BG Biogeosciences BG Biogeosciences 1726-4189 Copernicus Publications Göttingen, Germany 10.5194/bg-14-3401-2017Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems ScholzeMarko marko.scholze@nateko.lu.se https://orcid.org/0000-0002-3474-5938 BuchwitzMichael https://orcid.org/0000-0001-7616-1837 DorigoWouter https://orcid.org/0000-0001-8054-7572 GuanterLuis QueganShaun Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany Department of Geodesy and Geoinformation, Vienna University of Technology (TU Wien), Vienna, Austria Remote Sensing Section, German Research Center for Geosciences (GFZ), 14473 Potsdam, Germany Centre for Terrestrial Carbon Dynamics, The University of Sheffield, Sheffield S3 7RH, UK Marko Scholze (marko.scholze@nateko.lu.se)19July2017 14 14 34013429 22December2016 12January2017 19June2017 20June2017 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://bg.copernicus.org/articles/14/3401/2017/bg-14-3401-2017.html The full text article is available as a PDF file from https://bg.copernicus.org/articles/14/3401/2017/bg-14-3401-2017.pdf

The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).

We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by , emphasising the rapid advance in relevant space-based observations.

 Introduction

The anthropogenic perturbation of the global carbon cycle has led to a global mean increase of 43 % in atmospheric CO2 (from 280 to 398 ppm) in 2014 compared to pre-industrial (before 1750) levels and is the main driver of climate change. The main causes for the increase in CO2 are burning of fossil fuels and land use change, which amount to emissions of 9.8±0.5 GtC in 2014. However, only about 44 % of these emissions stay in the atmosphere; the remainder is currently taken up by the land biosphere (≈ 30 %) and the surface ocean (≈ 26 %; ). Positive climate-carbon cycle feedbacks, predominantly acting on land processes, may reduce this sink capacity and thus accelerate global warming . Also, the sink strength of the terrestrial biosphere is more variable than that of the ocean and its quantification by process-based terrestrial carbon cycle models exhibit large uncertainties .

A common way to reduce uncertainties from process-based modelling is by confronting these models with observational data. pointed out that the systematic combination of observational data with process modelling, which is commonly referred to as “model-data fusion”, is an effective strategy for observing the Earth system. The term model-data fusion is sometimes understood in a more general way, which is that observational data is blended with (pre-computed) model output, whereas the term “data assimilation” refers to a robust mathematical framework for improving model predictions with observational data. Data assimilation is motivated by several benefits to make the best use of observations and models . These benefits include, among others, (1) forecasting and initialisation (forward predictions in time based on past observations), (2) model and data quality control (regular and systematic confrontation of model output with observations within their uncertainty statistics), (3) a combination of various data streams (combined constraints of independent observations can be stronger than the sum of the individual constraints), (4) filling in regions with sparse observations (consistent propagation of information from data-rich regions to data-poor regions), (5) estimating unobservable quantities (through process-based relations in the model observations constrain modelled quantities which are not directly measured) and (6) observing system design (what is the delta of a new type of observation).

Systematic observations are a key ingredient for data assimilation studies. Here, we focus on the carbon cycle and the land–atmosphere system. The land–atmosphere components of the carbon cycle are an important part of an integrated Earth observation system because of the close interactions on land between the carbon cycle and the water and energy cycles and hence its importance for climate projections and climate change mitigation strategies through the monitoring and management of terrestrial greenhouse gas sources and sinks.

provide an analysis of the various elements of a Terrestrial Carbon Observation System (TCOS). The need for, design and steps to be taken towards a TCOS were already outlined by others before but systematically reviewed two major components of a TCOS: the data assimilation methods and the observational data and data uncertainty characteristics for some selected, main kinds of relevant data. The requirements for a policy-relevant carbon observing system have been outlined by . They review the current systematic carbon cycle observations and illustrate the implementation of such a policy-relevant carbon observing system.

In this paper we provide an update of the observational data and data uncertainty characteristics as assessed by with a focus on existing but also new and upcoming, relevant space-based observations, in the following referred to as Earth observation (EO) data. In contrast to , who focus on carbon cycle observations, we focus here on relevant observational data to be (potentially) assimilated in a terrestrial carbon cycle data assimilation system (CCDAS).

In a CCDAS non-carbon observations can be exploited to constrain the simulated carbon cycle indirectly through the relations implemented in the process model. Such observational constraints act by ruling out combinations of the unknowns in a CCDAS (typically a combination of process parameters, initial- or boundary conditions), which are inconsistent with the observations and thereby reduce uncertainties in the simulated output. In that sense we are somewhat broader in terms of observed variables because the “non-carbon” observations (such as soil moisture or land surface temperature) are also able to constrain the carbon cycle indirectly through process information embedded in the underlying models. At the same time, the focus of our review is narrower than that of Ciais et al. (2014), who also addressed ocean and anthropogenic components.

The paper is organised as follows: in the next section we contrast data assimilation with recently established benchmarking activities and give a brief overview of commonly used data assimilation approaches and their applications in terrestrial carbon cycling. We continue with a short overview on data characteristics including an update on progress for some of the observations discussed in . Since there has been much developments in the provision of remotely sensed observations, we focus here on the characteristics of the most relevant EO data streams.

 Data assimilation Data assimilation versus benchmarking

In the recent past the international land surface and terrestrial ecosystem modelling communities have recognised the importance of model benchmarking and evaluation e.g.. One of the reasons for this development is the huge range of model results from different models in key diagnostics of the land–atmosphere interface such as gross primary productivity (GPP) and latent heat flux .

In general “benchmarking” is understood as the quantification of performance against a reference using some pre-defined metrics. The reference can either be output from some previous model simulations, other (ensembles of) models or reference data sets based on observations if the model simulates the analogue quantity. suggest a theoretical framework for benchmarking land models based on standardised references and metrics to measure model performance skills. A large variety of such metrics and their characteristics is introduced by . Some examples of benchmarking terrestrial carbon cycle models (either stand-alone or coupled to climate models) are given, for example, by , and .

The commonality between benchmarking/evaluation and data assimilation lies in the quantitative assessment of model output. In benchmarking the quantitative assessment is performed by calculating some metrics against either observations or other references, while in data assimilation this is achieved by defining a cost function, which quantifies the mismatch of some simulated model quantity against observations weighted by the inverse of their uncertainties (including a model uncertainty). However, data assimilation goes beyond benchmarking as it minimises the quantified mismatch to improve model performance directly by adjusting initial and boundary conditions, state variables and/or model process parameters.

