MACHINE LEARNING AND BIG DATA TECHNIQUES FOR SATELLITE-BASED RICE PHENOLOGY MONITORING

New sources of information are required to support rice production decisions. To cope with this challenge, studies have found practical applications on mapping rice using remote sensing techniques. This study attempts to implement a methodology aimed at monitoring rice phenology using optical satellite data. The relationship between rice phenology and reflectance metrics was explored at two levels: growth stages and biophysical modifications caused by diseases. Two optical moderate-resolution missions were combined to detect growth phases. Three machine-learning approaches (random forest, support vector machine, and gradient boosting trees) were trained with multitemporal NDVI data. Analytics from validation showed that the algorithms were able to estimate rice phases with performances above 0.94 in f-1 score. Tested models yielded an overall accuracy of 71.8%, 71.2%, 60.9% and 94.7% for vegetative, reproductive, ripening and harvested categories. A second exploration was carried out by combining Sentinel-2 data and ground-based information about rice disease incidence. K-means clustering was used to map rice biophysical changes across reproductive and ripening phases. The findings ascertained the remote sensing capabilities to create new information about rice for Colombia’s conditions

Machine learning and big data techniques for satellite-based rice phenology

New sources of information are required to support rice production decisions. To cope with this challenge, studies have found practical applications on mapping rice using remote sensing techniques. This study attempts to implement a methodology aimed at monitoring rice phenology using optical satellite data. The relationship between rice phenology and reflectance metrics was explored at two levels: growth stages and biophysical modifications caused by diseases. Two optical moderate-resolution missions were combined to detect growth phases. Three machine-learning approaches (random forest, support vector machine, and gradient boosting trees) were trained with multitemporal NDVI data. Analytics from validation showed that the algorithms were able to estimate rice phases with performances above 0.94 in f-1 score. Tested models yielded an overall accuracy of 71.8%, 71.2%, 60.9% and 94.7% for vegetative, reproductive, ripening and harvested categories. A second exploration was carried out by combining Sentinel-2 data and ground-based information about rice disease incidence. K-means clustering was used to map rice biophysical changes across reproductive and ripening phases. The findings ascertained the remote sensing capabilities to create new information about rice for Colombia’s conditions

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

The Evolution of Galaxies and Their Environment

The Third Teton Summer School on Astrophysics discussed the formation of galaxies, star formation in galaxies, galaxies and quasars at high red shift, and the intergalactic and intercluster medium and cooling flows. Observation and theoretical research on these topics was presented at the meeting and summaries of the contributed papers are included in this volume

Coastal area management in Southeast Asia: policies, management strategies and case studies

Coastal zone management, Southeast Asia,

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Combining remote sensing and crop modeling techniques to derive a nitrogen fertilizer application strategy

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.In dieser Arbeit wurde eruiert, ob mit Hilfe von Sensordaten und Pflanzenwachstumsmodellen eine N-Düngemittelstrategie abgeleitet werden kann, die in der Lage ist die ökologischen Belastung zu verringern. Dies umfasste die Evaluation folgender Fragestellungen: (I) Wird die spektrale Reflexion und somit die Bestimmung der N-Konzentration durch die Messung an verschiedenen Blattetagen und -Positionen beeinflusst (Publikation I)? Für die Klärung dieser ersten Frage wurden in zwei hydroponischen Gewächshausversuchen Weizenpflanzen bei unterschiedlicher N-Exposition und Trockenstress kultiviert. Für jede Pflanze wurden spektrale Reflexionsmessungen an drei Blattetagen und an drei Positionen auf dem Blatt durchgeführt. Insgesamt wurden die 16 üblichsten auf spektraler Reflexion basierenden Vegetationsindizes für jede Kombination von Blattetage und -Position berechnet. Die N-Konzentration der Pflanze wurde durch Laboranalysen bestimmt. Weder die Position auf dem Blatt noch die Blattetage hatten einen Einfluss auf die Genauigkeit der Bestimmung der N-Konzentration der Pflanze durch spektrale Reflexionsmessungen. Daher sind Messungen auf Bestandsebene ausreichend. Falls jedoch weitere Stressfaktoren wie Trockenheit oder Krankheitsbefall auftreten, kann eine Differenzierung zwischen verschiedenen Blattetagen notwendig oder von Vorteil sein. In der nächsten Fragestellung (Publikation II) wurde untersucht, wie Blattkrankheiten in ein DSSAT-Weizenmodell integriert werden können, um so die Auswirkungen von Blattkrankheiten auf den Ertrag zu simulieren. Eine Modellerweiterung wurde entwickelt, durch die Integration eines Blattkrankheitsmoduls in das bestehende DSSAT Weizenmodell. Das Modul simuliert die Auswirkungen des täglichen Schadens durch die Krankheit auf die Photosynthese und den Blattflächenindex. Der Ansatz wurde an einem zweijährigen Datensatz aus Argentinien mit verschiedenen Weizensorten und an einem einjährigen Datensatz aus Deutschland mit verschiedenen Inokulumniveaus von Septoria tritici-Blotch (STB) getestet. Die Sensitivitätsanalyse zeigte die Möglichkeit des Modells, den Ertrag in einer exponentiellen Beziehung mit zunehmendem Infektionsgrad (0-70%) zu reduzieren. Das erweiterte Modell stellt somit eine Möglichkeit dar, STB-Infektionen standortspezifisch in Verbindung mit verfügbaren Sensordaten zu simulieren. Um die Modellierungsgenauigkeit noch weiter zu erhöhen, wurde der Einsatz eines MM-Ansatz geprüft. Die Kombination von verschiedenen Modellen erhöht die Komplexität der Simulation und des damit verbundenen Kalibrierungsverfahrens, insbesondere wenn der Benutzer nicht mit allen Modellen vertraut ist. Die dritte Fragestellung (iii) untersuchte daher, ob objektive Kalibrierungsergebnisse gewährleitet werden könnten, wenn die cultivar coefficients im Modell auf Basis tatsächlich gemessener Daten mittels eines neu entwickelten automatischen Calibrator-Programms optimiert wurden. Die Modellkalibrierung wurde an einem 4-jährigen-Weizendüngungsversuch in Südwestdeutschland durchgeführt. Die statistische Auswertung des Kalibrierverfahrens zeigte zufriedenstellende Ergebnisse und führte zur vierten Fragestellung. Die vierte Fragestellung befasste sich mit dem Thema, ob ein MM-Ansatz die Gesamtmodelliergenauigkeit verbessern kann. Die Auswertung des Düngemittelversuchs zeigte in den meisten Fällen eine verbesserte Modellierungsgenauigkeit, insbesondere in einem durch Wasserstress geprägten Versuchsjahr wie 2018. Unter Verwendung eines MM-Ansatzes, durch Anpassung der Modellvariablen und durch die Integration von Sensordaten wurde ein Nitrogen Application Prescription System (NAPS) entwickelt. Eine Voraussetzung für das NAPS-Konzepts ist das Vorhandensein von Langzeit-Daten (Ertrag, Klima- und Bodenbedingungen), um eine korrekte MM-Kalibrierung zu gewährleisten. Nach der Kalibrierung werden die Bedingungen der aktuellen Wachstumssaison (Wetter, Managementinformationen) bis zum Düngetermin benötigt. Anschließend berechnet das NAPS basierend auf Sensorinformationen und simulierten Wetterbedingungen verschiedene Düngeszenarien. Ökonomische und ökologische Kriterien bestimmen die optimierte Düngemenge. Darüber hinaus muss das NAPS-Konzept auf räumlicher Ebene arbeiten, indem es die Bodenparameter berücksichtigt. So kann unter Beachtung der Feldvariabilität eine standortspezifische N-Ausbringung gewährleistet werden. In Summe zeigte sich, dass NAPS die Düngung an ökonomische und ökologische Faktoren anpasst, indem es aktuelle Sensordaten, historische Ertragsaufzeichnungen und zukünftige Wettervorhersagen zur Ermittlung einer präziseren N-Ausbringung nutzt. Das Konzept hat so das Potenzial, die nachteiligen Auswirkungen einer Überdüngung zu begrenzen, so dass eine umweltfreundlichere Agrarproduktion gewährleistet wird