Browsing by Subject "Remote sensing"

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Application of infrared imaging for early detection of downy mildew (Plasmopara viticola) in grapevine
(2022) Zia-Khan, Shamaila; Kleb, Melissa; Merkt, Nikolaus; Schock, Steffen; Müller, Joachim
Late detection of fungal infection is the main cause of inadequate disease control, affecting fruit quality and reducing yield of grapevine. Therefore, infrared imagery as a remote sensing technique was investigated in this study as a potential tool for early disease detection. Experiments were conducted under field conditions, and the effects of temporal and spatial variability in the leaf temperature of grapevine infected by Plasmopara viticola were studied. Evidence of the grapevine’s thermal response is a 3.2 °C increase in leaf temperature that occurred long before visible symptoms appeared. In our study, a correlation of R2 = 0.76 at high significance level (p ≤ 0.001) was found between disease severity and MTD. Since the pathogen attack alters plant metabolic activities and stomatal conductance, the sensitivity of leaf temperature to leaf transpiration is high and can be used to monitor irregularities in temperature at an early stage of pathogen development.
Combining remote sensing and crop modeling techniques to derive a nitrogen fertilizer application strategy
(2020) Röll, Georg; Graeff-Hönninger, Simone
The crucial question in this thesis was how can remote sensing data and crop models be used to derive a N fertilizer strategy that is capable to lower the environmental side effects of N fertilizer application. This raised the following detailed objectives: The first objective (i) how N content determination via spectral reflectance is influenced by different leaves and positions on the leaf was investigated in Publication I. Different wheat plants were cultivated under different N levels and under drought stress in two hydroponic greenhouse trials. Spectral reflectance measurements were taken from three leaves and at three positions on the leaf for each plant. In total, 16 vegetation indices broadly used in the literature were calculated based on the spectral reflectance for each combination of leaf and position. The plant N content was determined by lab analyses. Neither the position on the leaf nor leaf number had an impact on the accuracy of plant N determination via spectral reflectance measurements. Therefore measurements taken at the canopy level seem to be a valid approach. However, if other stress symptoms like drought or disease infection occur, a differentiation between leaves and positions on the leaf might play a more crucial role. Publication II dealt with the second objective on (ii), how to incorporate leaf disease into the DSSAT wheat model to enable the simulation of the impact of leaf disease on yield. An integration of sensor information in crop growth models requires the update of model state variables. A model extension was developed by adding a pest damage module to the existing wheat model. The approach was tested on a two-year dataset from Argentina with different wheat cultivars and on a one-year dataset from Germany with different inoculum levels of septoria tritici blotch (STB). After the integration of disease infection, the accuracy of the simulated yield and leaf area index (LAI) was improved. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). A sensitivity analysis also showed a strong responsiveness of the model by the integration of different STB disease infection scenarios. Increasing the modeling accuracy even further a MM approach seems to be suitable. Assembling more models increases the complexity of the simulation and the involved calibration procedure especially if the user is not familiar with all models. To avoid these conflicts, Publication III evaluated the third objective (iii) if an automatic calibration procedure in a MM approach for winter wheat can eliminate the subjectivity factor in model calibration. The model calibration was performed on a 4-yr N wheat fertilizer trial in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-Index 0.93) and evaluation dataset (d-Index 0.81). This lead to the fourth (iv) objective to use a MM approach to improve the overall modeling accuracy. The evaluation of a fertilizer trial showed an improved modeling accuracy in most cases, especially in the drought season 2018. Based on the combination of a MM approach and the incorporation of sensor data, a Nitrogen Application Prescription System (NAPS) was developed. The initial NAPS setup requires long term recorded data (yield, weather, and soil) to ensure proper MM calibration. After calibration, the current growing season conditions are required (weather, management information) until the N application date. Afterward, the NAPS incorporates remote sensing information and generated weather for running future N application scenarios. The selection of the proper amount of N is determined by economic and ecological criteria. Furthermore, in order to account for differences in in-field variabilities and to deliver a N prescription site-specifically, the NAPS concept has to be applied on a geospatial scale by adjusting soil parameters spatially. The NAPS concept has the potential to adjust the N application more economically and ecologically by using current sensor data, historical yield records, and future weather prediction to derive a more precise N application strategy. Finally, this concept exhibits the potential for reconciliation of the issue of an economic, agricultural production without harming the environment.
Convective-scale data assimilation of thermodynamic lidar data into the weather research and forecasting model
(2022) Thundathil, Rohith Muraleedharan; Wulfmeyer, Volker
This thesis studies the impact of assimilating temperature and humidity profiles from ground-based lidar systems and demonstrates its value for future short-range forecast. Thermodynamic profile obtained from the temperature Raman lidar and the water-vapour differential absorption lidar of the University of Hohenheim during the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) project Observation Prototype Experiment (HOPE) are assimilated into the Weather Research and Forecasting model Data Assimilation (WRFDA) system by means of a new forward operator. The impact study assimilating the high-resolution thermodynamic lidar data was conducted using variational and ensemble-based data assimilation methods. The first part of the thesis describes the development of the thermodynamic lidar operator and its implementation through a deterministic DA impact study. The operator facilitates the direct assimilation of water vapour mixing ratio (WVMR), a prognostic variable in the WRF model, without conversion to relative humidity. Undesirable cross sensitivities to temperature are avoided here so that the complete information content of the observation with respect to the water vapour is provided. The assimilation experiments were performed with the three-dimensional variational (3DVAR) DA system with a rapid update cycle (RUC) with hourly frequency over ten hours. The DA experiments with the new operator outperformed the previously used relative humidity operator, and the overall humidity and temperature analyses improved. The simultaneous assimilation of temperature and WVMR resulted in a degradation of the temperature analysis compared to the improvement observed in the sole temperature assimilation experiment. The static background error covariance matrix (B) in the 3DVAR was identified as the reason behind this behaviour. The correlation between the temperature and WVMR variables in the background error covariance matrix of the 3DVAR, which is static and not flow-dependent, limited the improvement in temperature. The second part of the thesis provides a solution for overcoming the static B matrix issue. A hybrid, ensemble-based approach was applied using the Ensemble Transform Kalman Filter (ETKF) and the 3DVAR to add flow dependency to the B matrix. The hybrid experiment resulted in a 50% lower temperature and water vapour root mean square error (RMSE) than the 3DVAR experiment. Comparisons against independent radiosonde observations showed a reduction of RMSE by 26% for water vapour and 38% for temperature. The planetary boundary layer (PBL) height of the analyses also showed an improvement compared to the available ceilometer. The impact of assimilating a single lidar vertical profile spreads over a 100 km radius, which is promising for future assimilation of water vapour and temperature data from operational lidar networks for short-range weather forecasting. A forecast improvement was observed for 7 hours lead time compared with the ceilometer derived planetary boundary layer height observations and 4 hours with Global Navigation Satellite System (GNSS) derived integrated water vapour observations. With the help of sophisticated DA systems and a robust network of lidar systems, the thesis throws light on the future of short-range operational forecasting.
Entwicklung eines GIS-gestützten schlagbezogenen Führungsinformationssystems für die Zuckerwirtschaft
(2005) Laudien, Rainer; Doluschitz, Reiner
The European Union aspires the GIS-based documentation of every agricultural area under cultivation from the year 2005 onwards. With this in mind, this thesis aims to design and develop a user-friendly Management Information System (MIS) for the sugar beet industry, which processes, visualizes, archives and documents geographical, remote sensing and attribute data. To meet the EU requirements the design of this "'Sugar beet Management Information System"' (SuMIS) is targeted at a GIS-based, modular, field-based approach which reflects the whole sugar beet supply chain. Therefore, the user of SuMIS will be able to geo-track and -trace every step from soil sampling to the beet delivery (fff = "from farm to factory"). By including and integrating GIS- and remote sensing data, SuMIS is a comprehensive System which can also be used as a Decision Support System within the Supply Chain Management. Due to the modular process-oriented design of SuMIS the potential of the system can be used by different users of the Supply Chain e.g. the field based documentation on the part of the farmer or the GIS-based decision making on the part of the sugar company. The design of SuMIS is based on the geo-datasets of two areas under investigation: Gemmingen/Kraichgau (area 1) and Plattling/Niederbayern (area 2). The dataset of area 1 represents the main part of the relational SuMIS geo-database and includes operational and external geo-data. The sugar beets in this area do not show plant diseases in general. Therefore, multi- and hyperspectral reflectance data of selected fields of area 2 is used to detect biotic growth-anomalies, general stress indicators and differences concerning plant vitalities and to create the respective spatial cognition. In order to collect the field data, a hyperspectral spectroradiometer (FieldSpec Handheld) is mounted stationary on a developed measurement device. This data is stored in a HTML-based spectral library. Besides that, multitemporal tractor- and airborne hyperspectral spectroradiometer measurements (GVIS, AVIS) are included to validate the ground based data. The reflection measurements are utilized to differentiate between healthy and unhealthy plants by using multispectral and hyperspectral vegetation indices. SuMIS includes new components which are developed and embedded by using the developer software "'Visual Basic"'. These are combined with existing functionalities in order to meet the EU GIS-requirements. Beside the functionalities which are used to analyze the hyperspectral data, two land-use classification methods are presented, applied and compared. Therefore, an object oriented (by using ERDAS Imagine® and a pixel based approach (by using eCognition) is employed to differentiate sugar beets from other crops in a simple and time efficient manner. QuickBird high resolution satellite form the basis for the accurate land use map. By applying the SuMIS functionalities and tools presented in this thesis, the users will be able to digitize their field data without any knowledge about GIS or geo-databases. Furthermore, storing and visualizing alphanumeric geodata is also possible by using these tools. Because of the information-specific structure of the geodata and its storage in several information layers, SuMIS is able to generate for instance mathematic calculations, clip-, merge- and join-procedures. This can be used for the spatial analysis or for creating new information layers. In this thesis such spatial GIS-results are shown in the context of a case study. The results of this case study indicate that the approaches developed lead to plausible results. Besides the description of the design of SuMIS and it's functionalities, the acceptance and survey of the expected individual benefits by potential selected users has been tested. Concerning the utility and value of SuMIS for the sugar beet industry, the functionalities are evaluated. The investigated results are discussed and perspectives for a broad application are described.
Fernerkundungsgestützte Analyse und Bewertung ökologischer Auswirkungen des Anbaus von Bioenergiepflanzen auf die Agro-Biodiversität anhand der Modellierung der Habitatansprüche der Feldlerche (Alauda arvensis)
(2017) Schlager, Patric; Schmieder, Klaus
For the first time in 2002, the transformation of the conventional energy system into a system based on renewable energies was politically and legally decided in Germany. On the regional level numerous communities and municipalities followed this decision by voicing their own political resolutions, addressing the coverage of energy consumption with renewable energies. Their implementation is accompanied by a spatial expansion of bioenergy crops which lead to a controversial discussion about food safety, biodiversity and landscape change. Framed by the above mentioned discussion, this study assesses potential changes of skylark (Alauda arvensis) occurrence caused by a spatial expansion of bioenergy crops in the municipality of Schwäbisch Hall, Germany. The skylark was selected due to the comprehensive state of research about skylarks, their endangerment (“Red list of German breeding birds”), and the status as umbrella species for open agricultural landscapes (skylarks typically avoid vertical structures like hedges or edges of forests). The latter emphasizes their role as representatives for other species which are potentially affected by an expansion of bioenergy crops. This study is based on a stratified bird monitoring scheme of Baden-Württemberg, which was developed during a project that aimed to set up an indicator for species richness and was financed by the Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz (BMELV). From the bird monitoring scheme, the stratum, which covers the municipality of Schwäbisch Hall, was extracted and served as a base for the development of a Generalized Linear Habitat Model of the skylark. In order to assess potential habitat changes caused by an expansion of bioenergy crops, Schwäbisch Hall was mapped with an airborne remote sensing technology in 2011. The resulting aerial images were transformed into orthophotos and later classified (focusing on agricultural areas) with an object oriented image analysis approach. Based on the outcomes of the habitat association model and the land use classification, skylark territories were predicted for 1 km² plots covering Schwäbisch Hall. For an in-depth understanding of ecological impacts from expanded bioenergy cropping, a bioenergy scenario was developed considering § 17 BBodSchG (national soil protection act) and regional food security. Based on the scenario, skylark territories were predicted for 1 km² plots covering Schwäbisch Hall. The most reasonable habitat association model resulted in a negative binomial Generalized Linear Model with the predictors winter sown crops and mean patch size per plot. Model performance was assessed by Wald z-statistics with p-values, ANOVA, explained variance, theta, residuals, AIC, and independent field data. Field data was only available for one plot. Therefore, the field data only indicate model performance. The comparison of the model predictions with the field data resulted in an accuracy of 92.21%. The land use classification resulted in the following five classes: 1. winter sown crops (33985.78 ha), 2. maize (9621.36 ha), rapeseed (2952.36 ha), unidentified crops (7244.18 ha), and grassland (30720.88 ha). Grasslands were not mapped by remote sensing techniques, but taken from a digital landscape model. Accuracy assessment showed an overall accuracy of 89.16 % and 0.78 kappa statistics. Prediction of skylark territories based on the land use classification of 2011 resulted in 46269 territories, or a mean density of 8.4 territories per 10 ha on agricultural areas and 5.4 territories per 10 ha on agricultural and grassland combined areas. The scenario assumed a three partite crop rotation (maize, rapeseed, winter sown crops) and a mean value of 0.17 ha per inhabitant for food security. Areas for fodder production were considered in course of the calculation of food security because Schwäbisch Hall is characterized by many livestock farms, which made it necessary to avoid conflicts between potential bioenergy sites and areas for fodder production. Considering the above mentioned assumptions, Schwäbisch Hall has a bioenergy potential of 5955 ha for maize and 15033 ha for rapeseed cropping. The results of the bioenergy scenario were randomly distributed to the land use polygons which resulted from the remote sensing analysis. With that, prediction of skylark territories based on the bioenergy scenario was feasible. Skylark territories for the bioenergy scenario resulted in 36472 territories, or a mean value of 6.8 territories per 10 ha on agricultural areas and 4.3 territories per 10 ha on agricultural and grassland combined areas. Considering both land use options, skylark territories declined by 8797 in total numbers or by 19.43 % in relative numbers. In addition to the land use options described above, landscape structure and territory distribution were analyzed based on six landscape units (Naturräumliche Haupteinheiten) covering the municipality of Schwäbisch Hall. The analysis revealed an agriculturally dominated northwestern part, with high numbers and mean values of skylark territories, and a grassland/forest dominated southeastern part, with lower numbers and mean values of skylark territories. The relative decline of these territories between the two land use options within the landscape units resulted approximately in 22 % in the northwestern and approximately 11-15 % in the southeastern part. The results indicate that an expansion of bioenergy crops will have negative effects on breeding birds in open agricultural landscapes which already suffer from degraded habitat conditions. Based on the assumptions of this study, skylark territories will decline by approximately 20 % in comparison to 2011. Yet, considering the results of the indicator report of the German National Strategy on Biodiversity (BMU 2010) and the European Bird Census Council the baseline of 2011 already represents a degraded situation in terms of habitat quality for agricultural breeding birds.
Implementation and improvement of an unmanned aircraft system for precision farming purposes
(2016) Geipel, Jakob; Claupein, Wilhelm
Precision farming (PF) is an agricultural concept that accounts for within-field variability by gathering spatial and temporal information with modern sensing technology and performs variable and targeted treatments on a smaller scale than field scale. PF research quickly recognized the possible benefits unmanned aerial vehicles (UAVs) can add to the site-specific management of farms. As UAVs are flexible carrier platforms, they can be equipped with a range of different sensing devices and used in a variety of close-range remote sensing scenarios. Most frequently, UAVs are utilized to gather actual in-season canopy information with imaging sensors that are sensitive to reflected electro-magnetic radiation in the visual (VIS) and near-infrared (NIR) spectrum. They are generally used to infer the crops’ biophysical and biochemical parameters to support farm management decisions. A current disadvantage of UAVs is that they are not designed to interact with their attached sensor payload. This leads to the need of intensive data post-processing and prohibits the possibility of real-time scenarios, in which UAVs can directly transfer information to field machinery or robots. In consequence, this thesis focused on the development of a smart unmanned aircraft system (UAS), which in the thesis’ context was regarded as a combination of a UAV carrier platform, an on-board central processing unit for sensor control and data processing, and a remotely connected ground control station. The ground control station was supposed to feature the possibility of flight mission control and the standardized distribution of sensor data with a sensor data infrastructure, serving as a data basis for a farm management information system (FMIS). The UAS was intended to be operated as a flexible monitoring tool for in-season above-ground biomass and nitrogen content estimation as well as crop yield prediction. Therefore, the selection, development, and validation of appropriate imaging sensors and processing routines were key parts to prove the UAS’ usability in PF scenarios. The individual objectives were (i) to implement an advanced UAV for PF research, providing the possibilities of remotely-controlled and automatic flight mission execution, (ii) to improve the developed UAV to a UAS by implementing sensor control, data processing and communication functionalities, (iii) to select and develop appropriate sensor systems for yield prediction and nitrogen fertilization strategies, (iv) to integrate the sensor systems into the UAS and to test the performance in example use cases, and (v) to embed the UAS into a standardized sensor data infrastructure for data storage and usage in PF applications. This work demonstrated the successful development of a custom rotary-wing UAV carrier platform with an embedded central processing unit. A modular software framework was developed with the ability to control any kind of sensor payload in real-time. The sensors can be triggered and their measurements are retrieved, fused together with the carrier’s navigation information, logged and broadcasted to a ground control station. The setup was used as basis for further research, focusing on information generation by sophisticated data processing. For a first application of predicting the grain yield of corn (Zea mays L.), a simple RGB camera was selected to acquire a set of aerial imagery of early- and mid-season corn crops. Orthoimages were processed with different ground resolutions and were computed to simple vegetation indices (VI) for a crop/non-crop classification. In addition to that, crop surface models (CSMs) were generated to estimate the crop heights. Linear regressions were performed with the corn grain yield as dependent variable and crop height and crop coverage as independent variable. The analysis showed the best prediction results of a relative root mean square error (RMSE) of 8.8 % at mid-season growth stages and ground resolutions of 4 cm px −1 . Moreover, the results indicate that with on-going canopy closure and homogeneity accounting for high ground resolutions and crop/non-crop classification becomes less and less important. For the estimation of above-ground biomass and nitrogen content in winter wheat (Triticum aestivum L.) a programmable multispectral camera was developed. It is based on an industrial multi-sensor camera, which was equipped with bandpass filters to measure four narrow wavelength bands in the so-called red-edge region. This region is the transition zone in between the VIS and NIR spectrum and known to be sensitive to leaf chlorophyll content and the structural state of the plant. It is often used to estimate biomass and nitrogen content with the help of the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). The camera system was designed to measure ambient light conditions during the flight mission to set appropriate image acquisition times, which guarantee images with high contrast. It is fully programmable and can be further developed to a real-time image processing system. The analysis relies on semi-automatic orthoimage processing. The NDVI orthoimages were analyzed for the correlation with biomass by means of simple linear regression. These models proved to estimate biomass for all measurements with RMSEs of 12.3 % to 17.6 %. The REIP was used to infer nitrogen content and showed good results with RMSEs of 7.6 % to 11.7 %. Both NDVI and REIP were also tested for the in-season grain yield prediction ability (RMSE = 9.0–12.1 %), whereas grain protein content could be modeled with the REIP, except for low-fertilized wheat plots. The last part of the thesis comprised the development of a standardized sensor data infrastructure as a first step to a holistic farm management. The UAS was integrated into a real-time sensor data acquisition network with standardized data base storage capabilities. The infrastructure was based on open source software and the geo-data standards of the Open Geospatial Consortium (OGC). A prototype implementation was tested for four exemplary sensor systems and proved to be able to acquire, log, visualize and store the sensor data in a standardized data base via a sensor observation service on-the-fly. The setup is scalable to scenarios, where a multitude of sensors, data bases, and web services interact with each other to exchange and process data. This thesis demonstrates the successful prototype implementation of a smart UAS and a sensor data infrastructure, which offers real-time data processing functionality. The UAS is equipped with appropriate sensor systems for agricultural crop monitoring and has the potential to be used in real-world scenarios.
Microwave forward model for land surface remote sensing
(2015) Park, Chang-Hwan; Wulfmeyer, Volker
In order to improve hydro-meteorological model prediction using remote-sensing measurements the difference between the model world and the observed world should be identified. The forward model proposed in this study allows us to simulate the BT (brightness temperature) from the land surface model to compare with the observed microwave BT. The proposed dielectric mixing model is the key part of the forward model to properly link the model parameters and the BT observed by remote sensing. In this study, it was established that the physically valid computation of the effective dielectric constant should be based on the arithmetic average with consideration of the proposed universal damping factor. This physically based dielectric mixing model is superior to the refractive mixing model or semi-empirical/calibration model with RMSE values of 0.96 and 0.63 for the predicted real and imaginary parts, respectively, compared to the measured values. The RMSE obtained with the new model is smaller than those obtained by other researchers using refractive mixing models for operational microwave remote sensing. Once we determine the model uncertainty using this forward model, we can update the model state using the values obtained from the remote-sensing measurement. The challenging task in this process is to resolve the ill-posed inversion problem (estimation of multiple model parameters from a single BT measurement). This study proposes a simple partitioning factor based on model physics. Again, the forward model is crucial because these factors are required to be computed in BT space. In the case study involving the Schäfertal catchment area, the proposed forward model, including the new dielectric mixing model, and the proper partitioning factors computed from land surface model physics was able to successfully extract the refined soil texture information from the microwave BT measurements. The highly resolved soil moisture variability based on the refined soil texture will allow us to predict convective precipitation with higher spatial and temporal accuracy in the numerical weather forecasting model. Moreover, microwave remote sensing using the developed forward model, which provides the soil texture, soil moisture, and soil temperature with a fine scale resolution, is expected to open up new possibilities to examine the energy balance closure problem with unprecedented realism.
Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer
(2024) Wulfmeyer, Volker; Senff, Christoph; Späth, Florian; Behrendt, Andreas; Lange, Diego; Banta, Robert M.; Brewer, W. Alan; Wieser, Andreas; Turner, David D.
A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵm≃7×10-4 g2 kg-2 s-1 for mixing ratio and ϵθ≃1.6×10-3 K2 s-1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃10-2 m2 s-3. We also demonstrate that the vertical wind ACF coefficient kw∝w′2‾ and the TKE dissipation ϵ∝w′2‾3/2. For the molecular destruction rates, we show that ϵm∝m′2‾w′2‾1/2 and ϵθ∝θ′2‾w′2‾1/2. These equations can be used for parameterizations of ϵ, ϵm, and ϵθ. All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.
Screening tools for late drought resistance in tropical potato
(2023) Hölle, Julia; Asch, Folkard
Potato (Solanum tuberosum L.) is a drought sensitive crop, and even short drought spells or infrequent irrigation during stolon formation, tuber initiation, or tuber bulking reduces tuber yields. A number of morphological traits have been described that potentially improve genotypic performance of potato under moisture deficit conditions. In breeding processes, a large set of genotypes are tested at the same time and because the genotypes differ in their phenology, various phenological stages occur simultaneously in the field. Consequently, during a drought spell different varieties will be subjected to soil moisture deficit at different phenological stages. We tested thirteen contrasting genotypes under field conditions in a desert in South Peru in four different irrigation treatments at two different soil types. The irrigation was withheld after 50, 65 and 80 days after planting until final harvest after 120 days. Sequential harvests, remote sensing and phenological evaluation was conducted in five to ten-days intervals. In literature, the belowground and aboveground development of potato has been described as closely and linearly related, meaning that in many studies belowground development is estimated according to aboveground development. The synchrony of the aboveground and belowground development is strongly influenced by both, water deficit and development stage at drought initiation. Under early drought, the aboveground development was accelerated and belowground development slowed. The opposite was found at later development stages. The earlier drought was initiated, the longer the tuber-filling phase, while the bulking phase was shortened. Water deficit also slowed down the aboveground development of flowering by a couple of days. In further drought experiments it is important to evaluated the belowground development separately, as we cannot conclude from the above to the belowground development stage. In conventional breeding experiments often only one final harvest is used to analyze the final tuber yield. This proceeding do not describe under which circumstances like stress intensity the tuber yield was achieved. Genotype evaluation in breeding experiments often relies only on visual evaluation of the aboveground biomass with no harvest of the plant. Besides the phenological stage at drought initiation the stress severity is another important aspect to determinate the drought stress response of potato genotypes. The stress severity depends on the water availability in term of soil water tension and the drought duration. In this study we developed a stress severity index (SSI) which combines all three important parameters, phenology, soil water tension and drought duration. With this SSI the selection processes should be improved and genotypes can be compared independently from environment, seasons and years. The SSI combines the yield response of potato to water deficit based on the soil tension the genotype was subjected to for the duration of the stress modified by the development stage of the genotype and drought duration. SSI allows for comparison of genotypic performance independent of year, location, season, soil type effects, and drought scenario. An SSI value of up to 1000 is able to differentiate between sensitive genotypes from more resistant genotypes. Beyond 1000, yields were generally reduced by more than 60% and a differentiation between genotypes was not possible anymore. SSI allows accumulating stress severity and thus, the higher the yield at a high SSI the stronger are the plants defense and adaptation mechanisms. Therefore, other indices that have looked into stay-green syndrome, rooting depth adaptations, leaf surface temperature, or canopy reflectance indices with only medium success, may benefit from including SSI in their indices to identify the underlying mechanisms of drought tolerance in potato. Remote sensing allows to evaluated many genotype simultaneously at field level. Proven indicators in drought tolerance screening are the normalized vegetation index (NDVI), the photochemical reflectance index (PRI) and thermography which describes the transpirational cooling of the leaves. Therefore, the last objective of this study was to validate the suitability of the SSI in remote-sensing stress diagnosis. The cluster analysis, including SSI, tuber yield reduction, NDVI, PRI and thermography identified three SSI groups with their corresponding physiological reactions under drought. The first group include SSI<1000 with fast decreasing NDVI, PRI and temperature deficit, in the second group matched SSI values from 1000 to 2000 with almost constant NDVI and temperature deficit and in the third group we found SSI beyond 2000 with corresponding small changes of NDVI, PRI and temperature deficit. The combination of these four parameters (tuber yield reduction, NDVI, PRI, thermography) explained 76 % of the variance which indicates this combination as valuable dataset analyzing drought tolerance in potato. Thus, combining these indicators with SSI and tuber yield reduction proved to be a first promising step for a new screening method for drought tolerance in a wider genotypic range. Whereas reflectance data can be recommended for assessing responses under mild to moderate stress severity, thermal imaging should rather be used to screen under mild or early drought stress.
UAV remote sensing for high-throughput phenotyping and for yield prediction of Miscanthus by machine learning techniques
(2022) Impollonia, Giorgio; Croci, Michele; Ferrarini, Andrea; Brook, Jason; Martani, Enrico; Blandinières, Henri; Marcone, Andrea; Awty-Carroll, Danny; Ashman, Chris; Kam, Jason; Kiesel, Andreas; Trindade, Luisa M.; Boschetti, Mirco; Clifton-Brown, John; Amaducci, Stefano
Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using 15 VIs time series, and predicted yield using peak descriptors derived from these VIs time series with root mean square error of 2.3 Mg DM ha−1. The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production.