Retrieving cloud ice masses from geostationary images with neural networks

Clouds are essential to the Earth\u27s energy budget and atmospheric circulation. Despite this, many cloud parameters are poorly known, including the mass of frozen hydrometeors. On the one hand, there will be specialized satellite missions targeting such hydrometeors. On the other hand, existing satellite data can be leveraged. There should be a particular interest in using geostationary satellite observations since they provide continuous coverage. Traditionally, retrievals of cloud ice masses from geostationary measurements require solar reflectances, ignore any spatial correlations, and solely retrieve the vertically-integrated ice mass density, known as the ice water path.This thesis challenges the traditional approach by applying supervised learning against CloudSat collocations, the only existing satellite mission targeting ice clouds. A set of neural networks is assembled to compare the performance of using different visible or infrared channels as retrieval input as well as the added value of using spatial context. The retrievals are probabilistic, in the sense that all neural networks predict quantiles to estimate the retrieval irreducible uncertainty, and thus represent the state of the art for atmospheric retrievals.With several spectral channels, infrared retrievals are found to have a similar performance compared to the peak accuracy offered by the combination of visible and infrared channels. However, the infrared-only retrievals enable a consistent diurnal performance. The use of spatial information reinforces the retrievals, which is demonstrated by the ability to provide skilful three-dimensional estimates of ice masses, known as ice water content, from only one infrared channel. The latter retrieval scheme is supported by an extensive validation with independent measurements.These neural network-based retrievals offer the possibility to derive new insights into cloud physics, reduce present ice cloud uncertainties, and validate climate models. Ideally, such retrieval schemes will complement the sparse measurements from specialized instruments. Finally, this thesis contains the groundwork for executing retrievals on multidecadal geostationary observations, offering unprecedented spatially and temporally continuous three-dimensional data for the tropics and mid-latitudes. The implementation of these ongoing retrievals is publicly released as part of the Chalmers Cloud Ice Climatology

Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and machine learning methods in order to estimate crop GPP. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Neural Network Emulation of the Integral Equation Model with Multiple Scattering

The Integral Equation Model with multiple scattering (IEMM) represents a well-established method that provides a theoretical framework for the scattering of electromagnetic waves from rough surfaces. A critical aspect is the long computational time required to run such a complex model. To deal with this problem, a neural network technique is proposed in this work. In particular, we have adopted neural networks to reproduce the backscattering coefficients predicted by IEMM at L- and C-bands, thus making reference to presently operative satellite radar sensors, i.e., that aboard ERS-2, ASAR on board ENVISAT (C-band), and PALSAR aboard ALOS (L-band). The neural network-based model has been designed for radar observations of both flat and tilted surfaces, in order to make it applicable for hilly terrains too. The assessment of the proposed approach has been carried out by comparing neural network-derived backscattering coefficients with IEMM-derived ones. Different databases with respect to those employed to train the networks have been used for this purpose. The outcomes seem to prove the feasibility of relying on a neural network approach to efficiently and reliably approximate an electromagnetic model of surface scattering

Remote sensing of phytoplankton in the Arctic Ocean : development, tuning and evaluation of new algorithms

