Using hyperspectral remote sensing to map grape quality in 'Tempranillo' vineyards affected by iron deficiency chlorosis

The objectives of this work wereto investigate the relationships between chlorophyll a+b concentration in leaves (Cab) and grape composition parameters in vineyards affected by iron chlorosis, andstudy whether the assessment of Cab from hyperspectral remote sensing imagery could be useful to map different potential quality zones in these vineyards.A field trial was conducted in a vineyard with the chlorosis susceptible cultivar. 'Tempranillo', over '110 Richter', located in Northern Spain. Three experimental treatments were applied: 0, 2, and 4 foliar sprayings with a ligninsulphonate derived product (10 % water soluble Fe) in a randomized design with 3 replications. The yield and grape composition parameters at harvest were measured for each base-plot (10x10 m in size), and related with chlorophyll concentration in leaves. On the other hand, a total of 24 'Tempranillo' commercial vineyards were identified for field and airborne data collection with CASI hyperspectral sensor, comprising 103 study areas of 10x10 m in size. A total of 1467 leaves were collected for determining pigment concentration and optical properties. Several narrow-band vegetation indices were calculated from leaf reflectance spectra. Results of trial showed that the lack of pigmentation in leaves was a major factor limiting grape ripening. Significant linear regressions between Cab and total soluble solids concentration and colour density of the must were detected. Estimation of Cab using the image-calculated TCARI/OSAVI through the PROSPECT-rowMCRM model simulation for all study zones, including the specific ligninsulphonate experiment, demonstrated the potential of hyperspectral imagery for mapping Cab in vineyards for chlorosis detection using remote sensing methods. Given the described relationship between Cab and quality parameters in vineyards affected by iron chlorosis, high-spatial resolution imagery with narrow bands might enable the segmentation in areas of potential quality in the framework of precision viticulture.

Estimating evaporation with thermal UAV data and two-source energy balance models

Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperature-difference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST

Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images

Quick and low cost delineation of site-specific management zones (SSMZ) would improve applications of precision agriculture. In this study, a new method for delineating SSMZ using object-oriented segmentation of airborne imagery was demonstrated. Three remote sensing domains—spectral, spatial, and temporal- are exploited to improve the SSMZ relationship to yield. Common vegetation indices (VI), and first and second derivatives ([Formula: see text], [Formula: see text]) from twelve airborne hyperspectral images of a cotton field for one season [Formula: see text] were used as input layers for object-oriented segmentation. The optimal combination of VI, SSMZ size and crop phenological stage were used as input variables for SSMZ delineation, determined by maximizing the correlation to segmented yield monitor maps. Combining narrow band vegetation indices and object-oriented segmentation provided higher correlation between VI and yield at SSMZ scale than that at pixel scale by reducing multi-resource data noise. VI performance varied during the cotton growing season, providing better SSMZ delineation at the beginning and middle of the season (days after planting (DAP) 66–143).The optimal scale determined for SSMZ delineation was approximately 240 polygons for the study field, but the method also provided flexibility enabling the setting of practical scales for a given field. For a defined scale, the optimal single phenological stage for the study field was near July 11 (DAP 87) early in the growing season. SSMZs determined from multispectral VIs at a single stage were also satisfactory; compared to hyperspectral indices, temporal resolution of multi-spectral data seems more important for SSMZ delineation

Utilización de modelos de reflectancia como nexo entre muestras foliares y la cobertura forestal: aplicación a datos hiperespectrales

[ES] El presente trabajo demuestra la utilización de modelos de simulación de la cobertura forestal mediante su aplicación a datos hiperespectrales del sensor aerotransportado CASI. Los modelos SAIL y Kuusk permiten ser utilizados como nexo de unión entre los niveles de hoja y de cobertura: las relaciones a nivel de hoja obtenidas entre índices ópticos y bioindicadores de estrés, como contenido clorofílico o fluorescencia clorofílica, pueden ser transformadas a un nivel superior de cobertura mediante la utilización de dichos modelos. Finalmente se realiza una demostración de la utilización de modelos de cobertura a través de los resultados obtenidos en el proyecto Bioindicators of Forest Sustainability, desarrollado en 12 zonas de Acer saccharum M. localizadas en Ontario (Canadá) donde se obtuvieron medidas de campo de muestras foliares, así como datos hiperespectrales del sensor aerotransportado CASI en 1997, 1998 y 1999. Los indices ópticos desarrollados a nivel de hoja fueron aplicados, a través de modelos de cobertura, a los datos de reflectancia obtenidos por CASI de 2 m de resolución espacial y 72 bandas[EN] This paper demonstrates the use and applications of Canopy Reflectance Models (CR) with airborne hyperspectral CASI data. SAIL and Kuusk canopy reflectance models are the link between the leaf and canopy levels: leaf-level relationships obtained between optical indices and stress bioindicators, such as chlorophyll content and chlorophyll fluorescence can be scaled-up to the canopy level using canopy reflectance models. The application of canopy reflectance models is demonstrated with the results obtained in the Bioindicators of Forest Sustainability Project. The work was carried out in 12 study areas of Acer saccharum M. in the Algoma Region, Ontario (Canada), where field measurements and hyperspectral CASI imagery have been collected in 1997, 1998 and 1999 deployments. Single leaf reflectance and transmittance, chlorophyll and carotenoid content, and chlorophyll fluorescence of broad leaves were measured. The physiological indices and derivative analysis indices extracted from leaf spectral reflectance were tested at canopy level using CASI data of 72 channels and 2 m spatial resolution.Peer reviewe

