well estimated (R2 = 0.94) by the SegVeg model, while the senescent and background fractions show slightly degraded performances (R2 = 0.70 and 0.73, respectively) with a mean 95% confidence error interval of 2.7% and 2.1% for the senescent vegetation and background, versus 1% for green vegetation. We have made SegVeg publicly available as a ready-to-use script and model, along with the entire annotated grid-pixels dataset. We thus hope to render segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge or, at least, offering a pretrained model for more specific use.

Recurrent droughts and water restrictions occur more and more often in Southeastern France. In this context, the evaluation of available resources according to the crop development and needs is becoming a priority. This study uses Sentinel data, for two main purposes: -i) evaluating the accuracy of new soil moisture products obtained from Sentinel 1 & 2 (S2MP) delivered from the THEIA platform, by using continuous soil moisture measurements of an agricultural plot located in Avignon (France) -ii) assessing the potential of Sentinel 1 & 2 for monitoring soil moisture, irrigation and phenology of irrigated orchards. Significant correlations were found between in situ measurements and soil moisture products normalized with the yearly amplitude $(\mathrm{r}^{2} =0.76\ \ \text{rmse}=0.15\%/\%)$ . Time series of the green fractional cover fCOVER and the fraction of absorbed radiation (fAPAR) computed from Sentinel 2 permitted to identify full flowering and end of flowering phenological stages when irrigation start.