Thèse en cotutelle : Université Laval, Québec, Canada, Philosophiæ doctor (Ph. D.) et Wuhan University, Wuhan, Chine.Au cours des dernières décennies, l'augmentation de la production primaire (PP) dans l'océan Arctique (AO) a en partie été associée à une augmentation de la biomasse phytoplanctonique, comme l'ont montré des études de télédétection. La concentration en chlorophylle a (Chl), un indicateur de la biomasse phytoplanctonique, est un facteur clé qui peut biaiser les estimations de la PP quand elle comporte des erreurs de mesure. En d'autres mots, une estimation précise de la Chl est cruciale pour améliorer notre connaissance de l'écosystème marin et de sa réponse au changement climatique en cours. Cependant, la télédétection de la couleur de l'océan dans l'océan Arctique présente plusieurs défis. Tout d'abord, il est bien connu que l'échec des algorithmes standards de la couleur de l'océan dans l'AO est dû à l'interférence des matières colorées et détritiques (CDM) dans le spectre visible, mais comment et dans quelle mesure cela va biaiser l'estimation de la Chl reste inconnu. En outre, la Chl étant un facteur clé utilisé pour estimer la PP, la propagation des erreurs des estimations de la Chl aux estimations de la PP doit être évaluée. Le dernier mais le plus important est qu'un algorithme robuste avec une incertitude raisonnable, en particulier pour les eaux côtières complexes et productives, n'est pas encore disponible. Pour résoudre ces problèmes, dans cette étude, nous avons d'abord compilé une grande base de données bio-optiques in situ dans l'Arctique, à partir de laquelle nous avons évalué de manière approfondie les algorithmes de couleur de l'océan actuellement disponibles du point de vue des impacts des CDM. Nous avons constaté que plus le niveau de CDM par rapport à la Chl dans la colonne d'eau était élevé, plus il biaisait les estimations de la Chl. L'analyse de sensibilité des estimations de la PP sur la Chl a montré que l'erreur des estimations de la Chl était amplifiée de 7% lorsqu'elle était passée dans l'estimation du PP en utilisant un modèle de PP résolu spectralement et verticalement. En outre, pour obtenir de meilleurs résultats, nous avons optimisé un algorithme semi-analytique (Garver-Siegel-Maritorena, GSM) pour l'AO en ajoutant la bande spectrale de 620 nm qui est moins affectée par le CDM et le signal ici est généralement élevé pour les eaux riches en CDM, devenant anisi important pour le GSM afin d'obtenir des estimates précises de la Chl. Notre algorithme ajusté, GSMA, n'a amélioré la précision que de 8% pour l'AO, mais l'amélioration pour les eaux côtières a atteint 93%. Enfin, étant donné que les algorithmes qui n'exploitent pour la plupart que les parties bleue et verte du spectre visible sont problématiques pour les eaux très absorbantes/obscures, nous avons introduit un modèle d'émission de fluorescence pour tenir compte des propriétés bio-optiques du phytoplancton dans la partie rouge du spectre visible. En se couplant avec le GSMA, le nouvel algorithme à spectre complet, FGSM, a encore amélioré la précision des estimations de la Chl de ~44% pour les eaux eutrophes. À l'avenir, des couplages sont nécessaires à des fins de validation en ce qui concerne l'application satellitaire. De plus, de nouvelles approches pouvant être appliquées pour détecter le maximum de chlorophylle sous la surface (SCM), les efflorescences en bordure de glace et/ou sous la glace, les types fonctionnels de phytoplancton (PFT), sont attendues.In the recent decades, the raise of primary production (PP) in the Arctic Ocean (AO) is mainly driven by the increase of phytoplankton biomass as multiple remote sensing studies have suggested. Chlorophyll a concentration (Chl), a proxy of phytoplankton biomass, is a key factor that biases PP estimates. In terms of bottom-up control, accurate Chl estimate is crucial to improve our knowledge of marine ecosystem and its response to ongoing climate change. However, there are several challenges of ocean color remote sensing in the Arctic Ocean. Firstly, it is well known that the failure of standard ocean color algorithms in the AO is due to the interference of colored and detrital material (CDM) in the visible spectrum, but how and to what extend it will bias the estimation of Chl remains unknown. Besides, Chl as a key factor used to estimate PP, error propagation from Chl estimates to PP estimates needs to be assessed. The last but most important is that a robust algorithm with reasonable uncertainty, especially for the complex and productive coastal waters, is not yet available. To address these problems, in this study, we first compiled a large Arctic in situ bio-optical database, based on which we thoroughly evaluated presently available ocean color algorithms from a perspective of the impacts of CDM. We found that the higher the level of CDM relative to Chl in the water column, the larger it would bias Chl estimates. Sensitivity analysis of PP estimates on Chl showed that the error of Chl estimates was amplified within 7% when passed into the estimation of PP using a spectrally- and vertically-resolved PP model. Besides, to obtain better results, we tuned GSM for the AO by adding 620 waveband which is less affected by CDM and the signal here is generally high for CDM-rich waters thus become important for GSM to retrieve accurate Chl estimates. Our tuned algorithm, GSMA, merely improved the accuracy by 8% for the AO, but the improvement for coastal waters reached up to 93%. Finally, given that algorithms that only exploits visible spectrum are problematic for highly-absorbing/dark waters, we introduced the fluorescence emission model to account for the bio-optical properties of phytoplankton in the near infrared spectrum. By coupling with GSMA, the novel full-spectrally algorithm, FGSM, further improved the accuracy of Chl estimates by ~44% for eutrophic waters. In the future, matchups are needed for validation purposes with respect to satellite application. Moreover, new approaches that can be applied to detect subsurface chlorophyll maximum (SCM), ice-edge and/or under-ice blooms, phytoplankton functional types (PFT) and so on are expected

Advanced methods for earth observation data synergy for geophysical parameter retrieval

The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Western Spain. The method was customized to control the evolution of time lag during wetting and drying conditions. Climate factors in combination with crop growing seasons were studied to reveal SM-related processes. The heterogeneity of land use was analyzed using high-resolution images of NDVI from Sentinel-2 to provide information about the level of spatial representativity of SMOS data to each in-situ station. The comparison of long-term precipitation records and potential evapotranspiration allowed estimation of SM seasons describing different SM conditions depending on climate and soil properties. The second part of the thesis focuses on data-driven methods for sea ice segmentation and parameter retrieval. A Bayesian framework is employed to segment sets of multi-source satellite data. The Bayesian unsupervised learning algorithm allows to investigate the ‘hidden link’ between multiple data. The statistical properties are accounted for by a Gaussian Mixture Model, and the spatial interactions are reflected using Hidden Markov Random Fields. The algorithm segments spatial data into a number of classes, which are represented as a latent field in physical space and as clusters in feature space. In a first application, a two-step probabilistic approach based on Expectation-Maximization and the Bayesian segmentation algorithm was used to segment SAR images to discriminate surface water from sea ice types. Information on surface roughness is contained in the radar backscattering images which can be - in principle - used to detect melt ponds and to estimate high-resolution sea ice concentration (SIC). In a second study, the algorithm was applied to multi-incidence angle TB data from the SMOS L1C product to harness the its sensitivity to thin ice. The spatial patterns clearly discriminate well-determined areas of open water, old sea ice and a transition zone, which is sensitive to thin sea ice thickness (SIT) and SIC. In a third application, SMOS and the AMSR2 data are used to examine the joint effect of CIMR-like observations. The information contained in the low-frequency channels allows to reveal ranges of thin sea ice, and thicker ice can be determined from the relationship between the high-frequency channels and changing conditions as the sea ice ages. The proposed approach is suitable for merging large data sets and provides metrics for class analysis, and to make informed choices about integrating data from future missions into sea ice products. A regression neural network approach was investigated with the goal to infer SIT using TB data from the Flexible Microwave Payload 2 (FMPL-2) of the FSSCat mission. Two models - covering thin ice up to 0.6m and the full-range of SIT - were trained on Arctic data using ground truth data derived from the SMOS and Cryosat-2. This work demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.La primera parte de la tesis se centra en el análisis de los factores relevantes para estimar el tiempo de respuesta entre la humedad del suelo (SM) basada en el satélite y la in-situ, utilizando una deformación temporal dinámica (DTW). El DTW se aplicó al SMOS L4 SM, y se comparó con la SM in-situ en la red REMEDHUS en el oeste de España. El método se adaptó para controlar la evolución del desfase temporal durante diferentes condiciones de humedad y secado. Se estudiaron los factores climáticos en combinación con los períodos de crecimiento de los cultivos para revelar los procesos relacionados con la SM. La heterogeneidad del uso del suelo se analizó utilizando imágenes de alta resolución de NDVI de Sentinel-2 para proporcionar información sobre el nivel de representatividad espacial de los datos de SMOS a cada estación in situ. La comparación de los patrones de precipitación a largo plazo y la evapotranspiración potencial permitió estimar las estaciones de SM que describen diferentes condiciones de SM en función del clima y las propiedades del suelo. La segunda parte de esta tesis se centra en métodos dirigidos por datos para la segmentación del hielo marino y la obtención de parámetros. Se emplea un método de inferencia bayesiano para segmentar conjuntos de datos satelitales de múltiples fuentes. El algoritmo de aprendizaje bayesiano no supervisado permite investigar el “vínculo oculto” entre múltiples datos. Las propiedades estadísticas se contabilizan mediante un modelo de mezcla gaussiana, y las interacciones espaciales se reflejan mediante campos aleatorios ocultos de Markov. El algoritmo segmenta los datos espaciales en una serie de clases, que se representan como un campo latente en el espacio físico y como clústeres en el espacio de las variables. En una primera aplicación, se utilizó un enfoque probabilístico de dos pasos basado en la maximización de expectativas y el algoritmo de segmentación bayesiano para segmentar imágenes SAR con el objetivo de discriminar el agua superficial de los tipos de hielo marino. La información sobre la rugosidad de la superficie está contenida en las imágenes de backscattering del radar, que puede utilizarse -en principio- para detectar estanques de deshielo y estimar la concentración de hielo marino (SIC) de alta resolución. En un segundo estudio, el algoritmo se aplicó a los datos TB de múltiples ángulos de incidencia del producto SMOS L1C para aprovechar su sensibilidad al hielo fino. Los patrones espaciales discriminan claramente áreas bien determinadas de aguas abiertas, hielo marino viejo y una zona de transición, que es sensible al espesor del hielo marino fino (SIT) y al SIC. En una tercera aplicación, se utilizan los datos de SMOS y de AMSR2 para examinar el efecto conjunto de las observaciones tipo CIMR. La información contenida en los canales de baja frecuencia permite revelar rangos de hielo marino delgado, y el hielo más grueso puede determinarse a partir de la relación entre los canales de alta frecuencia y las condiciones cambiantes a medida que el hielo marino envejece. El enfoque propuesto es adecuado para fusionar grandes conjuntos de datos y proporciona métricas para el análisis de clases, y para tomar decisiones informadas sobre la integración de datos de futuras misiones en los productos de hielo marino. Se investigó un enfoque de red neuronal de regresión con el objetivo de inferir el SIT utilizando datos de TB de la carga útil de microondas flexible 2 (FMPL-2) de la misión FSSCat. Se entrenaron dos modelos - que cubren el hielo fino hasta 0.6 m y el rango completo del SIT - con datos del Ártico utilizando datos de “ground truth” derivados del SMOS y del Cryosat-2. Este trabajo demuestra que las misiones CubeSat de coste moderado pueden proporcionar datos valiosos para aplicaciones de observación de la Tierra.Postprint (published version

Using Machine Learning for Model Physics: an Overview

In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications of ML to emulate existing parameterizations, to develop new parameterizations, to ensure physical constraints, and control the accuracy of developed applications are described. Some ML approaches that allow developers to go beyond the standard parameterization paradigm are discussed.Comment: 50 pages, 3 figures, 1 tabl

Estimating the crop leaf area index using hyperspectral remote sensing

AbstractThe leaf area index (LAI) is an important vegetation parameter, which is used widely in many applications. Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies. During the last two decades, hyperspectral remote sensing has been employed increasingly for crop LAI estimation, which requires unique technical procedures compared with conventional multispectral data, such as denoising and dimension reduction. Thus, we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques. First, we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation. Second, we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types: approaches based on statistical models, physical models (i.e., canopy reflectance models), and hybrid inversions. We summarize and evaluate the theoretical basis and different methods employed by these approaches (e.g., the characteristic parameters of LAI, regression methods for constructing statistical predictive models, commonly applied physical models, and inversion strategies for physical models). Thus, numerous models and inversion strategies are organized in a clear conceptual framework. Moreover, we highlight the technical difficulties that may hinder crop LAI estimation, such as the “curse of dimensionality” and the ill-posed problem. Finally, we discuss the prospects for future research based on the previous studies described in this review