Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented.
Abstract. The CloudRoots field experiment was designed to obtain a comprehensive observational dataset that includes soil, plant, and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub-kilometre scale. Our findings demonstrate the need to include measurements at leaf level to better understand the relations between stomatal aperture and evapotranspiration (ET) during the growing season at the diurnal scale. Based on these observations, we obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the model reproduces the stomatal leaf conductance and the leaf-level photosynthesis satisfactorily. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From highly resolved vertical profile measurements of carbon dioxide (CO2) and other state variables, we infer a profile of the CO2 assimilation in the canopy with non-linear variations with height. Observations taken with a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of photosynthetically active radiation by cloud flecks. More specifically, we find 2 min delays between the cloud radiation perturbation and ET. To study the relevance of advection and surface heterogeneity for the land–atmosphere interaction, we employ a coupled surface–atmospheric conceptual model that integrates the surface and upper-air observations made at different scales from leaf to the landscape. At the landscape scale, we calculate a composite sensible heat flux by weighting measured fluxes with two different land use categories, which is consistent with the diurnal evolution of the boundary layer depth. Using sun-induced fluorescence measurements, we also quantify the spatial variability of ET and find large variations at the sub-kilometre scale around the CloudRoots site. Our study shows that throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large diurnal variations of plant transpiration at the leaf, plant, canopy, and landscape scales. Integrating different advanced instrumental techniques with modelling also enables us to determine variations of ET that depend on the scale where the measurement were taken and on the plant growing stage.
This article is protected by copyright. All rights reserved • Solar-induced fluorescence (SIF) is highly relevant in mapping photosynthesis from remotesensing platforms. This requires linking SIF to photosynthesis and understanding the role of non-photochemical quenching (NPQ) mechanisms under field conditions. Hence, active and passive fluorescence were measured in Arabidopsis thaliana with altered NPQ in outdoor conditions. • Plants had mutations in either violaxanthin de-epoxidase (npq1) or PsbS protein (npq4), resulting in reduced NPQ capacity. Parallel measurements of NPQ, photosystem II efficiency, SIF and spectral reflectance (ρ) were conducted diurnally on one sunny summer day and two consecutive days during a simulated cold spell. • Results showed that both npq mutants had higher levels of SIF compared to wild type. Changes in reflectance were related to changes in the violaxanthin-antheraxanthin-zeaxanthin cycle and not to PsbS-mediated conformational changes. When plants were exposed to cold temperatures, rapid onset of photoinhibition strongly quenched SIF in all lines. • Using well-characterized npq mutants of Arabidopsis, we could show for the first time the quantitative link between SIF, photosynthetic efficiency, NPQ components and leaf reflectance. We discuss the functional potential and limitations of SIF and reflectance measurements for estimating photosynthetic efficiency and NPQ in the field.
This work presents a Sentinel-2 based exploratory workflow for the estimation of Above Ground Biomass (AGB) in a Mediterranean forest. Up-to-date and reliable mapping of AGB has been increasingly required by international commitments under the climate convention, and in the last decades, remote sensing-based studies on the topic have been widely investigated.After the generation of several vegetation and topographic features, the proposed approach consists of 4 major steps: 1) Feature selection 2) AGB prediction with k-Nearest Neighbour (kNN), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Networks (ANN); 3) hyper-parameters finetuning with Bayesian Optimization; and finally, 4) model explanation with the SHapley Additive exPlanations (SHAP) package. The following results were obtained: 1) before hyper-parameters optimization, the Deep Neural Network (DNN) yielded the best performance with a Root Mean Squared Error (RMSE) of 42.30 t/ha; 2) after hyper-parameters fine-tuning with Bayesian Optimization, the Extreme Gradient Boosting (XGB) model yielded the best performance with a RMSE of 37.79 t/ha; 3) model explanation with SHAP allowed for a deeper understanding of the features impact on the model predictions. Finally, the predicted AGB throughout the study area showed an average value of 83 t/ha, ranging from 0 t/ha to 346.56 t/ha.
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