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Cirrus clouds are key modulators of Earth's climate. Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models. This work uses three years of satellite and reanalysis data to study the link between cirrus drivers and cloud properties. We use a gradient-boosted machine learning model and a Long Short-Term Memory (LSTM) network with an attention layer to predict the ice water content and ice crystal number concentration. The models show that meteorological and aerosol conditions can predict cirrus properties with $R^2 = 0.49$. Feature attributions are calculated with SHapley Additive exPlanations (SHAP) to quantify the link between meteorological and aerosol conditions and cirrus properties. For instance, the minimum concentration of supermicron-sized dust particles required to cause a decrease in ice crystal number concentration predictions is $2 \times 10^{-4}$ mg m\textsuperscript{-3}. The last 15 hours before the observation predict all cirrus properties., Comment: Presented at Climate Informatics 2023 in Cambridge; Submitted to Environmental Data Science Journal Updates Version: New version of dataset is linked. Please use that version: https://zenodo.org/record/7965381

Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires weeks and months in advance to plan forest fuel management, resource procurement and allocation. To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system's inherent spatio-temporal interactions, such as memory effects and teleconnections. We propose a teleconnection-driven vision transformer (TeleViT), capable of treating the Earth as one interconnected system, integrating fine-grained local-scale inputs with global-scale inputs, such as climate indices and coarse-grained global variables. Through comprehensive experimentation, we demonstrate the superiority of TeleViT in accurately predicting global burned area patterns for various forecasting windows, up to four months in advance. The gain is especially pronounced in larger forecasting windows, demonstrating the improved ability of deep learning models that exploit teleconnections to capture Earth system dynamics. Code available at https://github.com/Orion-Ai-Lab/TeleViT.

Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's eye view of the essential scientific tools and approaches informing and supporting the transition from raw EO data to usable EO-based information. The promises, as well as the current challenges of these developments, are highlighted under dedicated sections. Specifically, we cover the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and (viii) the much-needed discussion of ethical and societal issues related to the massive use of ML technologies in EO.

Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from another one. Granger causality has been widely used in many applied disciplines, from neuroscience and econometrics to Earth sciences. We revisit GC under a graphical perspective of state-space models. For that, we use GraphEM, a recently presented expectation-maximisation algorithm for estimating the linear matrix operator in the state equation of a linear-Gaussian state-space model. Lasso regularisation is included in the M-step, which is solved using a proximal splitting Douglas-Rachford algorithm. Experiments in toy examples and challenging climate problems illustrate the benefits of the proposed model and inference technique over standard Granger causality methods., Comment: 4 pages, 2 figures, 3 tables, CausalStats23: When Causal Inference meets Statistical Analysis, April 17-21, 2023, Paris, France

We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas for 17 years (2006-2022). It is designed as a cloud-friendly spatio-temporal dataset, namely a datacube, harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks. We extract two ML-ready datasets that establish distinct tracks to demonstrate this potential: (1) short-term wildfire danger forecasting and (2) final burned area estimation given the point of ignition. We define appropriate metrics and baselines to evaluate the performance of models in each track. By publishing the datacube, along with the code to create the ML datasets and models, we encourage the community to foster the implementation of additional tracks for mitigating the increasing threat of wildfires in the Mediterranean.

Cloud detection is a crucial step in the optical satellite image processing pipeline for Earth observation. Clouds in optical remote sensing images seriously affect the visibility of the background and greatly reduce the usability of images for land applications. Traditional methods based on thresholding, multi-temporal or multi-spectral information are often specific to a particular satellite sensor. Convolutional Neural Networks for cloud detection often require labeled cloud masks for training that are very time-consuming and expensive to obtain. To overcome these challenges, this paper presents a hybrid cloud detection method based on the synergistic combination of generative adversarial networks (GAN) and a physics-based cloud distortion model (CDM). The proposed weakly-supervised GAN-CDM method (available online https://github.com/Neooolee/GANCDM) only requires patch-level labels for training, and can produce cloud masks at pixel-level in both training and testing stages. GAN-CDM is trained on a new globally distributed Landsat 8 dataset (WHUL8-CDb, available online doi:https://doi.org/10.5281/zenodo.6420027) including image blocks and corresponding block-level labels. Experimental results show that the proposed GAN-CDM method trained on Landsat 8 image blocks achieves much higher cloud detection accuracy than baseline deep learning-based methods, not only in Landsat 8 images (L8 Biome dataset, 90.20% versus 72.09%) but also in Sentinel-2 images (“S2 Cloud Mask Catalogue” dataset, 92.54% versus 77.00%). This suggests that the proposed method provides accurate cloud detection in Landsat images, has good transferability to Sentinel-2 images, and can quickly be adapted for different optical satellite sensors.

Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, using historical Earth observation data from the last decade to predict next-day's fire danger. To that end, we collect, pre-process and harmonize an open-access datacube, featuring a set of covariates that jointly affect the fire occurrence and spread, such as weather conditions, satellite-derived products, topography features and variables related to human activity. We implement a variety of Deep Learning (DL) models to capture the spatial, temporal or spatio-temporal context and compare them against a Random Forest (RF) baseline. We find that either spatial or temporal context is enough to surpass the RF, while a ConvLSTM that exploits the spatio-temporal context performs best with a test Area Under the Receiver Operating Characteristic of 0.926. Our DL-based proof-of-concept provides national-scale daily fire danger maps at a much higher spatial resolution than existing operational solutions., Comment: Accepted to the workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

Global warming is leading to unprecedented changes in our planet, with great societal, economical and environmental implications, especially with the growing demand of biofuels and food. Assessing the impact of climate on vegetation is of pressing need. We approached the attribution problem with a novel nonlinear Granger causal (GC) methodology and used a large data archive of remote sensing satellite products, environmental and climatic variables spatio-temporally gridded over more than 30 years. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces, and use the covariance in Gaussian processes. The method generalizes the linear and kernel GC methods, and comes with tighter bounds of performance based on Rademacher complexity. Spatially-explicit global Granger footprints of precipitation and soil moisture on vegetation greenness are identified more sharply than previous GC methods., Comment: 7 pages, 3 figures, AI for Earth Sciences Workshop at NeurIPS 2020. arXiv admin note: text overlap with arXiv:2011.14444

Monitoring spatio-temporal changes in terrestrial gross primary productivity (GPP) of crops is key for estimating, understanding and predicting global carbon fluxes. Satellite remote sensing has been widely applied in the last decades to monitor agricultural resources, and the amount and quality of remote sensing data continuously increase. Since recently, and partly due the European Copernicus Programme, an unprecedented amount of open access data suitable for agriculture observations is now available. Benefiting from recent developments in satellite remote sensing technology, great advances in machine learning and advancements in our understanding of photosynthetic processes leading to increasingly complex and detailed photosynthesis models, we developed a hybrid approach to model GPP using satellite reflectance data by combining radiative transfer modeling and machine learning (ML). We have combined process-based model SCOPE with ML algorithms to estimate GPP of C3 crops using a variety of satellite data (Sentinel-2, Landsat and MODIS) and ancillary meteorological information. We link reflectance and meteorological data directly with crop GPP, bypassing the need of retrieving the set of input vegetation parameters needed to represent photosynthesis in an intermediate step, while still accounting for the complex processes of the original model. Several ML models, trained with the simulated data, were tested and validated using flux tower data. First, we tested our approach using Sentinel-2 data, which provide high frequency of observation, high spatial resolution of 20 m and multiple bands including red edge. Our final neural network model was able to estimate GPP at the tested flux towers with r2 of 0.92 and RMSE of 1.38 gC m$^{-2}$ d-1. Our model successfully estimated GPP across a variety of C3 crop types and environmental conditions, including periods of no vegetation, even tough it did not use any additional local information from the site. Since our learning approach is fast and efficient in the test phase and, at the same time, is based on a process-based model (and not on local empirical relationships), it can be applied globally. Furthermore, the simulated training dataset can be easily adapted to band settings of different instruments, assuring thus consistency among many sensors. However, such a global application requires high computational power and therefore we applied our approach to Landsat and MODIS data using Google Earth Engine (GEE) platform that provides cloud computing resources for processing large geospatial datasets. The results were validated using the FLUXNET2015 Dataset.

