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

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)

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

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)

Global Estimation of Biophysical Variables from Google Earth Engine Platform

This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R2 role= presentation \u3e2 = 0.87, RMSE = 0.54 m2 role= presentation \u3e2/m2 role= presentation \u3e2 and ME = 0.03 m2 role= presentation \u3e2/m2 role= presentation \u3e2 in the case of LAI, and R2 role= presentation \u3e2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 role= presentation \u3e2 = 0.42 and R2 role= presentation \u3e2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications