Development of an earth observation processing chain for crop biophysical parameters at local and global scale

This thesis’ topics embrace remote sensing for Earth observation, specifically in Earth vegetation monitoring. The Thesis’ main objective is to develop and implement an operational processing chain for crop biophysical parameters estimation at both local and global scales from remote sensing data. Conceptually, the components of the chain are the same at both scales: First, a radiative transfer model is run in forward mode to build a database composed by simulations of vegetation surface reflectance and concomitant biophysical parameters associated to those spectrum. Secondly, the simulated database is used for training and testing nonlinear and non-parametric machine learning regression algorithms. The best model in terms of accuracy, bias and goodness-of-fit is then selected to be used in the operational retrieval chain. Once the model is trained, remote sensing surface reflectance data is fed into the trained model as input in the inversion process to retrieve the biophysical parameters of interest at both local and global scales depending on the inputs spatial resolution and coverage. Eventually, the validation of the leaf area index estimates is performed at local scale by a set of ground measurements conducted during coordinated field campaigns in three countries during 2015 and 2016 European rice seasons. At global scale, the validation is performed through intercomparison with the most relevant and widely validated reference biophysical products. The work elaborated in this Thesis is structured in six chapters including an introduction of remote sensing for Earth observation, the developed processing chain at local scale, the ground LAI measurements acquired with smartphones, the developed chain at global scale, a chapter discussing the conclusions of the work, and a chapter which includes an extended abstract in Valencian. The Thesis is completed by an annex which include a compendium of peer-reviewed publications in remote sensing international journals

Demo 162. Ilustración de la fuerza de Lorentz con una bombilla de filamento de carbono

Se presenta una demostración de aula que muestra que el filamento de carbono de la bombilla vibra cuando circula corriente AC y se le aproxima un imán. Esto demuestra que un elemento de corriente eléctrica es una fuente del campo magnético y sirve de ilustración de la fuerza de Lorentz.A classroom demonstration is presented showing that the carbon filament in the light bulb vibrates when AC current flows and is approached by a magnet. This phenomenon shows that an element of electric current is a source of the magnetic field and illustrates the Lorentz force

Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data

This work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields. Three series of dual-pol interferometric SAR data acquired with large baselines (2–3 km) by the TanDEM-X system during its science phase (April–September 2015) are exploited. A novel inversion algorithm especially suited for rice fields cultivated in flooded soil is proposed and evaluated. The validation is carried out over three test sites located in geographically different areas: Sevilla (SW Spain), Valencia (E Spain), and Ipsala (W Turkey), in which different rice types are present. Results are obtained during the whole growth cycle and demonstrate that PolInSAR is useful to produce accurate height estimates (RMSE 10–20 cm) when plants are tall enough (taller than 25–40 cm), without relying on external reference information.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under project TIN2014-55413-C2-2-P. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement 606983, and the Land-SAF (the EUMETSAT Network of Satellite Application Facilities) project. The in-situ measurements in the Ipsala site were conducted with the funding of The Scientific and Technological Research Council of Turkey (TUBITAK, Project No.: 113Y446)

Demo 161. Resistencia eléctrica de un metal y de un semimetal. Bombillas de filamento incandescente: tungsteno vs. carbono

Se presenta una demostración de aula que muestra que la curva voltaje-intensidad del filamento de una bombilla incandescente es no lineal. Para un filamento de tungsteno se observa que la resistencia eléctrica aumenta con la temperatura del filamento, la cual aumenta con la potencia eléctrica consumida, pues esta se disipa por radiación según la ley de Stefan. Para un filamento de carbono se observa que la resistencia eléctrica disminuye con la temperatura, evidenciando así que no es un metal (sino un semimetal).A classroom demonstration is presented showing that the voltage-intensity curve of the filament of an incandescent light bulb is non-linear. For a tungsten filament, it is observed that the electrical resistance increases with the temperature of the filament, which increases with the electrical power consumed, since this is dissipated by radiation according to Stefan's law. For a carbon filament, it is observed that the electrical resistance decreases with temperature, thus showing that it is not a metal (but a semi-metal)

Deep learning for agricultural land use classification from Sentinel-2

[ES] En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de imágenes. Sin embargo, se han realizado pocos esfuerzos encaminados a su comprensión, lo que lleva a ejecutarlos como si fueran “cajas negras”. Este trabajo pretende evaluar el rendimiento y acercarnos al entendimiento de un algoritmo de aprendizaje profundo, basado en una red recurrente bidireccional de memoria corta a largo plazo (2-BiLSTM), a través de un ejemplo de clasificación de usos de suelo agrícola de la Comunidad Valenciana dentro del marco de trabajo de la política agraria común (PAC) a partir de series temporales de imágenes Sentinel-2. En concreto, se ha comparado con otros algoritmos como los árboles de decisión (DT), los k-vecinos más cercanos (k-NN), redes neuronales (NN), máquinas de soporte vectorial (SVM) y bosques aleatorios (RF) para evaluar su precisión. Se comprueba que su precisión (98,6% de acierto global) es superior a la del resto en todos los casos. Por otra parte, se ha indagado cómo actúa el clasificador en función del tiempo y de los predictores utilizados. Este análisis pone de manifiesto que, sobre el área de estudio, la información espectral y espacial derivada de las bandas del rojo e infrarrojo cercano, y las imágenes correspondientes a las fechas del período de verano, son la fuente de información más relevante utilizada por la red en la clasificación. Estos resultados abren la puerta a nuevos estudios en el ámbito de la explicabilidad de los resultados proporcionados por los algoritmos de aprendizaje profundo en aplicaciones de teledetección.[EN] The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.Este trabajo ha sido subvencionado gracias al Convenio 2019 y 2020 de colaboración entre la Generalitat Valenciana, a través de la Conselleria d’Agricultura, Medi Ambient, Canvi Climàtic i Desenvolupament Rural, y la Universitat de València – Estudi General.Campos-Taberner, M.; García-Haro, F.; Martínez, B.; Gilabert, M. (2020). Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2. Revista de Teledetección. 0(56):35-48. https://doi.org/10.4995/raet.2020.13337OJS3548056Baraldi, A., Parmiggiani, F. 1995. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293-304. https://doi.org/10.1109/36.377929Bengio, Y., Simard, P., Frasconi, P. 1994. Learning long-term dependencies with gradient descent is difficult. 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Demo 168. Demostración de la ley de Raoult y de la nucleación heterogénea con un frasco de Franklin vertical

Se presenta una demostración de aula que muestra que la condensación del vapor se produce preferentemente sobre la superficie de partículas sólidas y que la presión de vapor de una disolución disminuye cuando aumenta la concentración de soluto.A classroom demonstration is presented showing that vapor condensation occurs preferentially on the surface of solid particles and that the vapor pressure of a solution decreases as the solute concentration increases

Demo 185. Miscibilidad parcial: Fenómeno de expulsión salina

Se presenta una demostración de aula que muestra que la adición de NaCl a una mezcla líquida de dos componentes miscibles (agua y propan-2-ol) hace que dejen de ser miscibles y se formen dos fases líquidas (ternarias), una acuosa rica en sal y la otra orgánica rica en alcohol. El objetivo es ilustrar el fenómeno de expulsión salina para evidenciar la importancia de las interacciones agua-electrólito frente a las interacciones agua-propan-2-ol.A classroom demonstration is presented showing that the addition of NaCl to a liquid mixture of two miscible components (water and propan-2-ol) causes them to become immiscible and to form two (ternary) liquid phases: one aqueous phase rich in salt and one organic phase rich in alcohol. The objective is to illustrate salting out and to demonstrate the importance of water-electrolyte interactions compared to water-propan-2-ol interactions