med 2018, 11-12 December 2018, Frascati, Rome, Italy, Dimensional reduction methods are key to Earth system sciences, since they allow dealing with extensive spatio-temporal climatic data sets in terms of individual variables as time series and spatial distributions. Principal Component Analysis (PCA), also called Empirical Orthogonal Functions (EOF) in geophysics, is a simple yet very powerful method to uncover the main geographical patterns at distinct temporal scales present in the data. A variety of PCA extensions have been proposed to deal with specific aspects of the input data, as the complex PCA (CEOF), the rotated PCA (REOF) or the nonlinear or Kernel PCA. The rotated complex kernel PCA (ROCK PCA) was recently proposed to combine these three methods and allow making a physically interpretable spatio-temporal decomposition of extensive climatic variables. In this work, we apply the ROCK-PCA method to the 8 years of ESA's SMOS Soil Moisture (SM) and Ocean Salinity (OS) data available over the Mediterranean region. Results show the distribution of the total SM and OS variance among its different components, and indicate the dominant modes of temporal variability of these two variables. The connection of these modes to human-induced changes or natural fluctuations (e.g. El Niño Southern Oscillation) have been explored and will be shown at the workshop, Research funded by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL project (grant agreement 647423) and TEC2016-81900-REDT. M. Piles is supported by a Ramón y Cajal contract (MINECO)

New social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently, many governments and institutions have raised concerns about the lack of fairness, equity and ethics in machine learning to treat these problems. It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included. Instead, including fairness in the objective function has been shown to be more efficient. We present novel fair regression and dimensionality reduction methods built on a previously proposed fair classification framework. Both methods rely on using the Hilbert Schmidt independence criterion as the fairness term. Unlike previous approaches, this allows us to simplify the problem and to use multiple sensitive variables simultaneously. Replacing the linear formulation by kernel functions allows the methods to deal with nonlinear problems. For both linear and nonlinear formulations the solution reduces to solving simple matrix inversions or generalized eigenvalue problems. This simplifies the evaluation of the solutions for different trade-off values between the predictive error and fairness terms. We illustrate the usefulness of the proposed methods in toy examples, and evaluate their performance on real world datasets to predict income using gender and/or race discrimination as sensitive variables, and contraceptive method prediction under demographic and socio-economic sensitive descriptors., Comment: Work published on ECML'17, http://ecmlpkdd2017.ijs.si/papers/paperID275.pdf

Kernel dependence measures yield accurate estimates of nonlinear relations between random variables, and they are also endorsed with solid theoretical properties and convergence rates. Besides, the empirical estimates are easy to compute in closed form just involving linear algebra operations. However, they are hampered by two important problems: the high computational cost involved, as two kernel matrices of the sample size have to be computed and stored, and the interpretability of the measure, which remains hidden behind the implicit feature map. We here address these two issues. We introduce the Sensitivity Maps (SMs) for the Hilbert-Schmidt independence criterion (HSIC). Sensitivity maps allow us to explicitly analyze and visualize the relative relevance of both examples and features on the dependence measure. We also present the randomized HSIC (RHSIC) and its corresponding sensitivity maps to cope with large scale problems. We build upon the framework of random features and the Bochner's theorem to approximate the involved kernels in the canonical HSIC. The power of the RHSIC measure scales favourably with the number of samples, and it approximates HSIC and the sensitivity maps efficiently. Convergence bounds of both the measure and the sensitivity map are also provided. Our proposal is illustrated in synthetic examples, and challenging real problems of dependence estimation, feature selection, and causal inference from empirical data.

Gaussian processes (GPs) have experienced tremendous success in biogeophysical parameter retrieval in the last few years. GPs constitute a solid Bayesian framework to consistently formulate many function approximation problems. This article reviews the main theoretical GP developments in the field, considering new algorithms that respect signal and noise characteristics, extract knowledge via automatic relevance kernels to yield feature rankings automatically, and allow applicability of associated uncertainty intervals to transport GP models in space and time that can be used to uncover causal relations between variables and can encode physically meaningful prior knowledge via radiative transfer model (RTM) emulation. The important issue of computational efficiency will also be addressed. These developments are illustrated in the field of geosciences and remote sensing at local and global scales through a set of illustrative examples. In particular, important problems for land, ocean, and atmosphere monitoring are considered, from accurately estimating oceanic chlorophyll content and pigments to retrieving vegetation properties from multi- and hyperspectral sensors as well as estimating atmospheric parameters (e.g., temperature, moisture, and ozone) from infrared sounders.

Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research.

Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Recent advances in machine learning have introduced novel algorithms for estimating otherwise intractable likelihood functions using a likelihood ratio trick based on binary classifiers. Consequently, efficient likelihood approximations can be obtained whenever good probabilistic classifiers can be constructed. We propose a kernel classifier for sequential data using path signatures based on the recently introduced signature kernel. We demonstrate that the representative power of signatures yields a highly performant classifier, even in the crucially important case where sample numbers are low. In such scenarios, our approach can outperform sophisticated neural networks for common posterior inference tasks., Comment: Accepted for publication at AISTATS 2022

Earth observation through satellite sensors, models and in situ measurements provides a way to monitor our planet with unprecedented spatial and temporal resolution. The amount and diversity of the data which is recorded and made available is ever-increasing. This data allows us to perform crop yield prediction, track land-use change such as deforestation, monitor and respond to natural disasters and predict and mitigate climate change. The last two decades have seen a large increase in the application of machine learning algorithms in Earth observation in order to make efficient use of the growing data-stream. Machine learning algorithms, however, are typically model agnostic and too flexible and so end up not respecting fundamental laws of physics. On the other hand there has, in recent years, been an increase in research attempting to embed physics knowledge in machine learning algorithms in order to obtain interpretable and physically meaningful solutions. The main objective of this thesis is to explore different ways of encoding physical knowledge to provide machine learning methods tailored for specific problems in remote sensing.Ways of expressing expert knowledge about the relevant physical systems in remote sensing abound, ranging from simple relations between reflectance indices and biophysical parameters to complex models that compute the radiative transfer of electromagnetic radiation through our atmosphere, and differential equations that explain the dynamics of key parameters. This thesis focuses on inversion problems, emulation of radiative transfer models, and incorporation of the above-mentioned domain knowledge in machine learning algorithms for remote sensing applications. We explore new methods that can optimally model simulated and in-situ data jointly, incorporate differential equations in machine learning algorithms, handle more complex inversion problems and large-scale data, obtain accurate and computationally efficient emulators that are consistent with physical models, and that efficiently perform approximate Bayesian inversion over radiative transfer models.

In Bayesian Optimization (BO) we study black-box function optimization with noisy point evaluations and Bayesian priors. Convergence of BO can be greatly sped up by batching, where multiple evaluations of the black-box function are performed in a single round. The main difficulty in this setting is to propose at the same time diverse and informative batches of evaluation points. In this work, we introduce DPP-Batch Bayesian Optimization (DPP-BBO), a universal framework for inducing batch diversity in sampling based BO by leveraging the repulsive properties of Determinantal Point Processes (DPP) to naturally diversify the batch sampling procedure. We illustrate this framework by formulating DPP-Thompson Sampling (DPP-TS) as a variant of the popular Thompson Sampling (TS) algorithm and introducing a Markov Chain Monte Carlo procedure to sample from it. We then prove novel Bayesian simple regret bounds for both classical batched TS as well as our counterpart DPP-TS, with the latter bound being tighter. Our real-world, as well as synthetic, experiments demonstrate improved performance of DPP-BBO over classical batching methods with Gaussian process and Cox process models., Comment: To be published in AISTATS 2022

We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization of captured events, search for the maximum of the intensity function and learning level sets of the intensity function. We model the intensity function as a sample from a truncated Gaussian process, represented in a specially constructed positive basis. In this basis, the positivity constraint on the intensity function has a simple form. We show how the minimal description positive basis can be adapted to the covariance kernel, to non-stationarity and make connections to common positive bases from prior works. Our adaptive sensing algorithms use Langevin dynamics and are based on posterior sampling (Cox-TnomPsoN) and top-two posterior sampling (ToP2) principles. With latter, the difference between samples serves as a surrogate to the uncertainty. We demonstrate the approach using examples from environmental monitoring and crime rate modeling, and compare it to the classical Bayesian experimental design approach. ISSN:2640-3498

In times of global change, we must closely monitor the state of our planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems-i.e. the biosphere, atmosphere, hydrosphere, cryosphere, and anthroposphere-can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region, e.g. the Multivariate ENSO (El Ñino-Southern Oscillation) Index. Indicator approaches have been used extensively to describe socioeconomic data too, and a range of indices have been proposed to synthesize and interpret this information. For instance the "Human Development Index" (HDI) by the United Nations Development Programme was designed to capture specific aspects of development. "Dimensionality reduction" (DR) is a widely used approach to find low dimensional and interpretable representations of data that are natively embedded in high-dimensional spaces. Here, we propose a robust method to create indicators using dimensionality reduction to better represent the terrestrial biosphere and the global socioeconomic system. We aim to explore the performance of the approach and to interpret the resulting indicators. For biosphere indicators, the concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73% of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We also apply the method on the "World Development Indicators", a database with more than 1500 variables, to track the socioeconomic development at a country level. The aim was to extract the core dimensions of development in a highly efficient way, using a method of nonlinear dimensionality reduction. We find that over 90% of variance in the WDIs can be represented by five uncorrelated and nonlinear dimensions. The first dimension (explaining 74%) represents the state of education, health, income, infrastructure, trade, population, and pollution. The second dimension (explaining 10%) differentiates countries by gender ratios, labor market, and energy production patterns. Overall, we find that the data contained in the WDIs are highly nonlinear therefore requiring nonlinear methods to extract the main patterns of development. Globally, most countries show rather consistent temporal trends towards wealthier and aging societies. Deviations from the long-term trajectories are detected with our approach during warfare, environmental disasters, or fundamental political changes. In general we find that the indicator approach is able to extract general patterns from complex databases and that it can be applied to databases of varying characteristics. We also find that indicators are can different kinds of changes occurring in the system, such as extreme events, permanent changes or trends. Therefore it is a useful tool for general monitoring and exploratory data analysis. The approach is flexible and can be applied to complex datasets, such as large data, nonlinear data, as well as data with many missing values. In times of global change, we must closely monitor the state of our planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems-i.e. the biosphere, atmosphere, hydrosphere, cryosphere, and anthroposphere-can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region, e.g. the Multivariate ENSO (El Ñino-Southern Oscillation) Index. Indicator approaches have been used extensively to describe socioeconomic data too, and a range of indices have been proposed to synthesize and interpret this information. For instance the "Human Development Index" (HDI) by the United Nations Development Programme was designed to capture specific aspects of development. "Dimensionality reduction" (DR) is a widely used approach to find low dimensional and interpretable representations of data that are natively embedded in high-dimensional spaces. Here, we propose a robust method to create indicators using dimensionality reduction to better represent the terrestrial biosphere and the global socioeconomic system. We aim to explore the performance of the approach and to interpret the resulting indicators. For biosphere indicators, the concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73% of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We also apply the method on the "World Development Indicators", a database with more than 1500 variables, to track the socioeconomic development at a country level. The aim was to extract the core dimensions of development in a highly efficient way, using a method of nonlinear dimensionality reduction. We find that over 90% of variance in the WDIs can be represented by five uncorrelated and nonlinear dimensions. The first dimension (explaining 74%) represents the state of education, health, income, infrastructure, trade, population, and pollution. The second dimension (explaining 10%) differentiates countries by gender ratios, labor market, and energy production patterns. Overall, we find that the data contained in the WDIs are highly nonlinear therefore requiring nonlinear methods to extract the main patterns of development. Globally, most countries show rather consistent temporal trends towards wealthier and aging societies. Deviations from the long-term trajectories are detected with our approach during warfare, environmental disasters, or fundamental political changes. In general we find that the indicator approach is able to extract general patterns from complex databases and that it can be applied to databases of varying characteristics. We also find that indicators are can different kinds of changes occurring in the system, such as extreme events, permanent changes or trends. Therefore it is a useful tool for general monitoring and exploratory data analysis. The approach is flexible and can be applied to complex datasets, such as large data, nonlinear data, as well as data with many missing values.

The terrestrial biosphere is currently slowing down global warming by absorbing about 30% of human emissions of carbon dioxide (CO2). The largest flux of the terrestrial carbon uptake is gross primary production (GPP) defined as the production of carbohydrates by photosynthesis. Elevated atmospheric CO2 concentration is expected to increase GPP (“CO2 fertilization effect”). However, Earth system models (ESMs) exhibit a large range in simulated GPP projections. In this study, we combine an existing emergent constraint on CO2 fertilization with a machine learning approach to constrain the spatial variations of multimodel GPP projections. In a first step, we use observed changes in the CO2 seasonal cycle at Cape Kumukahi to constrain the global mean GPP at the end of the 21st century (2091–2100) in Representative Concentration Pathway 8.5 simulations with ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 171 ± 12 Gt C yr−1, compared to the unconstrained model range of 156–247 Gt C yr−1. In a second step, we use a machine learning model to constrain gridded future absolute GPP and gridded fractional GPP change in two independent approaches. For this, observational data are fed into the machine learning algorithm that has been trained on CMIP5 data to learn relationships between present‐day physically relevant diagnostics and the target variable. In a leave‐one‐model‐out cross‐validation approach, the machine learning model shows superior performance to the CMIP5 ensemble mean. Our approach predicts an increased GPP change in northern high latitudes compared to regions closer to the equator., Plain Language Summary: About a quarter of human emissions of carbon dioxide (CO2) is absorbed by vegetation and soil on the Earth's surface and hence does not contribute to global warming caused by CO2 in the atmosphere. Thus, in order to better define the remaining carbon budgets left to meet particular warming targets like the 1.5°C of the Paris Agreement, it is important to accurately quantify the carbon uptake by plants in the future. Currently, this is modeled by Earth system models yet with great uncertainties. In this work, we present an alternative machine learning approach to reduce spatial uncertainties of vegetation carbon uptake in future climate projections using observations of today's conditions., Key Points: An emergent constraint on CO2 seasonal cycle amplitude changes reduces uncertainties in global mean gross primary production projections. A machine learning model with multiple predictors can further constrain the spatial distribution of gross primary production. High‐latitude ecosystems show higher gross primary production increase over the 21st century compared to regions closer to the equator., EC | Horizon 2020 Framework Programme 4C, EC | Horizon 2020 Framework Programme CRESCENDO, ERC Consolidator Grant SEDAL, ERC Synergy Grant USMILE

L’observació de la Terra a partir de les dades proporcionades per sensors abord de satèl·lits, així com les proporcionades per models de transferència radiativa o climàtics, juntament amb les mesures in situ proporcionen una manera sense precedents de monitorar el nostre planeta amb millors resolucions espacials i temporals. La riquesa, quantitat i diversitat de les dades adquirides i posades a disposició també augmenta molt ràpidament. Aquestes dades ens permeten predir el rendiment dels cultius, fer un seguiment del canvi d’ús del sòl com ara la desforestació, supervisar i respondre als desastres naturals, i predir i mitigar el canvi climàtic. Per tal de fer front a tots aquests reptes, les dues darreres dècades han evidenciat un gran augment en l'aplicació d'algorismes d'aprenentatge automàtic en l'observació de la Terra. Amb l'anomenat `machine learning' es pot fer un ús eficient del flux de dades creixent en quantitat i diversitat. Els algorismes d'aprenentatge màquina, però, solen ser models agnòstics i massa flexibles i, per tant, acaben per no respectar les lleis fonamentals de la física. D’altra banda, en els darrers anys s’ha produït un augment de la investigació que intenta integrar el coneixement de física en algorismes d’aprenentatge, amb la finalitat d’obtenir solucions interpretables i que tinguin sentit físic. L’objectiu principal d’aquesta tesi és dissenyar diferents maneres de codificar el coneixement físic per proporcionar mètodes d’aprenentatge automàtic adaptats a problemes específics en teledetecció. Introduïm nous mètodes que poden fusionar de manera òptima fonts de dades heterogènies, explotar les regularitats de dades, incorporar equacions diferencials, obtenir models precisos que emulen, i per tant són coherents amb models físics, i models que aprenen parametrizacions del sistema combinant models i simulacions. Earth observation through satellite sensors, models and in situ measurements provides a way to monitor our planet with unprecedented spatial and temporal resolution. The amount and diversity of the data which is recorded and made available is ever-increasing. This data allows us to perform crop yield prediction, track land-use change such as deforestation, monitor and respond to natural disasters and predict and mitigate climate change. The last two decades have seen a large increase in the application of machine learning algorithms in Earth observation in order to make efficient use of the growing data-stream. Machine learning algorithms, however, are typically model agnostic and too flexible and so end up not respecting fundamental laws of physics. On the other hand there has, in recent years, been an increase in research attempting to embed physics knowledge in machine learning algorithms in order to obtain interpretable and physically meaningful solutions. The main objective of this thesis is to explore different ways of encoding physical knowledge to provide machine learning methods tailored for specific problems in remote sensing. Ways of expressing expert knowledge about the relevant physical systems in remote sensing abound, ranging from simple relations between reflectance indices and biophysical parameters to complex models that compute the radiative transfer of electromagnetic radiation through our atmosphere, and differential equations that explain the dynamics of key parameters. This thesis focuses on inversion problems, emulation of radiative transfer models, and incorporation of the abovementioned domain knowledge in machine learning algorithms for remote sensing applications. We explore new methods that can optimally model simulated and in-situ data jointly, incorporate differential equations in machine learning algorithms, handle more complex inversion problems and large-scale data, obtain accurate and computationally efficient emulators that are consistent with physical models, and that efficiently perform approximate Bayesian inversion over radiative transfer models.

[ES] Reseña de tesis doctoral defendida el 17 de Julio de 2017. Lugar: Facultat de Física, Universitat de València., The research leading to these results was conducted within the ERMES FP7 project (http://www. ermes-fp7space.eu/) which received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement 606983. This work was also supported by the European Research Council (ERC) funding under the ERC Consolidator Grant 2014 SEDAL (Statistical Learning for Earth Observation Data Analysis) project under Grant Agreement 647423 and by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) through the LSA SAF (Land Surface Analysis Satellite Applications Facility) project.

When applied to new datasets, acquired at different time moments, with different sensors or under different acquisition conditions, deep learning models might fail spectacularly. This is because they have learned from the data distribution observed during training and, as such, do not generalize out of that domain naturally. This chapter introduces methodologies designed to tackle this problem and provide deep learning models able to adapt to new data distributions, i.e. domain adaptation. Domain adaptation works by either adapting the representation to the new data distribution, modifying the inputs or performing smart sampling. But independently of the strategy, they lead to updated models, able to process effectively the new data without needing observation from it (or a very limited amount).