Vegetation stress detection through chlorophyll a+b estimation and fluorescence effects on hyperspectral imagery",

ABSTRACT between the light and the canopy under observation, physical methods must be developed (Zarco-Tejada, Physical principles applied to remote sensing data are key to suc- 2000). cessfully quantifying vegetation physiological condition from the study of the light interaction with the canopy under observation. We used The C aϩb content is a potential indicator of vegetatio

Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling

Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r2VALUES>0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.Data collection was partially supported by the European Union's Horizon 2020 research and innovation programme through grant agreements POnTE (635646) and XF-ACTORS (727987). A. Hornero was supported by research fellowship DTC GEO 29 “Detection of global photosynthesis and forest health from space” from the Science Doctoral Training Centre (Swansea University, UK). The authors would also like to thank QuantaLab-IAS-CSIC (Spain) for laboratory assistance and the support provided during the airborne campaigns and image processing. B. Landa, C. Camino, M. Montes-Borrego, M. Morelli, M. Saponari and L. Susca are acknowledged for their support during the field campaigns, as well as IPSP-CNR and Dipartimento di Scienze del Suolo (Università di Bari, Italy) as host institutions

Recent research accomplishments on early detection of Xylella fastidiosa outbreaks in the Mediterranean Basin

Xylella fastidiosa is a major transboundary plant pest, causing severe socioeconomic impacts. Development of preventive strategies and methods for surveillance, early detection, monitoring, and accurate diagnosis of X. fastidiosa and its vectors, are keys to preventing the effects of this plant pathogen, and assist timely eradication or optimisation of containment measures. This review focuses on approaches for early detection of X. fastidiosa in the Mediterranean Basin, including development of climatic suitability risk maps to determine areas of potential establishment, and epidemiological models to assist in outbreak management through optimized surveillance and targeted responses. The usefulness of airborne hyperspectral and thermal images from remote sensing to discriminate X. fastidiosa infections from other biotic- and abiotic-induced spectral signatures is also discussed. The most commonly used methods for identifying X. fastidiosa in infected plants and vectors, and the molecular approaches available to genetically characterize X. fastidiosa strains, are described. Each of these approaches has trade-offs, but stepwise or simultaneous combinations of these methods may help to contain X. fastidiosa epidemics in the Mediterranean Basin

Multimodal Earth observation data fusion: Graph-based approach in shared latent space

Multiple and heterogenous Earth observation (EO) platforms are broadly used for a wide array of applications, and the integration of these diverse modalities facilitates better extraction of information than using them individually. The detection capability of the multispectral unmanned aerial vehicle (UAV) and satellite imagery can be significantly improved by fusing with ground hyperspectral data. However, variability in spatial and spectral resolution can affect the efficiency of such dataset's fusion. In this study, to address the modality bias, the input data was projected to a shared latent space using cross-modal generative approaches or guided unsupervised transformation. The proposed adversarial networks and variational encoder-based strategies used bi-directional transformations to model the cross-domain correlation without using cross-domain correspondence. It may be noted that an interpolation-based convolution was adopted instead of the normal convolution for learning the features of the point spectral data (ground spectra). The proposed generative adversarial network-based approach employed dynamic time wrapping based layers along with a cyclic consistency constraint to use the minimal number of unlabeled samples, having cross-domain correlation, to compute a cross-modal generative latent space. The proposed variational encoder-based transformation also addressed the cross-modal resolution differences and limited availability of cross-domain samples by using a mixture of expert-based strategy, cross-domain constraints, and adversarial learning. In addition, the latent space was modelled to be composed of modality independent and modality dependent spaces, thereby further reducing the requirement of training samples and addressing the cross-modality biases. An unsupervised covariance guided transformation was also proposed to transform the labelled samples without using cross-domain correlation prior. The proposed latent space transformation approaches resolved the requirement of cross-domain samples which has been a critical issue with the fusion of multi-modal Earth observation data. This study also proposed a latent graph generation and graph convolutional approach to predict the labels resolving the domain discrepancy and cross-modality biases. Based on the experiments over different standard benchmark airborne datasets and real-world UAV datasets, the developed approaches outperformed the prominent hyperspectral panchromatic sharpening, image fusion, and domain adaptation approaches. By using specific constraints and regularizations, the network developed was less sensitive to network parameters, unlike in similar implementations. The proposed approach illustrated improved generalizability in comparison with the prominent existing approaches. In addition to the fusion-based classification of the multispectral and hyperspectral datasets, the proposed approach was extended to the classification of hyperspectral airborne datasets where the latent graph generation and convolution were employed to resolve the domain bias with a small number of training samples. Overall, the developed transformations and architectures will be useful for the semantic interpretation and analysis of multimodal data and are applicable to signal processing, manifold learning, video analysis, data mining, and time series analysis, to name a few.This research was partly supported by the Hebrew University of Jerusalem Intramural Research Found Career Development, Association of Field Crop Farmers in Israel and the Chief Scientist of the Israeli Ministry of Agriculture and Rural Development (projects 20-02-0087 and 12-01-0041)

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields