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Decision support systems (DSS) are needed to carry out precision irrigation. Key issues in this regard include how to deal with spatial variability and the adoption of deficit irrigation strategies at the field scale. A software application originally designed for water balance-based automated irrigation scheduling locally fine-tuned through the use of sensors has been further developed with the emerging paradigm of both digital twins and the Internet of Things (IoT). The aim of this research is to demonstrate the feasibility of automatically scheduling the irrigation of a commercial vineyard when adopting regulated deficit irrigation (RDI) strategies and assimilating in near real time the fraction of absorbed photosynthetically active radiation (fAPAR) obtained from Sentinel-2 imagery. In addition, simulations of crop evapotranspiration obtained by the digital twin were compared with remote sensing estimates using surface energy balance models and Copernicus-based inputs. Results showed that regression between instantaneous fAPAR and in situ measurements of the fraction of intercepted photosynthetically active radiation (fIPAR) had a coefficient of determination (R2) ranging from 0.61 to 0.91, and a root mean square deviation (RMSD) of 0.10. The conversion of fAPAR to a daily time step was dependent on row orientation. A site-specific automated irrigation scheduling was successfully adopted and an adaptive response allowed spontaneous adjustments in order to stress vines to a certain level at specific growing stages. Simulations of the soil water balance components performed well. The regression between digital twin simulations and remote sensing-estimated actual (two-source energy balance Priestley–Taylor modeling approach, TSEB-PTS2+S3) and potential (Penman–Monteith approach) evapotranspiration showed RMSD values of 0.98 mm/day and 1.14 mm/day, respectively.

Accurate irrigation water management is crucial for maximizing crop yield and minimizing water waste. Remote sensing technology offers a promising solution for efficiently estimating irrigation water use at the field scale.In this study, we adapted the PrISM (Precipitation inferred from Soil Moisture) methodology to detect and estimate irrigation events from soil moisture remotely sensed data. PrISM is a well-known approach to correct precipitation estimates using soil moisture data. Its main application is to provide a near real-time corrected precipitation product. PrISM employs an antecedent precipitation index (API) formula coupled with a particle filter assimilation scheme for soil moisture.In this study, we adapted the PrISM methodology to estimate irrigation amounts from soil moisture. The methodology uses initial precipitation estimates and soil moisture profile to detect whenever water excess is present in the soil (not caused by precipitation) and estimates its amount, together with its uncertainty. The methodology does not need extensive calibration and it is adaptable to different spatial and temporal scales. A synthetic study was performed to investigate the effect of a degraded soil moisture signal in terms of temporal resolution (lowering the temporal sampling of the soil moisture time-series), spatial resolution (lowering the percentage of irrigated area in a pixel), and random noise (increasing RMSE values). Results from this study suggested that high spatial resolution is critical in order to avoid underestimation of irrigation amounts. Ideally, a field-level soil moisture (with more than 75% of the pixel irrigated) and a product with low RMSE (0.02 m3/m3) is required for precise estimations (in order to keep the error of annual cumulative irrigation below 20%). Temporal resolution has a lower impact, especially when an assumption on the frequency of irrigation events (deduced from the system of irrigation used at the field-level) is included in the algorithm.Consequently, the developed algorithm was applied to actual satellite soil moisture products at different spatial scales over the same area. Validation was performed using in situ data at the district level of Algerri-Balaguer from the study area in Catalunya, Spain, where ground-based irrigation amounts were available for various years. Additional validation was performed at the field-level at the Segarra-Garrigues irrigation district using in-situ data from a few fields where soil moisture profiles and irrigation amounts were continuously monitored. Our results suggest that PrISM can be used effectively to estimate irrigation from soil moisture remote sensing data and that this methodology could be potentially applied on a large scale, with the only limitation being the quality and spatial resolution of the satellite soil moisture product.

The almond production has increased by doubling their hectares under irrigation treatments in Spain. In a context of water scarcity, the estimation of Evapotranspiration (ET) and its components, Transpiration (T) and Evaporation (E), are key variables to monitor and manage the water resources. High-resolution ET can be retrieved from surface energy flux modeling, such as a Two-Source Energy Balance (TSEB) model, using an Unmanned Aerial System (sUAS). sUAS equipped with Thermal and Multispectral cameras allows us to obtain the main parameters required in TSEB. Currently, there are no studies that evaluate the T obtained with TSEB Priestley Taylor (TSEB-PT) and TSEB-2T models in tree-scale almonds under different irrigation treatments (IR) and production systems (PS). In this context, we evaluated the T retrieved with TSEB-PT and TSEB-2T models using Sap Flows sensor in trees with three PS, Open Vase with Minimal Pruning (OVMP), Central Axis (CA) and Hedgerow (HGR), and three levels IR, Full Irrigation (FI), Mild Stressed (MS) and Stressed (SS). Five flights were conducted from March 2021 to July 2021 to analyze the almond growing season with an aircraft equipped with a thermal and multispectral camera. Leaf area index (LAI), stem water potential (Ψstem) and Fractional Intercepted Photosynthetically Active Radiation (fIPAR) was also measured concomitant to image acquisition. PS presents significant differences in fractional canopy cover (F_C), tree height (H_C), LAI and Sap Flow transpiration (Tsf). The two TSEB models show a generalized overestimation with a BIAS of 0.99 and 1.22 for TSEB-2T and TSEB-PT respectively. TSEB-PT presented worse statistics and R2 decreases in the more intensive production system. HGR has equal or greater LAI but lower F_C, which would imply an overestimation of canopy temperature (T_C) by the PT method. This is in addition to the difficulty of setting the PT coefficient according to the context of the crop. The overestimation in both models could be associated with an error in Campbell (1998) Radiative Transfer Model used to estimate transmittance, which has an error of 0.14 RMSE and 0.12 BIAS compared with fIPAR. Our results suggest the use of TSEB-2T with high resolution images considering the current available technology that allows us to estimate T_C and T_S separately, especially in intensive or super-intensive almond crops. To improve the T estimation, it is recommended to use in situ PAR measurement to decrease the influence of LAI measurements on the models.

Irrigation is one of the main users of water worldwide and its overuse may affect the natural regimes of water systems. To avoid this, drainage and irrigation management needs to be improved. This study aims to determine the amount of water lost to drainage in a semi-arid Mediterranean irrigated area. Water use, rainfall and drainage were monitored for 12 months (2019–2020) in a 425 ha sub-basin in the Algerri-Balaguer irrigation district (8000 ha, NE Spain). In addition, irrigation requirements were estimated using the single-crop FAO-56 method and a two-source energy balance model (TSEB) was used to estimate actual evapotranspiration in the sub-basin. Water lost to drainage in the sub-basin was estimated as 18% of the total water that entered the perimeter as irrigation and rainfall, which are almost five time higher than theoretical requirements of leaching. Out of the total drainage water, 57% was estimated to be irrigation water and 43% rainwater. The average amount of irrigation water used was 614 mm and irrigation efficiency in the sub-basin was estimated at 80.2% and averaged actual evapotranspiration at 1144 mm. The available margin of improvement is between 19.3% of the present irrigation drainage ratio and the 3.8% estimated with the leaching requirement model. This research was performed under the PCI2020-112030 funded by Agencia Estatal de Investigación, Ministerio de Ciencia e Innovación: MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR and supported by the IDEWA project (ANR-19-P026-003).

Drainage is the water flowing under the roots when the soil's capacity to hold water is exceeded. It is an important term of the crop water budget, controlling soil salinization, potentially impacting downstream ecosystems, and being a strong indicator of water productivity. Estimating this flux at spatial scales relevant to farmers (plot-scale) and to watershed or irrigation district managers (sub-basin-scale) is thus fundamental for a sustainable management of water, soils, and downstream rivers. However, even though it can be measured or estimated locally with lysimeters or physically-based models, respectively, drainage is generally neglected or considered as a residual variable in crop water balance models. Furthermore, many studies have shown the relevance of using remote sensing data to spatialize crop water fluxes. The aim of this work is thus to estimate drainage from the plot-scale to the sub-basin-scale by using a crop water balance model coupled with remote sensing observations. For this, SAMIR, a crop water balance model based on the FAO-56 method using remote sensing data to constrain the modeled vegetation and the soil water status, is used. The approach is tested over the Algerri-Balaguer irrigation district in northeastern Spain where in-situ drainage measurements are available at an integrated (sub-basin) scale. Those data were obtained from a network of drains feeding four outlets where the drainage is continuously measured. Additionally, four lysimeters were installed on two experimental fields of the area. Firstly, the drainage simulated by SAMIR and by a physically-based model (HYDRUS-1D) are compared with the lysimeters measurements. Preliminary results indicate that both models are able to reproduce the drainage amount cumulated over the season given a site-specific calibration. Secondly, the field-scale SAMIR drainage estimates are aggregated over several sub-basins and compared against the drainage measurements at the outlet of the associated sub-basins. A strategy for calibrating SAMIR at the sub-basin-scale is proposed. The obtained results provide a first assessment of the potential of using a satellite-driven crop water balance to retrieve agricultural drainage at multiple spatial scales.

The overall objective of the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) project is to improve the understanding of land-atmosphere and hydrology interactions in a semi-arid region characterized by strong surface heterogeneity owing to contrasts between the natural landscape and intensive, irrigated agriculture. It is known that irrigation can potentially impact the local atmospheric boundary layer (ABL) characteristics, thereby modifying near surface atmospheric conditions within and downwind of irrigated areas (e.g., Lawston et al., 2020) and potentially the recycling of precipitation. The understanding of the impact of anthropization and its representation in models have been inhibited due to a lack of consistent and extensive observations. In recent years, land surface and atmospheric observation capabilities have advanced while irrigated surfaces have been increasing, leading to a renewed need for dedicated field campaigns over contrasting (climate) regions.We present a summary of the LIAISE field campaign intensive phase between July 15–29 2021, which took place over the Catalan counties of Urgell and Pla d'Urgell within the Ebro basin in north-eastern Spain. LIAISE is located in a semi-arid hot, dry Mediterranean climate, with a very sharp delineation between a vast, nearly continuous intensively-irrigated region and the generally much more dry rain-fed zone to the east of the study domain. Intensive, spatially distributed surface-based and airborne measurements of the atmospheric boundary layer were made, along with intensive eco-physiological observations and remotely-sensed high spatial resolution mesoscale measures of surface variables from aircraft and unmanned aerial vehicles (UAVs). Two energy budget sites were extended with a network of soil moisture sensors since they were used to evaluate remote sensing data from aircraft. More than 30 institutions took part in the campaign covering a wide range of expertise and spatiotemporal scales in the methods involved, be it through modelling or measurements.In this contribution we will elaborate on the spatial and temporal scales involved in the processes of evapotranspiration. We will make a strong case for integrated approaches combining observation and modelling techniques to further develop our understanding of evapotranspiration. This will be illustrated with a case study using a mixed layer column model heavily guided and constrained by measurements.

In precision irrigation, a wise approach for decision making would consider not only a response to the current observations by sensors or other means. It should also consider forecasting the near future and prospecting hypothetical scenarios for water requirements and potential yield. These would require simulations which, in turn, demand for site specific characterization of the Soil-Plant-Atmosphere scenario. While crop parameters can be retrieved relatively easy from remote sensing, the availability of precise soil data would be limiting the accuracy of the simulations. Such limitations could be alleviated by in-situ calibration of the soil-crop models where the simulated soil water budget is contrasted with observed series of crop’s vigor and water state. This contribution describes an example where the soil waterholding capacity was estimated from inverse modelling during the seasons of 2020 and 2021 in a vineyard near Lleida, Spain.

It is known that irrigation can impact the local atmospheric boundary layer characteristics, thereby modifying near surface atmospheric conditions within and downwind of irrigated areas and potentially the recycling of precipitation. The understanding of the impact of anthropization and its representation in models have been inhibited due to a lack of consistent and extensive observations, but in recent years, land surface and atmospheric observation capabilities have advanced. The overall objective of the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) project is to improve the understanding and prediction of land-atmosphere-hydrology interactions in a semi-arid region characterized by strong surface heterogeneity between the natural landscape and intensive agriculture. The study region is located over the Pla d’Urgell region within the Ebro basin in NE Spain. This area was selected since it is a breadbasket region: there are discussions underway to further expand this irrigated zone owing to its economic importance, but consensus of current climate projections predicts a significant warming and drying over this region in upcoming years. Thus there is an urgent need to improve the prediction of the potential changes to the regional water cycle since water resources are limited. Here we present an overview of the intense phase of the LIAISE observational campaign, which is part of the HYdrological cycles in the Mediterranean Experiment (HyMeX) phase 2, that took place in July, 2021 when land surface heterogeneity was at a maximum. A network of 7 stations provided continuous measurements of the surface energy and water budget components for multiple representative land cover types, including irrigated surfaces, along with detailed surface biophysical measurements from the leaf to field scale. Surface fluxes at the field scale were made using scintillometer configurations over 3 of the sites. Lower atmospheric measurements were obtained from tethered balloons, lidar, UHF profilers, frequent radio-sounding releases, UAVs and several aircraft. Finally, airborne instruments measured solar induced florescence, surface temperature over several spectral bands and soil moisture over a transect cutting across the rain-fed and irrigated areas. The main outcome of this project is to provide the underpinnings for improved models leading to better water resource impact studies for both the present and under future climate change.

The basal crop coefficient (Kcb) is the ratio of crop evapotranspiration that primarily corresponds to transpiration. A comparison of different approaches using remote sensing observations for the estimation of Kcb of grapevines is carried out. The study is done over a cv.Tempranillo vineyard located in Catalunya, Spain from March to July 2021.The different approaches tested are the following: 1- a linear relation between NDVI and Kcb(Campos et al., 2010), 2 and 3 - the two different approaches proposed in (Allen and Pereira, 2009), where Kcb is estimated thanks to a density factor. The first version lies on an exponential of Leaf Area Index (LAI), the second version lies on the tree height and the fraction of effective exposed area. 4 and 5- an intercepted photosynthetic radiation (fiPAR) model (Oyarzun et al., 2007) using inferred crop height and width is related to Kcb through the (Lebon et al., 2003) and (Picón-Toro et al., 2012) proposals. 6- The generic tabulated approach proposed by (Allen et al., 1998) is also used to compare to a reference, however it must be remembered that those tabulations are only indicative.The different approaches are compared to the actual Kcb retrieved from a flux tower and the reference evapotranspiration of a nearby weather station. The resulting Kcb are injected into a water budget and daily evapotranspirations are finally compared to actual measurements.The simple linear method did not transfer well on this particular vineyard. The "Allen& Pereira LAI" and the "Oyarzun/Picon" had the best performance with a respective r2, RMSE of 0.57, 0.60 and 0.54 0.62 mm.day-1 on evapotranspiration estimates. However, the former approach through LAI does not seem really operational. The later method only needs to be parameterized with some easy to retrieve descriptions of the plot and plantation. In any case, a drawback of these NDVI based methods is the possible appearance of adventices, or the presence of a inter-row crop. Those must be withdrawn from the NDVI signal of the grapevine.

This research aims at introducing a new methodology to create maps of irrigation types at very high resolution, with yearly updates. While different studies were already performed on simply mapping irrigated areas, there is still no research on classifying irrigation types based on remotely sensed data. This information has a critical scientific value since detailed information on irrigation greatly improves the understanding of human activities on the water cycle. In particular, precise knowledge of different irrigation types is needed in order to correctly model the anthropogenic impact in various land surface models (Ozdogan et al., 2010; Evans and Zaitchik, 2008). Additionally, these maps are also useful for administrative purposes, to estimate the percentage of different irrigation types, monitor changes in irrigation practices and consequently encourage more sustainable use of the freshwater resources. In this research, we produce maps of irrigation types combining state-of-the-art supervised AI classification algorithms for time series classification together with a selection of hydrological variables. In order to train and test the AI models, a field campaign to collect ground truth data was performed in November 2020 around the intensely cultivated region of Catalunya, Spain. From this campaign, important information about crop types and irrigation types (sprinkler, flood, drip/subsurface and non-irrigated) were retrieved for a large number of fields, ensuring to collect a representative sample of the different cultivation and irrigation types employed in the area. Three different models were tested using as inputs a large variety of hydrological variables both alone and combined in multivariate models. Two machine learning models, Time-Series Forest and Rocket, and one Deep Neural Network model, ResNET, were selected for this classification task. The classification was performed using time-series from three different years in order to train the models with a more general and robust dataset, independent from specific meteorological conditions of a single year. The main finding of the research was that Soil Moisture (SM) and Actual Evapotranspiration (ETa) at very high spatial resolution (20 m) consistently showed the highest accuracy, when combined together, with respect to the other variables considered, regardless of the AI model used. Additionally, ResNET showed consistently better performance than the other two AI models over all the metrics used for the comparison (accuracy, precision, recall and kappa). The final classification accuracy retrieved from ResNET using SM and ETa as inputs was 86.59 +/- 2.79, obtained from 10 different runs of the model trained each time with different ground truth data subsamples. As a result of these findings, yearly maps of irrigation types can be created for large areas at field level, delivering detailed information on the status and evolution of irrigation practices. REFERENCE:Ozdogan, M.; Rodell, M.; Beaudoing, H.K.; Toll, D.L. Simulating the effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data. J. Hydrometeorol 2010, 11, 171–184.Evans, J.P.; Zaitchik, B.F. Modeling the large-scale water balance impact of different irrigation systems. Water Res. 2008, 44, W08448.

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions. info:eu-repo/semantics/publishedVersion

While the agronomic and economic benefits of regulated deficit irrigation (RDI) strategies have long been established in red wine grape varieties, spatial variability in water requirements across a vineyard limits their practical application. This study aims to evaluate the performance of an integrated methodology—based on a vine water consumption model and remote sensing data—to optimize the precision irrigation (PI) of a 100-ha commercial vineyard during two consecutive growing seasons. In addition, a cost-benefit analysis (CBA) was conducted of the tested strategy. Using an NDVI generated map, a vineyard with 52 irrigation sectors and the varieties Tempranillo, Cabernet and Syrah was classified in three categories (Low, Medium and High). The proposed methodology allowed viticulturists to adopt a precise RDI strategy, and, despite differences in water requirement between irrigation sectors, pre-defined stem water potential thresholds were not exceeded. In both years, the difference between maximum and minimum water applied in the different irrigation sectors varied by as much as 25.6%. Annual transpiration simulations showed ranges of 240.1–340.8 mm for 2016 and 298.6–366.9 mm for 2017. According to the CBA, total savings of 7090.00 € (2016) and 9960.00 € (2017) were obtained in the 100-ha vineyard with the PI strategy compared to not PI. After factoring in PI technology and labor costs of 5090 €, the net benefit was 20.0 € ha−1 in 2016 and 48.7 € ha−1 in 2017. The water consumption model adopted here to optimize PI is shown to enhance vineyard profitability, water use efficiency and yield.

International audience; In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (Kcb), or potential crop water use. The soil water evaporation coefficient (Ke) was measured from microlysimeters. The water stress coefficient (Ks) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ψstem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum Kcb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an R2 ≥ 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm·day−1 for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions.; En Californie, l’eau est une préoccupation constante. À mesure que la concurrence pour les ressources en eau augmente, la croissance de la population augmente. Il existe un besoin imminent de disposer de méthodes fiables pour fournir des informations sur la variabilité temporelle et spatiale des besoins en eau des cultures. Cette étude porte sur l'estimation des coefficients d'évaporation et des cultures d'un verger d'amandiers et de pistaches situé dans la Central Valley, en Californie, pendant toute une saison de croissance, en combinant un modèle simple d'évapotranspiration des cultures avec des données de télédétection. Un ensemble de données de l'indice de végétation NDVI dérivé de Landsat-8 a été utilisé pour faciliter l'estimation du coefficient de culture de base (Kcb) ou de l'utilisation potentielle de l'eau par la culture. Le coefficient d'évaporation de l'humidité du sol (Ke) a été mesuré à partir de microlysimètres. Le coefficient de stress hydrique (Ks) a été calculé à l'aide de méthodes thermiques basées sur le couvert forestier et télédétectées en vol, à l'aide de régressions saisonnières entre l'indice de stress hydrique des cultures (CWSI) et le potentiel hydrique de la tige (stem). Ces régressions étaient statistiquement significatives pour les deux cultures, indiquant de nettes différences saisonnières dans les pistaches, mais pas dans les amandes. Dans les amandes, les valeurs maximales estimées de Kcb se situaient entre 1,05 et 0,90, tandis que pour les pistaches, elles se situaient entre 0,89 et 0,80. Le modèle indiquait une différence de transpiration de 97 mm au cours de la saison entre les deux cultures. L'évaporation du sol représentait en moyenne 16% et 13% de l'évapotranspiration réelle totale d'amandes et de pistaches, respectivement. La vérification de l'évapotranspiration quotidienne des cultures basée sur un modèle a été effectuée à l'aide des données de covariance de Foucault et de renouvellement de la surface recueillies dans les mêmes vergers, conduisant à un R2 ≥ 0,7 et des erreurs moyennes de racine carrée (RMSE) de 0,74 et 0,91 mm · jour-1 pour amande et pistache, respectivement. Il est conclu que la combinaison de modèles d'évapotranspiration de cultures et de données de télédétection est utile pour augmenter l'information d'irrigation de l'échelle de la plante au champ et peut donc être utilisée par les agriculteurs pour prendre des décisions quotidiennes de gestion de l'irrigation.

This study addressed the possibility of increasing consumer acceptance of nectarine fruit by reducing irrigation for a short period during the late phase of fruit growth. Deficit irrigation (DI) was applied for 15 days before harvest in 2011 and for nine days in 2012 to the early-maturing ‘Gardeta’ nectarine. Fruit diameter, pulp dry matter concentration, firmness, titratable acidity, soluble solids concentration, skin and pulp colour, several sensory traits and consumer acceptance of fruit were evaluated twice at harvest. In 2011 and 2012 mean midday stem water potential (SWP) during the experimental period was −1.25 MPa and −1.05 MPa in the DI treatment, respectively. Control trees had always midday SWP values higher than −0.85 MPa. Less yield and lower mean fruit weight at harvest was observed only in 2011. In 2011, DI increased pulp dry matter and soluble solids concentrations, and pulp and skin colour. In 2012, DI did not affect fruit composition. Sensory quality was relatively unaffected by DI in both experimental years but DI consistently increased consumer acceptance of fruit by 27% in 2011 and by 25% in 2012. Consumers apparently perceived modest changes in fruit chemical composition and more advanced maturity in response to water stress that was not reflected by fruit composition and sensory analysis. The consumer panels appeared to be more sensitive than sensory panels and analytical instruments used for fruit composition determination. The inclusion of consumer panels in future DI studies is recommended. Consumer acceptance increased with modest decreases in tree water status as indicated by SWP at the cost of significant reductions in fruit size. However, an improvement of consumer acceptance was possible with minimum impact on fruit size and yield when dealing with very mild levels of water stress.

The level of irrigation restriction to apply in a deficit irrigation (DI) programme for sustainable peach (‘Baby gold 6’) production was investigated. The experiment involved four irrigation treatments over five consecutive seasons (2007–2011). They were full irrigation (control), reducing irrigation by 20 % during the first half of stage III (DI-80 %), withholding irrigation until reaching a light stress level (DI-L) and withholding irrigation until reaching a moderate stress level (DI-M). The withholding of irrigation in both DI-L and DI-M was applied only during stage II and postharvest periods and was based on midday stem water potential thresholds (Ψ stem). For the DI-L treatment −1.5 MPa was used in both periods, and for DI-M −1.8 and −2.0 MPa were used during stage II and postharvest, respectively. Average Ψ stem values during DI periods were approximately −1.4 and −1.2 MPa for DI-M and DI-L, respectively. The pre-defined thresholds required to trigger irrigation were rarely reached. No significant differences between treatments were found in terms of yield in any experimental year. However, DI-M and to a lesser extent DI-L had lower final fruit fresh mass at harvest related to lower Ψ stem after three consecutive years of the experiment (during 2010 and 2011). Therefore, in terms of fruit size, DI was not sustainable. Rather than lowering Ψ stem thresholds, we recommend discontinuing DI after 3-year application.

Invasive species fundamentally change the bio-physical and ecological characteristics of the ecosystems they invade. Rapidly expanding invasive species may facilitate the spread of other invasive species, and successive invasion events may lead to novel species interactions that may push the system beyond its equilibrium state and change successional pathways. Knowing the direction of the invasion front may be useful to predict impacts of invasive species. Water primrose (Ludwigia spp.), one of the invasive floating macrophytes in the Sacramento-San Joaquin River Delta (hereafter, Delta), has increased in cover rapidly over the past three decades likely outcompeting native and non-native species, changing their functional relationships, with cascading effects in the macrophyte communities of the aquatic ecosystem. In this study, we analyze the directionality of water primrose invasion and assess which spaces it occupies, whether it has overcrowded or outcompeted other vegetation communities, and its implications for succession in the Delta. We used imaging spectroscopy data acquired in June of 2004, June of 2008, November of 2014, and October of 2016 for the 2500 km2 of the Delta to map the communities of submerged macrophytes, floating macrophytes, and emergent marsh. We found that water primrose cover increased fourfold in the Delta over the past 13 yr, changing significantly in the central Delta and Liberty Island region from 122 ha in 2004 to 471 ha in 2016. Water primrose expanded first by spreading over open water and submerged macrophytes and, when that habitat was exhausted, primrose invasion switched direction and encroached into emergent marsh. This bilateral expansion to both open water and the marsh is likely to change rates of succession and affect the restoration of the native Delta marshes. Understanding the mechanisms behind the expansion dynamics of this invasive will allow managers to counter its impact on newly established vulnerable marshes. info:eu-repo/semantics/publishedVersion

Background and Aims Mapping the spatial variability of vine water status within a vineyard is necessary for the efficient management of irrigation water. The objective of this study was to determine whether estimates of remotely sensed leaf water potential (Ψrem) could be employed as a precise tool for scheduling irrigation at the irrigation sector level throughout the season. Methods and Results Three irrigation treatments were applied in a 16-ha commercial vineyard to analyse the performance of the proposed methodology for monitoring regulated deficit irrigation strategies, and to evaluate the required frequency of the acquisition of thermal images for irrigation scheduling. An aircraft equipped with a thermal sensor flew over the vineyard throughout the season, and the averaged Ψrem of each irrigation sector was used as the irrigation trigger. The acquisition of about five or six Ψrem maps over the season is recommended. The starting date for acquiring thermal images depends on canopy vegetation size and on the difficulty of extracting pure vegetation pixels. The effect of acquiring thermal imagery on days after rainfall or with low vapour pressure deficits affected the estimation of Ψrem, and these constraints need to be considered for feasible irrigation purposes. Conclusions Remotely sensed leaf water potential was successfully used as an irrigation trigger to adopt regulated deficit irrigation strategies without any negative effect on yield and wine composition. Significance of the Study This study presented a promising and powerful method for scheduling irrigation throughout the season at vineyard level based on estimates of remotely sensed leaf water potential.

The seasonal characterization of spatial variability in water requirements across and within vineyards could assist the viticulturist to fine-tune irrigation management for quality optimization. Remotely sensed crop water stress index (CWSI) is related to crop water status, but it is not known how applicable it is to different grape varieties at different times of the season. This study focused on the determination of the lower and upper baselines for calculating CWSI for the Chardonnay, Pinot-noir, Syrah and Tempranillo varieties at different phenological stages. Baselines were determined based on canopy temperatures measured with infrared temperature sensors placed on top of well-watered grapevines in 2011. Results indicated that non-water-stressed baselines differed depending on variety and phenological stage. During 2011, an aircraft equipped with a thermal camera flew over the vineyards on six particular days throughout the season at 150 m altitude above ground level. At the same time, leaf water potential (ΨL) was measured for each variety. Variety and phenological stage affected the relationship between remotely sensed CWSI and ΨL, with phenology having greater influence on the observed measurements than on variety. For instance, the one-to-one relationship between estimated and measured ΨL had R2 of 0.634 and 0.729 for variety and phenology, respectively. The baselines and estimations of ΨL were validated in different vineyards of the same region and in a different season (2013) using the same methodology as in 2011. Data obtained in 2013 were in agreement with observations during 2011. It is concluded that the use of CWSI for assessing vineyard water status requires calibration to account for the effects, primarily of phenological stage, but also, of variety. Once calibrated, this can be successfully applied to other vineyards and seasons., This work received partial financial support from SUDOE for the European project Telerieg SOE1/P2/E082 and the Spanish Ministry of Science and Innovation (MINECO) for the project CONSOLIDER CSD2006-00067 and AGL2009-13105.

Thermal-infrared remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes the most recent modifications applied to the robust but relatively simple LST-based energy balance model, the Two-Source Energy Balance (TSEB), which solves for the soil/substrate and canopy temperatures that achieves a balance in the radiation and turbulent heat flux exchange with the lower atmosphere for the soil/substrate and vegetation elements. As a result, the TSEB modeling framework is applicable to a wide range in atmospheric and canopy cover conditions. This work illustrates the utility of high resolution LST data providing within-field variability in energy fluxes and evapotranspiration (ET), including modifications made in TSEB to be adapted for structurally complex crops, such as vineyards. Such high resolution spatial information is being used in precision farming applications to assess the impacts of within variability in soil texture, water availability and other stress factors on plant condition and productivity.

In the current scenario of worldwide limited water supplies, conserving water is a major concern in agricultural areas. Characterizing within-orchard spatial heterogeneity in water requirements would assist in improving irrigation water use efficiency and conserve water. The crop water stress index (CWSI) has been successfully used as a crop water status indicator in several fruit tree species. In this study, the CWSI was developed in three Prunus persica L. cultivars at different phenological stages of the 2012 to 2014 growing seasons, using canopy temperature measurements of well-watered trees. The CWSI was then remotely estimated using high-resolution thermal imagery acquired from an airborne platform and related to leaf water potential (ѰL) throughout the season. The feasibility of mapping within-orchard spatial variability of ѰL from thermal imagery was also explored. Results indicated that CWSI can be calculated using a common non-water-stressed baseline (NWSB), upper and lower limits for the entire growing season and for the three studied cultivars. Nevertheless, a phenological effect was detected in the CWSI vs. ѰL relationships. For a specific given CWSI value, ѰL was more negative as the crop developed. This different seasonal response followed the same trend for the three studied cultivars. The approach presented in this study demonstrated that CWSI is a feasible method to assess the spatial variability of tree water status in heterogeneous orchards, and to derive ѰL maps throughout a complete growing season. A sensitivity analysis of varying pixel size showed that a pixel size of 0.8 m or less was needed for precise ѰL mapping of peach and nectarine orchards with a tree crown area between 3.0 to 5.0 m2., We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)., The funding of this research came from the Spanish Ministry of Economy and Competitiveness, under Project INNPACTO IPT-2011-1786-060000. We are grateful to Codorniu winery and Sorigué, S.A. for their support in this study. We thank RS Aviation, and in particular Mr Robert Pedra for his contribution to the acquisition of images. Technicians from the Efficient Use of Water program in IRTA and Quantalab (IAS-CSIC) are also acknowledged for their technical support in the field campaigns.

Under EC Council Directive 2007/33/EC member states of the European Union are recommended to take soil samples ranging from 100 mL to >1500 mL to monitor populations of potato cyst nematodes [PCN] (Globodera spp.) in targeted fields. This study examines the effects of varying sample size on sample processing and cyst extraction using two widely used laboratory methods. Cyst recovery was stable using the Fenwick can from 100 mL up to the physical limits of the can. Recovery was significantly lower for low density 50 mL samples; however, this was partly due to a statistical artefact since higher numbers of cysts were lost from successively larger samples with constant cyst numbers (i.e. decreasing densities). The Schuiling centrifuge had functional limits of 500 mL. Outside this range, cyst recovery from low density samples was significantly reduced. Cyst recovery from samples of varying size, but with equal numbers of cysts was only constant above 100 mL. However, samples ranging from 200-500 mL were optimal for the Schuiling centrifuge. Cyst extraction efficiency was similar using both methods for samples ranging from 150 mL to 1500 mL, where larger samples were divided into

Characterizing the spatial variability in water status across vineyards is a prerequisite for precision irrigation. The crop water stress index (CWSI) indicator was used to map the spatial variability in water deficits across an 11-ha ‘Pinot noir’ vineyard. CWSI was determined based on canopy temperatures measured with infrared temperature sensors placed on top of well-watered and water-stressed grapevines in 2009 and 2010. CWSI was correlated with leaf water potential (ΨL) (R2 = 0.83). This correlation was also tested with results from high resolution airborne thermal imagery. An unmanned aerial vehicle equipped with a thermal camera was flown over the vineyard at 07:30, 09:30, and 12:30 h (solar time) on 31 July 2009. At about the same time, ΨL was measured in 184 grapevines. The image obtained at 07:30 was not useful because it was not possible to separate soil from canopy temperatures. Using the airborne data, the correlation between CWSI and ΨL had an R2 value of 0.46 at 09:30 h and of 0.71 at 12:30 h, suggesting that the latter was the more favorable time for obtaining thermal images that were linked with ΨL values. A sensitivity analysis of varying pixel size showed that a 0.3 m pixel was needed for precise CWSI mapping. The CWSI maps thus obtained by airborne thermal imagery were effective in assessing the spatial variability of water stress across the vineyard., This work received partial financial support from SUDOE for the European project Telerieg SOE1/P2/E082 and the Spanish Ministry of Science and Innovation (MCI) for the project CONSOLIDER CSD2006-00067 and AGL2009-13105.

Knowledge on crop water status at the orchard scale is necessary for the efficient management of irrigation water. Canopy temperature has long been recognized as a plant water status indicator, and crop water stress index (CWSI), which is obtained from canopy temperature could be used as a tool for remotely detecting plant water status from airborne high-resolution thermal imagery. In 2012, CWSI baselines for olive and peach trees were derived from infrared thermometer data. An aircraft equipped with a thermal sensor flew over two orchards acquiring high-resolution thermal images. At the same time, leaf water potential (ΨL) was measured in eighteen trees per specie. Relationship between the difference of canopy and air temperature (Tc-Ta) and ΨL had a R2 of 0.74 and 0.82, for peach and olive, respectively. CWSI ranged from zero to one and showed a significant correlation with ΨL. Maps of estimated ΨL (derived from CWSI - ΨL relationships) were able to detect the spatial variability of plant water status within the orchards, and may be a feasible tool for irrigation purposes., This work received partial financial support from the Spanish Ministry of Science and Innovation (MCI) for the project INNPACTO IPT-2011-1786-060000.

This paper introduces a modified version of the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) algorithm to disaggregate an SMAP surface soil moisture (SSM) product at a 20 m spatial resolution, through the use of sharpened Sentinel-3 land surface temperature (LST) data. Using sharpened LST as a high resolution proxy of SSM is a novel approach that needs to be validated and can be employed in a variety of applications that currently lack in a product with a similar high spatio-temporal resolution. The proposed high resolution SSM product was validated against available in situ data for two different fields, and it was also compared with two coarser DisPATCh products produced, disaggregating SMAP through the use of an LST at 1 km from Sentinel-3 and MODIS. From the correlation between in situ data and disaggregated SSM products, a general improvement was found in terms of Pearson’s correlation coefficient (R) for the proposed high resolution product with respect to the two products at 1 km. For the first field analyzed, R was equal to 0.47 when considering the 20 m product, an improvement compared to the 0.28 and 0.39 for the 1 km products. The improvement was especially noticeable during the summer season, in which it was only possible to successfully capture field-specific irrigation practices at the 20 m resolution. For the second field, R was 0.31 for the 20 m product, also an improvement compared to the 0.21 and 0.23 for the 1 km product. Additionally, the new product was able to depict SSM spatial variability at a sub-field scale and a validation analysis is also proposed at this scale. The main advantage of the proposed product is its very high spatio-temporal resolution, which opens up new opportunities to apply remotely sensed SSM data in disciplines that require fine spatial scales, such as agriculture and water management. info:eu-repo/semantics/publishedVersion

In viticulture, detailed spatial information about actual evapotranspiration (ETa) and vine water status within a vineyard may be of particular utility when applying site-specific, precision irrigation management. Over recent decades, extensive research has been carried out in the use of remote sensing energy balance models to estimate and monitor ETa at the field level. However, one of the major limitations remains the coarse spatial resolution in the thermal infrared (TIR) domain. In this context, the recent advent of the Sentinel missions of the European Space Agency (ESA) has greatly improved the possibility of monitoring crop parameters and estimating ETa at higher temporal and spatial resolutions. In order to bridge the gap between the coarse-resolution Sentinel-3 thermal and the fine-resolution Sentinel-2 shortwave data, sharpening techniques have been used to downscale the Sentinel-3 land surface temperature (LST) from 1 km to 20 m. However, the accurate estimates of high-resolution LST through sharpening techniques are still unclear, particularly when intended to be used for detecting crop water stress. The goal of this study was to assess the feasibility of the two-source energy balance model (TSEB) using sharpened LST images from Sentinel-2 and Sentinel-3 (TSEB-PTS2+3) to estimate the spatio-temporal variability of actual transpiration (T) and water stress in a vineyard. T and crop water stress index (CWSI) estimates were evaluated against a vine water consumption model and regressed with in situ stem water potential (Ystem). Two different TSEB approaches, using very high-resolution airborne thermal imagery, were also included in the analysis as benchmarks for TSEB-PTS2+3. One of them uses aggregated TIR data at the vine+inter-row level (TSEB-PTairb), while the other is based on a contextual method that directly, although separately, retrieves soil and canopy temperatures (TSEB-2T). The results obtained demonstrated that when comparing airborne Trad and sharpened S2+3 LST, the latter tend to be underestimated. This complicates the use of TSEB-PTS2+3 to detect crop water stress. TSEB-2T appeared to outperform all the other methods. This was shown by a higher R2 and slightly lower RMSD when compared with modelled T. In addition, regressions between T and CWSI-2T with Ystem also produced the highest R2.