As pointed out by there is a need for both model benchmarking and data assimilation: benchmarking may be used as a routine application to improve confidence and evaluate the performance (over time) in terrestrial carbon cycle modelling. However, if a benchmark test for a given model fails, this could simply imply that the model parameter values have not been specified correctly and optimised against observations. In contrast, data assimilation, in particular when used for parameter optimisation, potentially identifies structural model and/or data deficiencies if the model-data mismatch (or the benchmark test) is still inadequate after optimisation (see also Fig. ). On the other hand, a better fit between the posterior maximum likelihood simulation (i.e. using the optimised parameters) and the observations is not necessarily an indication for correct parameters and/or model structure as has been pointed out by .

 Data assimilation methods

The general problem of data assimilation can be formulated (following the notation of ) as follows: given a model M, a set of observations y of some observables o=H(z), with z being the state variables of the model, H the observation operator and prior information on some target variables x, produce an updated description of x. x may include elements of z and p (parameters; quantities not changed by the model, i.e. process parameters, boundary and initial conditions). The observation operator maps the model state onto observables. In the case of a CCDAS-assimilating atmospheric CO2 the observation operator is the atmospheric transport model that maps the net CO2 surface exchange fluxes as calculated by the terrestrial carbon cycle onto simulated atmospheric CO2 concentrations.

Schematic of a data assimilation system with x being the control vector containing the quantities to be updated by the assimilation. The loop between the “evaluation of J” box to the “model and observation operator” box indicates the assimilation process (assimilation loop). Often, the analysis of residuals in model-data comparison leads to either model improvements or adjustment of the measurement strategies (“model improvement” and “adjusting measurement strategy” arrows).

A data assimilation system consists of three main ingredients: a set of observations, a dynamical model including the observation operator and an assimilation method. When assimilating multiple data streams, each data stream usually requires its own observation operator see e.g.. In the Bayesian formulation of the assimilation problem uncertainties (i.e. the description of quantities by probability density functions, PDFs) are central to the concept of data assimilation. Both observations as well as models have errors arising for various reasons. We will detail the observational errors in the next section. Dynamical models as well as observation operators have errors arising from the parameterisations and the discretisation of analytical dynamics into a numerical model; for a more complete description of uncertainty in Earth system models or components of such we refer to .

We distinguish two basic approaches in data assimilation: sequential assimilation, which assimilates observations subsequently at discrete model time steps, and variational assimilation, which assimilates all observations at once at their respective measurement times over a given period, the so-called assimilation window. They differ in their numerical efficiency and adequacy for their specific use. A general data-assimilation scheme is shown in Fig. . In the sequential approach the assimilation loop is evaluated sequentially over time following the dynamics of the model. In the case of variational assimilation the assimilation loop is evaluated iteratively (assuming a non-linear model). Both cases evaluate a cost function J, formulated in the Bayesian framework as follows: J=12(x-xb)TB-1(x-xb)+(H(x)-y)TR-1(H(x)-y), where xb is the prior information, B the prior uncertainty covariance and R the observational uncertainty covariance. When multiple data streams with different observation operators are assimilated, there will be several summands of the form of the second term on the right hand side of Eq. (), one for each data stream.

introduce the theory fundamental to data assimilation and illustrate how the different implementations of data assimilation relate to this theory in a more narrative style. A more complete and mathematically precise introduction to the concepts of data assimilation is given in the textbooks by e.g.  and .

 Examples of terrestrial carbon cycle data assimilation

A variety of the methods as described by have been applied by the carbon cycle community. One example that is making use of formal assimilation methodologies for inferring surface-atmosphere CO2 exchange fluxes is based on atmospheric transport inversions. As mentioned before, in atmospheric inversions the observation model is an atmospheric tracer transport model. In atmospheric inversions both sequential and variational methods have been used together with observations of atmospheric trace gas concentrations such as from the flask sampling network, continuous in situ and aircraft measurements and more recently also remotely sensed total column measurements. The techniques for atmospheric transport inversions have been detailed in and a recent comparison of results from different transport inversion is given by .

A more recent development is the assimilation of observations into terrestrial biosphere models. Here, various methods and observations have been used to optimise model process parameters at different scales. A comparison of a whole suite of these assimilation methods applied to a test case using a simplified model at local scale is given by and .

were among the first who applied a formal algorithm together with observations of atmospheric CO2 concentrations to constrain the simple diagnostic biosphere model at global scale. This work was continued by the development of the first carbon cycle data assimilation system (CCDAS) with a process-based model (BETHY) at its core . The advantage of using a process-based model at the core of a CCDAS is that once the process parameters have been optimised the constrained model can also be used for predictions as demonstrated by . Also, such systems are capable of ingesting multiple independent data streams besides atmospheric CO2 concentrations. provide an overview on the developments of the CCDAS-BETHY since its first application while demonstrate the latest application of CCDAS-BETHY, assimilating atmospheric CO2 and remotely sensed surface soil moisture simultaneously. Since then several global terrestrial ecosystem models have been included in CCDAS employing a variational approach e.g..

Concurrently, there have been several studies at the local/regional scale assimilating various types of observations. For instance, used a genetic algorithm to infer soil carbon turnover times in a terrestrial carbon cycle model over Australia from in situ observations of plant production, biomass, litter and soil carbon. Local eddy covariance flux tower measurements of net exchange of CO2 and latent and sensible heat fluxes have been assimilated to optimise parameters related to photosynthesis, respiration and energy fluxes of terrestrial ecosystem models using Monte-Carlo-type methods e.g., sequential methods as well as variational approaches e.g.

Recent advances focus on multiple independent data stream assimilation to provide a more rigorous constraint on the multiple components of terrestrial ecosystem models and avoid equifinality, i.e. different parameter solutions providing the same cost function value at the minimum. Examples for such studies on local/regional scale are the assimilation of eddy covariance CO2 fluxes together with observations of vegetation structural information or carbon stocks e.g.. The assimilation of multiple data streams can be performed either in a stepwise e.g. or simultaneous approach e.g.; in the case of non-linear models or non-linear observation operators only the simultaneous assimilation makes optimal use of the observations . In Sect.  we provide more terrestrial carbon cycle data assimilation examples using some of the remotely sensed products discussed in the following.

Space–time diagram for a range of observations relevant for a Terrestrial Carbon Observation System.

 Data characteristics and provision

Observations are our measurable representation of the “truth”. They come with different characteristics in terms of spatial and temporal resolution, coverage of the observed system and errors. In analogy, models are also some representation of the truth, but via knowledge embodied in some form of functional relationships (with their own errors as mentioned before). The paper by has been instrumental in highlighting the challenges in combining models and observational data for building a TCOS focusing on the observational requirements. argue for a globally integrated carbon observation system to improve our understanding of the carbon cycle for predicting future changes and to be able to independently verify the impact of emission reduction measures. Such a system relies on atmospheric carbon observations as a backbone but also concerns observations of the terrestrial and ocean carbon cycle. They focus on a strategy towards a global carbon cycle monitoring system for achieving the above mentioned objectives.

Figure  depicts exemplarily the main observations of a TCOS and their space–time characteristics. In the following we briefly summarise the aspects of uncertainty in the observations and highlight progress on the specification of uncertainty for some of the observations in Fig.  as well as on their monitoring since .

Observational uncertainty

As mentioned before an important ingredient to any data assimilation system is not only the observations themselves but also the uncertainties associated to them. We distinguish three main types of observation errors:

Random errors are always present in measurements and are caused by unpredictable changes in the measurement system (e.g. electronic noise in electrical instrument). They show up as different readings of the same repeated measurement and thus can be reduced by taking the average of multiple measurements. Random errors are usually assumed to be normal (Gaussian) distributed, however, in some cases the random error distribution is log-normal (e.g. precipitation) or skewed by outliers due to unpredictable corruptions of the measurement system. Random errors are therefore related to the precision of a measurement system.

Systematic (bias) errors in observations are usually due to some recurring problems in the overall measurement system. They are caused by instrument miscalibrations or interferences with the measurement system. They can vary in space and time but they affect the measurement system in a predictable way. Biases can be both additive (absolute mean bias) and multiplicative (biases in the dynamic range affecting the amplitude of a signal). If the source for systematic errors is known they can usually be fixed and should be removed. Systematic errors are therefore related to the accuracy of a measurement system.

Representativeness error occurs when information is represented at a scale different from the source of the information. For instance a quantity simulated by a model is representative for a given spatial and temporal resolution of the model grid. In fact, the scale at which we trust the model may be larger than a grid cell. An individual measurement, however, represents information influenced by the local environment that is not resolved by the model grid (e.g. representation of atmospheric flask data in an atmospheric transport model grid cell).

For both random and systematic errors not only the magnitude of the error for a single observation is important, i.e. the diagonal elements in the observational uncertainty covariance matrix B, but also the correlations between errors for different observations. Hence there is a need to specify the off-diagonal elements in the error covariance matrix B. These off-diagonal elements are usually hard to specify, but it is important to quantify them in a data assimilation system. They have considerable impact on the solution because of their influence on the weight of the respective observations in the cost function.

In addition to the observational errors, models have errors, which, in a data assimilation system, are usually included in the observation errors. These errors in dynamical models are mainly caused by process parameterisations (instead of resolving the process) and by the discretisation of analytical dynamics into a numerical model. A more detailed description of the different model error sources is given in .

As mentioned before, have already reflected on the main properties of the data and their error covariances for observations of remotely sensed land surface properties (mainly the normalised differential vegetation index, NDVI), atmospheric CO2 concentrations, land–atmosphere net CO2 exchange fluxes and terrestrial carbon stores. The in situ measurements of CO2 concentrations are either based on flask samples or on continuous monitoring stations. The flask sampling network was established in 1961 by and has been extended since then to more than 200 sites globally. The continuous in situ network provide measurements at higher precision and temporal resolution than the flask networks. For both the flask and the continuous stations, improvements in precision and accuracy have been achieved through propagation of frequent comparisons and international standards .

The global FluxNet network consists of more than 200 sites globally measuring land–atmosphere fluxes of CO2, latent and sensible heat and others using the eddy covariance technique at a half-hourly temporal resolution . Many other (mostly meteorological) variables are measured at these sites as well. In the past years, there has been substantial progress in the homogenisation and availability of these direct CO2 flux measurements. The publicly available FLUXNET2015 data set includes more than 1500 site-years of data covering all major biome types from about 165 sites worldwide, spanning a period from 1991 (for some sites) up to 2014 . There has also been substantial progress in the specification of uncertainties in eddy covariance measurements of the land–atmosphere net CO2 exchange flux (net ecosystem productivity, NEP) and its component fluxes (GPP and ecosystem respiration, Reco). For instance, analysed the error distribution and found that the eddy flux data can almost entirely be represented by a superposition of Gaussian distributions with inhomogeneous variance. showed that the measurement errors in NEP are heteroscedastic; i.e. the error variance varies with the magnitude of the flux. In a more recent study investigated the uncertainty of GPP derived from partitioning the eddy covariance NEP measurements. They used a light-use efficiency model embedded in a Bayesian framework to estimate the uncertainty in the separated GPP from the posterior distribution at half-hourly time steps.

 Examples of systematic observations from satellite EO data

There has been a vast extension of EO capabilities during the past 10 years or so both in terms of product quality (including, for instance, improved accuracy) and quantity (new products).

In any data assimilation system using satellite EO data one needs to decide in the design phase of the system whether to assimilate observations at the sensor level (i.e. the spectral radiances for optical sensors or brightness temperatures for microwave sensor, referred to as level 1 data) or to assimilate the biogeophysical variable derived from the radiances through a retrieval algorithm (level 2 data product). When assimilating level 1 data the retrieval algorithm is part of the observation operator linking the model state to the observations in the data assimilation system. A more detailed description of the two alternatives in assimilating EO satellite observations into models of the Earth system is given by . In carbon cycle data assimilation systems level 2 data products (or even level 3 data, which are provided on a regular space–time grid) are most commonly used. However, there is a risk that when using products at level 2 or higher, the parameters/processes implemented in the retrieval algorithm may not be consistent with the corresponding equivalent parameters/processes in the underlying model, thus causing additional errors in the assimilation.

In the next subsections we present some selected remotely sensed Earth observation products, which are relevant for terrestrial carbon cycle data assimilation, in more detail:

atmospheric CO2,

vegetation activity (FAPAR and SIF),

soil moisture,

terrestrial biomass.

 These EO products either have already been used, are in the process of being used or would potentially be a useful data constraint in a CCDAS. For vegetation activity we distinguish two major types of products: more “traditional” reflectance- or radiative-based products, such as the fraction of absorbed photosynthetically active radiation (FAPAR), and recently developed products based on biogeochemical processes, such as sun-induced fluorescence (SIF). Leaf area index LAI, e.g., which is in effect closely related to FAPAR, is another geophysical parameter that represents vegetation activity. There is also a range of remotely sensed vegetation indices, of which NDVI is an example. Both LAI and NDVI have been used in data assimilation studies: an example for NDVI is given by and for LAI by and . In Sect.  we detail the difference between NDVI and FAPAR and explain that FAPAR is based on physical principles. FAPAR has already been demonstrated to provide a strong constraint on terrestrial carbon and water fluxes through its impact on the phenology components of the carbon cycle model either by assimilating only FAPAR data e.g. or in combination with other data streams e.g.. SIF is a promising observation for constraining the gross uptake of CO2 by plant photosynthesis. First assimilation results using SIF observations in a CCDAS show that the uncertainty in global annual GPP is largely reduced by constraining parameters that describe leaf phenology . Remotely sensed atmospheric CO2 concentration (XCO2; see Sect. ) has also been assimilated into a diagnostic terrestrial carbon cycle model to derive net CO2 fluxes consistent with independent in situ measurements of atmospheric CO2 and to reduce posterior uncertainties in the inferred net and gross CO2 fluxes . and assimilated both soil moisture and LAI data into a land surface model, but their focus was on improving the hydrological and land surface physical quantities and not the carbon cycle. assessed the impact of assimilating various remotely sensed soil moisture products into the SiBCASA ecosystem model on simulated carbon fluxes in boreal Eurasia. Although the impact of assimilating ASCAT surface soil moisture was significant, its skill in this hydrologically complex environment strongly depends on surface water and vegetation dynamics. In contrast, showed that when assimilating SMOS soil moisture simultaneously with in situ atmospheric CO2 concentrations, the reduction of uncertainty in gross and net CO2 fluxes relative to the prior is considerably higher than when only assimilating CO2, which quantifies the added value of SMOS observations as a constraint on the terrestrial carbon cycle. So far, remotely sensed biomass data have not been used in carbon cycle data assimilation studies, but several studies e.g. have demonstrated the added value of in situ above-ground biomass observations in constraining the terrestrial carbon cycle.

This list of EO products described in this paper is admittedly subjective and there are of course a whole range of additional remotely sensed products available, which are relevant for carbon cycle studies as well, e.g. burned area e.g., land cover e.g., land surface temperature e.g. or vegetation optical depth (VOD; e.g. ). However, these products are rather used as input or boundary conditions for terrestrial carbon cycle models (burned area and land cover) or, in the case of land surface temperature and VOD, they have so far not been used in carbon cycle data assimilation studies.

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Satellite retrievals of atmospheric carbon dioxide (CO2) and methane (CH4) are available from several satellite instruments such as mid-tropospheric CO2 and CH4 columns from the Infrared Atmospheric Sounding Interferometer (IASI; e.g. ) on EUMETSAT's Metop satellite series, vertical profiles with highest sensitivity in the middle/upper troposphere from AIRS on Aqua e.g., stratospheric profiles from MIPAS on ENVISAT limb observations e.g. and from the solar occultation observations of SCIAMACHY on ENVISAT and ACE-FTS e.g.. These observations have, however, only little or no sensitivity to CO2 and CH4 concentration changes close to the Earth's surface and therefore contain only limited information on regional or local CO2 and CH4 sources and sinks. Satellites with high near-surface sensitivity are nadir (down-looking) satellites which measure radiance spectra of reflected solar radiation in the relevant spectral bands in the near-infrared/shortwave-infrared (NIR/SWIR) spectral region, which are located around 1.6 µm (CO2 and CH4) and around 2.0 µm (CO2). Satellites instruments which perform (or have performed) these observations are SCIAMACHY on board ENVISAT (2002–2012; ), TANSO-FTS on board GOSAT (launched in 2009; ) and NASA's Orbiting Carbon Observatory 2 (OCO-2) mission (launched in 2014; ).

The main CO2 and CH4 data products of these sensors are near-surface-sensitive column-averaged dry-air mole fractions of CO2 and CH4, denoted XCO2 and XCH4. The quantities XCO2 and XCH4 are both retrieved from SCIAMACHY/ENVISAT (ground pixel size: 30 × 50 km2, along track times across track; swath width 960 km with contiguous ground pixels) and TANSO-FTS/GOSAT (10 km pixel size; several, e.g. 3 or 5 non-contiguous pixels across track). OCO-2 delivers XCO2 (8 ground pixels across track, each ≈ 1.3 km) and other satellites have been or will be launched, such as Europe's Sentinel-5-Precursor satellite (S5P; ), which will deliver (among several other parameters) XCH4 (7 km pixel size at nadir, 2600 km swath width with contiguous ground pixels; planned launch: autumn 2017) and China's TanSat (launched end of 2016), which will deliver XCO2 with similar characteristics to NASA's OCO-2. It can be expected that future satellites will provide improved measurements, in particular with respect to more localised emission sources (e.g. Buchwitz et al., 2013; Ciais et al., 2015). In the following we focus the discussion on sensors that have already delivered multiyear XCO2 and XCH4 data sets, i.e. on SCIAMACHY and TANSO.

These satellite-derived XCO2 and XCH4 data products are sensitive to surface fluxes because CO2 and CH4 emission and uptake by surface sources and sinks result in the largest changes of the atmospheric CO2 and CH4 mixing ratio close to the Earth's surface and therefore modify the observed vertical columns. This results in local or regional atmospheric enhancements (e.g. , discussing localised methane sources) or large-scale atmospheric gradients (e.g. , discussing CO2 uptake by the terrestrial biosphere).

Overview of SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT XCO2 and XCH4 level 2 data products (individual ground-pixel retrievals). For some products, level 3 (i.e. gridded data) products are also available (e.g. for CO2_SCI_WFMD and CH4_SCI_WFMD from http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/ and merged SCIAMACHY and TANSO-FTS XCO2 and XCH4 products in Obs4MIPs format from http://www.esa-ghg-cci.org/).

Variable Sensor Source Product (reference) XCO2 SCIAMACHY http://www.esa-ghg-cci.org/ CO2_SCI_BESD CH4_SCI_WFMD TANSO http://www.gosat.nies.go.jp/en/ NIES operational GOSAT http://www.esa-ghg-cci.org/ CO2_GOS_OCFP CO2_GOS_SRFP/RemoTeC http://www.iup.uni-bremen.de/~heymann/besd_gosat.php GOSAT BESD http://disc.sci.gsfc.nasa.gov/acdisc/documentation/ACOS.html NASA ACOS SCIAMACHY & http://www.esa-ghg-cci.org/ TANSO merged CO2_EMMA OCO-2 http://disc.sci.gsfc.nasa.gov/OCO-2 NASA OCO-2 XCH4 SCIAMACHY http://www.esa-ghg-cci.org/ CH4_SCI_WFMD CH4_SCI_IMAP TANSO http://www.gosat.nies.go.jp/en/ NIES operational GOSAT http://www.esa-ghg-cci.org/ CH4_GOS_OCPR CH4_GOS_SRPR/RemoTeC CH4_GOS_OCFP CH4_GOS_SRFP/RemoTeC SCIAMCHY & http://www.esa-ghg-cci.org/ TANSO merged CH4_EMMA

The XCO2 and XCH4 data products retrieved from SCIAMACHY and TANSO are generated from the radiance observations using different approaches. Most approaches are based on optimal estimation (OE; e.g. ), also called Bayesian inference. OE permits constraining the retrieval using a priori information on, e.g. atmospheric vertical profiles of trace gases and aerosols. In general, the radiances are simulated using a radiative transfer model (RTM) and RTM and other parameters (state vector elements) are adjusted until an optimal match is achieved between observed and simulated radiances. One algorithm (WFM-DOAS, WFMD ) is based on least-squares and does not use a priori information to constrain the fit parameters. As a consequence, the resulting XCO2 and XCH4 products are typically somewhat “noisier” compared to the OE products.

The XCO2 and XCH4 data products from SCIAMACHY are generated within the GHG-CCI project of ESA's Climate Change Initiative (CCI, ) and these products are available from the GHG-CCI website (http://www.esa-ghg-cci.org/). XCO2 and/or XCH4 products from GOSAT are generated at several institutions in Japan, Europe and the USA and are available from several sources as shown in Table . The quality of these GHG-CCI products and the XCO2 and XCH4 products generated elsewhere has been significantly improved during recent years e.g. and has now reached quite high maturity when compared to user requirements as formulated in, e.g. . This can be concluded, for example, from the quality of the latest version of the GHG-CCI SCIAMACHY and TANSO XCO2 and XCH4 data set (Climate Research Data Package No. 3, CRDP3; ). Based on comparisons with ground-based observations of the Total Carbon Column Observing Network (TCCON, ) it has been found that the GCOS requirements for systematic error (< 1 ppm for XCO2, < 10 ppb for XCH4) and long-term stability (< 0.2 ppm/year for XCO2, < 2 ppb yr-1 for XCH4) are met for nearly all products. As also shown in , the single observation (ground pixel) retrieval precision (random error primarily due to instrument noise) is about 2 ppm for XCO2 from SCIAMACHY and TANSO and ≈ 15 ppb for TANSO XCH4. For SCIAMACHY XCH4 the precision depends on the time period and retrieval algorithm and is in the range 35–80 ppb. For some products it has also been investigated to what extent the uncertainty can be reduced upon averaging and recommendations are given on how to take into account error correlations , i.e. which values to use for the non-diagonal elements of the error covariance matrix, as an important contribution to the full characterisation of the data needs for data assimilation studies.

Figure  presents an overview of GHG-CCI CRDP3 XCO2 (left) and XCH4 (right) data set in terms of time series and maps. These figures have been generated by gridding the underlying individual ground pixel (level 2) products to generate a 5∘ × 5∘ monthly level 3 Obs4MIPs product . Each 5∘ × 5∘ monthly grid cell also contains an estimate of the overall uncertainty (also shown in Fig. ) which has been computed by taking into account random and systematic error components. The grid cell uncertainty is computed from two terms: (i) using the reported uncertainties as given in the level 2 (individual ground pixel) product files for each of the used satellite products (using an ensemble of SCIAMACHY and GOSAT level 2 products) and (ii) using a term accounting for potential regional/temporal biases as obtained from validation using TCCON ground-based data (see above). The first term depends on the number of individual observations added (the error reduces in proportion to the square root of the number of observations added) whereas the latter term is constant and in the range 0.57–0.87 ppm depending on the satellite XCO2 product or in the range 6–10 ppb for XCH4. As can be seen from Fig. , the uncertainty of the satellite XCO2 retrievals for monthly 5∘ × 5∘ averages is estimated to be typically around 0.5–1 ppm (values larger than 1 ppm are typically associated with regions where only few observations per grid cell exist, e.g. due to clouds or higher latitudes corresponding to low sun elevation). For XCH4 the uncertainty is of the order of a few ppb (typically 4–8 ppb). In , initial TCCON validation results of the Obs4MIPs products are also presented. It is shown that the XCO2 product agrees with monthly averaged TCCON XCO2 within 0.29 ± 1.2 ppm (1σ) and the XCH4 product within 2.0 ± 10.7 ppb. This is hardly worse than the results which have been obtained by careful validation of the individual ground pixel retrievals taking into account the best possible spatio-temporal co-location and considering the averaging kernels, etc. e.g.. Note that the computed differences of Obs4MIPs monthly 5∘ × 5∘ satellite products with monthly averaged TCCON include the errors of the satellite data, errors of the TCCON products, errors due to neglecting altitude sensitivity differences (averaging kernels) and representativity error. This indicates that the representativity error is quite small (at least for monthly 5∘ × 5∘ spatio-temporal sampling and resolution), probably of the order of 0.1–0.2 ppm for XCO2 and a few ppb for XCH4 (it is planned to quantify this error in the future but currently only these rough estimates are available). Note that detailed information on all GHG-CCI products is available on the GHG-CCI website in terms of technical documents, links to peer-reviewed publications and figures including detailed maps for each month and each individual data product.

The SCIAMACHY and TANSO XCO2 and XCH4 retrievals have been used in a number of scientific studies to address important questions related to the sources and sinks of atmospheric CO2 and CH4 by atmospheric inversion studies e.g. and more recently also in a data assimilation context for optimising model parameters . Obviously, the longer the time series and the more accurate it is, the larger the information content of a given data set. Therefore, further improvements are desired and possible (at least in terms of time series extension but likely also in further reduction of remaining biases).

 Reflectance-based vegetation dynamics/activity

Since the early beginnings of remote sensing the state and evolution of the vegetation has been monitored by satellites. An early attempt to analyse vegetation dynamics from space is to calculate the normalised difference vegetation index (NDVI), defined as the ratio between the difference of NIR and visible red (RED) spectral bands, and the sum of NIR and RED: NDVI = (NIR - RED)/(NIR + RED) . The advantage of an index such as NDVI lies in its simplicity and applicability to sensors with few spectral bands such as the Advanced Very High Resolution Radiometer (AVHRR). Therefore this index has been applied for numerous purposes over the last 30 years or so. However NDVI is not a geophysical variable and it is sensitive to various perturbing factors such as atmospheric constituents (aerosols, water vapour), directional effects (geometry of illumination and observation), changes in soil background colour (depending on soil water content; e.g. ). There have been many attempts to modify NDVI and develop additional vegetation indices (VIs) to overcome its limitations, for example the Soil-Adjusted Vegetation Index , Atmospherically Resistant Vegetation Index or Global Environmental Monitoring Index . These indices generally exhibit some improvement in one respect but at the expense of degradation in another respect. demonstrate the limitations of such VIs in representing the complex radiative properties of the canopy–soil system over the visible to NIR albedo range. Satellite-derived LAI products e.g. seem to be an alternative to VIs. LAI is, however, model dependent; i.e. the correct interpretation of this variable depends on the formulation of the model used in the retrieval scheme and may differ from the interpretation adopted by the land biosphere model used for assimilating the LAI product .

Characteristics of a variety of FAPAR products; more details and products are provided by e.g..

Name Time Temporal Definition Reference period resolution MODIS 2000–present 8 days Green canopy, direct radiation SeaWiFS* 1997–2006 10 days Green canopy, diffuse radiation TIP-MODIS 2000–present 16 days FAPAR/green canopy, diffuse radiation TIP-GlobAlbedo 2002–2011 8 days FAPAR/green canopy, diffuse radiation Vegetation 1999–present 10 days FAPAR, direct radiation

* The same algorithm is used for MERIS (called JRC MGVI, 2002–2011) and SPOT-Vegetation (2012–present) with a 1.2 km, 10-day resolution .

Time series of satellite-derived XCO2 in three latitude bands (see annotation bottom left, e.g. red line: 30 to 60∘ N) and maps showing the spatial distribution of XCO2 for April 2014 (top left) and corresponding XCO2 uncertainty (top right). (b) As (a) but for XCH4 (maps: September 2014).

A rational approach to addressing all these issues together is to design a physically based quantity which is determined by the state of the canopy–soil system. The fraction of absorbed photosynthetically active radiation (FAPAR), which is a normalised fraction with values ranging from 0 to 1, provides information on the photosynthetic activity of the land vegetation. It is recognised as an essential climate variable (ECV; ) and is based on the land surface radiation budget. It is defined as the fraction of the photosynthetically active radiation (i.e. incoming solar radiation in the spectral region 0.4–0.7 µm) that is absorbed by the vegetation canopy (see also for a mathematical definition). Several FAPAR products are derived from a variety of optical sensors (e.g.  ATSR, MERIS, MISR, MODIS, SEVIRI, SeaWiFS, VEGETATION) at different spatial and temporal resolutions. Although there have been substantial efforts to harmonise products across sensors and establish standards and validation practices e.g., there are still considerable differences among the products. These differences can mainly be associated with differences in the retrieval methodology as well as to the quality of input variables. A recent overview of various FAPAR products and their specifications, but without an assessment of product uncertainties, is given by . Table  summarises the characteristics of the most common FAPAR products.

Several studies have compared the performance of different satellite-derived FAPAR products: looked at four FAPAR data sets over northern Eurasia for the year 2000, evaluated six products across Australia, compared three products over Europe, assessed five products over different land cover types, and compared two FAPAR products derived from GlobAlbedo and MODIS data. concluded that, although all six evaluated products display robust spatial and temporal patterns, there is considerable disagreement in the absolute magnitude among the products and none of the products outperforms the others. This has also been confirmed by the studies of and . One of the reasons for these differences are different assumptions on the underlying biome types. They also reviewed the consistency of the FAPAR products against in situ field measurements: the mean difference between the EO products and the in situ field measurements is around 0.1. This estimate is confirmed by the study of , who suggest an average uncertainty of 0.14 from validation against total FAPAR and 0.09 from validation against green FAPAR in situ measurements. In their comparison of the Joint Research Centre – Two-stream Inversion Package (JRC-TIP) MODIS, JRC MGVI (based on MERIS) and Boston University MODIS products (see Table ), placed special emphasis on the assessment of the product uncertainties by not only comparing the uncertainties (or quality indicators) as proposed by the product teams but also by calculating an independent theoretical uncertainty based on the triple collocation (TC) method (see Sect. ). While the uncertainties specified by the product teams differed by up to 0.1 among the products, the TC method suggested more consistent uncertainties among the three products of around 10–20 % of the signal.

The JRC-TIP is an inverse modelling system that was explicitly designed to retrieve a set of land surface variables, including FAPAR, in a form that is compliant with the requirements for assimilation into terrestrial biosphere models; hence we focus in the following on this product. TIP is based on a one-dimensional two-stream representation of the radiative transfer in the canopy–soil system and applies the same inversion approach as CCDAS, which is briefly sketched in Sect.  and detailed in and . In a first step it retrieves a set of model parameters describing the state of the vegetation canopy system including the full uncertainty covariance of the parameters by combining prior information with observed radiant fluxes. Further, the model is used to propagate this PDF forward onto simulated fluxes such as FAPAR. TIP uses observed broadband albedo in the NIR and visible spectral domains as input. The prior information used in the retrieval is constant in space and time; i.e. all variability is determined from space . This is in contrast to other retrieval approaches, which are based on prescribed land cover maps e.g.. Long-term global records of JRC-TIP products (see Table ) have been retrieved from broadband albedos provided by MODIS collection 5 and Globalbedo . Products are provided for each of the respective 16-day (MODIS) and 8-day (Globalbedo) synthesis periods. To reduce disk space, by default, JRC-TIP products are delivered without correlations among the uncertainties between individual variables, even though these correlations are available. An estimate of uncertainty correlation in space and time is not provided. Both JRC-TIP records are provided in the native 1 km resolution of the albedo input products. In order to maintain the above-mentioned compliance with terrestrial models, coarser-resolution products are to be derived by applying JRC-TIP to aggregated albedo inputs as in. JRC-TIP products are validated at site and regional scales ; more details on JRC-TIP are given in .

 Biogeochemical-based vegetation activity

Sun-induced fluorescence (SIF) is an electromagnetic signal emitted as a two-peak spectrum between 650 and 850 nm by the chlorophyll a of green plants under solar radiation. SIF can be directly related to photosynthetic electron transport rates and yields a mechanistic link to photosynthesis and the subsequent gross carbon uptake by terrestrial vegetation (GPP; ). Recent developments in satellite-based spectroscopy have enabled the first retrievals of SIF from space , which holds the promise of enabling new approaches to globally monitoring terrestrial photosynthesis. For example, a high linear correlation between data-driven GPP estimates and SIF retrievals at global and annual scales was reported by . The skills of SIF as a proxy for photosynthetic activity and GPP were also reported by studies over different ecosystems, like the Amazon rainforest , large crop belts and the boreal forests in Eurasia and North America . However, in the context of DA and in order to extract the maximal benefit from SIF data, the complex processes responsible for SIF in the plants' photochemical systems (as mentioned above) require complex models as observation operators for SIF.

The global retrieval of SIF from space relies on the principle of in-filling of solar Fraunhofer lines by SIF . Fraunhofer lines are absorption features in the solar spectrum, caused by elements in the solar atmosphere and sufficiently resolved by atmospheric spectrometers. Because of the additive nature of SIF, the fractional depth of the Fraunhofer lines detected by the satellite instrument decreases with the amount of SIF being emitted at the same wavelength. The retrieval of SIF from space is then based on the evaluation of the depth of the Fraunhofer lines present in red and NIR top-of-atmosphere spectra. The retrieval forward model is thus simple and can be linearised e.g., which simplifies the inversion.

Selected characteristics of operating and planned space-borne instruments able to deliver SIF data. Names of upcoming instruments are highlighted in italics. NIR stands for near-infrared. It must be noted that GOME-2 on MetOp-A has been operating with a reduced pixel size of 40 × 40 km2 since July 2013. References are examples: the full list is given in the text.

Name Time Overpass Spectral Spatial Temporal Reference period time sampling resolution resolution GOSAT 2009–today Midday NIR 10 km diam. 3 days e.g.  GOME-2 2007–today Morning red & NIR 40 × 80 km2 < 2 days e.g. , SCIAMACHY 2003–2012 Morning red & NIR 30 × 240 km2 < 3 days e.g. , OCO-2 2014–today Midday NIR 1.3 × 2.3 km2 16 days TROPOMI ∼ 2017 Midday red & NIR 7 × 7 km2 < 1 day FLEX ∼ 2022 Morning red & NIR 0.3 × 0.3 km2 < 27 days

Fraunhofer line-based SIF retrievals tend to be accurate but not precise: uncertainties are dominated by a random component associated to instrumental noise, which is linearly mapped into SIF retrievals. The amplitude of instrumental noise, and hence 1σ single-retrieval errors, scale with at-sensor radiance for the most common case of grating-based spectrometers dominated by multiplicative noise. This implies that retrieval errors are mostly driven by surface brightness and sun zenith angles . Because of this high contribution of random errors to the total retrieval uncertainty, single SIF retrievals are commonly linearly aggregated as spatio-temporal composites in which random errors are reduced. The number of retrievals to be aggregated into a given grid cell results from a compromise between spatial resolution, temporal resolution and precision of the gridded product, the size of the spatial and temporal bins being exchangeable in terms of their effect on the random uncertainty. The random uncertainty of the resulting spatio-temporal composites is then not only driven by surface albedo and illumination, but also by the number of soundings going into a given grid cell, which is in turn defined by cloudiness and latitude (in the case of overlapping orbits). Detailed analyses of random errors in SIF retrievals for different space-borne instruments can be found in and .

Global SIF data sets have been or are being derived from GOSAT, MetOp's Global Ozone Monitoring Experiment-2 (GOME-2), ENVISAT's SCIAMACHY and the OCO-2 mission . All four missions except for SCIAMACHY are still operating. Sample SIF maps from GOSAT, GOME-2 and SCIAMACHY for July 2010 are displayed in Fig. . The spectral, spatial and temporal sampling of single SIF soundings varies for each instrument, as it is summarised in Table . For example, GOME-2 and SCIAMACHY provide SIF retrievals in the red and NIR spectral regions with global coverage and a relatively high temporal resolution. However, this comes at the expense of a coarse spatial resolution, which is 40 × 80 km2 for GOME-2 (40 × 40 km2 for GOME-2 on MetOp-A since July 2013) and 30 × 240 km2 for SCIAMACHY. On the other hand, GOSAT and OCO-2 do not provide spatially continuous measurements (i.e. no global coverage), but single soundings in the NIR have a much higher spatial resolution than those of GOME-2 and SCIAMACHY. In particular, OCO-2 soundings correspond to ground areas of about 4 km2, which is substantially finer than that of the other data sets. The number of soundings per day by OCO-2 is also much larger (about 100×) than that by the other instruments , which makes OCO-2 SIF to be the most suited data set for studies over areas not requiring a continuous spatial sampling but benefiting from a high spatial resolution. This is the case, for example, of tropical and boreal forests: spatial continuity is less critical for those ecosystems because they are relatively homogeneous over large spatial scales, whereas the high spatial resolution is important for maximising the number of clear-sky soundings during the parts of the year with persistent cloudiness.

Concerning near-future perspectives for SIF monitoring, it can be expected that the limitations in spatial resolution and coverage of existing SIF products will be alleviated with the advent of the TROPOspheric Monitoring Instrument (TROPOMI) scheduled for launch on board the Sentinel-5 Precursor satellite mission by mid-2017 (Table ). TROPOMI will enable SIF retrievals in the red and NIR regions similar to GOME-2 and SCIAMACHY, but with a 7 km pixel, daily global coverage and a number of clear-sky observations per day, ≈ 200 times larger than GOME-2 and ≈ 600 times larger than SCIAMACHY. The SIF product from TROPOMI can therefore be anticipated to have a much higher spatio-temporal resolution and signal-to-noise ratio than those from GOME-2 and SCIAMACHY . Complementarily, the FLuorescence EXplorer (FLEX; ) has recently been selected for implementation by ESA, with launch currently expected for 2022. FLEX will provide global measurements of SIF in the red and NIR at a relatively low temporal resolution, but with the finest spatial resolution of all existing and upcoming space-borne instruments.

 Soil moisture

Soil moisture is measured in situ through large-scale soil moisture monitoring networks or at various FLUXNET sites . Yet, these point observations have only limited coverage in space time, have spatially very divergent properties , and often contain large representativeness errors at the scale of global ecosystem models . Satellite remote sensing in the microwave domain has the potential to overcome many of these issues. Microwave remote sensing uses the contrasting dielectric properties of water, air, ice and soil particles to infer the water content in the soil column . Both passive radiometer systems, measuring the emitted microwave radiance (brightness temperatures), and active radar systems, measuring backscattered microwave radiance, can be used to retrieve soil moisture. Various approaches exist that convert brightness temperatures and backscatter measurements into estimates of soil moisture, including radiative transfer model inversion approaches e.g., neural networks e.g., linear regressions e.g. and change detection methods . The latter is commonly applied to scatterometer measurements and yields, in contrast to the other approaches which provide soil moisture as volumetric water content, soil moisture as a percentage of total saturation. Microwave sensors operate in different frequency (wavelength) domains, of which L-band (with a wavelength of ≈ 23 cm) and C-band (≈ 5 cm) are most commonly used for retrieving soil moisture . Smaller wavelengths are more sensitive to the vegetation canopy covering the soil and increasingly lose their sensitivity to water. Still, frequencies up to 19 GHz (≈ 1.5 cm) have proven potential for providing robust soil moisture estimates at the global scale for moderately to sparsely vegetated areas . Due to the relatively low energy levels and the technical challenges in microwave domain, spatial resolutions of the satellite observations are generally coarse (≈ 25–50 km) but with high revisit frequencies (up to 1 day). Only synthetic aperture radar is able to provide much higher spatial resolutions, up to a few tens of metres, yet at the cost of long revisit times. Also observations made by the Gravity Recovery and Climate Experiment (GRACE; ) are sensitive to soil moisture, but the estimation of soil moisture content from these observations is not straightforward because they are also sensitive to changes in snow, surface water, groundwater and vegetation.

Maps of sun-induced fluorescence (SIF) for July 2010 derived from GOSAT, GOME-2 and SCIAMACHY satellite data.

Current (pre-)operational global soil moisture missions and products (see the list of acronyms).

Mission Organisation Measurement concept Band Mission start Data access MetOp – EUMETSAT Real aperture radar C-band Jan. 2007 http://hsaf.meteoam.it/soil-moisture.php ASCAT (scatterometer) http://land.copernicus.eu/global/products/swi SMOS ESA Interferometric L-band Nov. 2009 http://www.catds.fr/ radiometer GCOM-W1 JAXA Radiometer C-band May 2012 http://www.vandersat.com/ AMSR2 http://suzaku.eorc.jaxa.jp/GCOM_W/ SMAP NASA Radiometer L-band Jan. 2015 http://smap.jpl.nasa.gov/ & radar* Sentinel-1 ESA/ Synthetic aperture C-band Apr. 2014 https://www.eodc.eu/ Copernicus radar CCI ESA Combined scatterometer L-, C-, X- Nov. 1978 http://www.esa-soilmoisture-cci.org and radiometer Ku-band

* SMAP's radar failed in July 2015.

Since the release of the first global soil moisture data sets from microwave sensors in the early 2000s, the number of available soil moisture products and missions has rapidly expanded . Several (pre-)operational products are now available from a wide variety of data providers and space organisations (Table ). While initially soil moisture products were based on sensors mainly designed for other purposes (such as ASCAT, AMSR2 and Sentinel-1), ESA and NASA launched their own dedicated soil moisture satellite missions SMOS and SMAP . Differences between the various products exist in their technical design, observation bands and retrieval algorithms, which often result in complementary strengths over different land cover types . The missions also differ in their degree of operationalisation: while SMOS and SMAP are primarily scientific concept demonstrators, AMSR2 continues the legacy of C-band radiometer observations started by JAXA and NASA in 2002 with the launch of AMSR-E, while ASCAT is embedded in a fully operational programme of weather observing satellites with guaranteed continuation at least until 2023 and a follow-on mission already under development . Apart from the target variable surface soil moisture, some products come with estimates of freeze/thaw state and VOD, which are disentangled from the soil moisture impacts on the measured microwave signal during the retrieval process.

As none of the currently active missions cover a period long enough to study climate change impacts, ESA's Climate Change Initiative (CCI) endorsed the combination of available soil moisture products from active and passive microwave sensors into a consistent multidecadal record. The ESA CCI soil moisture product currently combines soil moisture products from 11 different sensors into a homogenised daily product spanning the period 1978–2015 . Several studies have demonstrated the value of ESA CCI soil moisture for assessing long-term interactions between soil moisture and vegetation productivity .

Key to a proper assimilation of remotely sensed soil moisture into carbon models is a correct characterisation of its errors. Apart from instrument errors which are common to all observations, the quality of microwave-based soil moisture retrievals is particularly impacted by vegetation cover, soil frost, snow cover, open water, topography, surface roughness, urban structures and radio frequency interference . Observations in which a strong adverse impact of these factors is detected are usually masked during processing, which may lead to data gaps for certain areas or periods of the year . If cases in which their impact on the soil moisture retrieval are only moderate, the errors that they introduce are either simulated during the retrieval itself using error propagation methods, or assessed a posteriori against reference data using various statistical methods .

While the ASCAT and AMSR2 products come with an estimate of the error variance for each observation by error propagation , this is still not common practice for all soil moisture products. Yet, no error propagation model perfectly represents all error sources and interactions . On the other hand, the use of in situ soil moisture measurements to estimate random errors is hampered by their heterogeneous nature and large spatial representativeness errors . As an alternative, in recent years triple collocation analysis (TCA) has firmly established itself as a robust alternative to estimate random errors in soil moisture data sets without the need of an absolute “true” reference . TCA estimates the error variances of three spatially and temporally collocated soil moisture data sets with independent error structures, e.g. a radiometer-based, a scatterometer-based and a land surface model soil moisture data set. Recently, the TCA has been intensively elaborated, e.g. to solve for colinearities between errors and non-linear dependencies between data sets . The most remarkable advancement has been to express TCA-based error estimates as a signal-to-noise ratio, which facilitates a direct intercomparison of the skill of data sets independent of their dynamic ranges ; see Fig. . Although the TCA provides an estimate that is entirely independent of any retrieval model assumptions, it only provides a single average error estimate for the entire period under consideration. Thus, synergistic use of error propagation and triple collocation estimates shall be exploited to better capture the temporal error dynamics needed for an optimal assimilation into carbon models. Due to the recent progress in product quality, error characterisation and operationalisation, satellite-based soil moisture products have reached the level of maturity that allows for a systematic assimilation into land surface models. These products have been used to improve model hydrology by, for example, who showed that the assimilation of SMOS and ESA CCI soil moisture generally has a small positive impact on soil water storage and evaporative fluxes as simulated by the GLEAM land evaporation model. Surface soil moisture from ASCAT is assimilated operationally in near-real-time into the ECMWF Land Data Assimilation System to obtain root-zone soil moisture . Global root-zone soil moisture products based on SMOS and SMAP are derived by a slightly different approach, which assimilate the observed brightness temperatures instead of the retrieved surface soil moisture products . The assimilation of satellite-based soil moisture products in terrestrial carbon cycle models has been described above.

Signal-to-noise ratio (in dB), estimated with the triple collocation analysis for four different satellite-based soil moisture products and a land surface model. (a) MetOp-A ASCAT based on the TU Wien method ; (b) AMSR2 based on the LPRM model ; (c) SMOS L3 ; (d) SMAP . An SNR of -3 indicates a signal variance that is half of the noise variance, an SNR of 0 a signal variance equal to the noise variance, an SNR of 3 a signal variance that is twice the noise variance and so on. In areas without data the TC could not be computed, e.g. because of too few observations in one of the data sets. For details see .

 Biomass

Continental-scale biomass maps have been produced from space using both radar and lidar. Biomass here refers to above-ground biomass (AGB), since there are no methods to measure the below-ground component, and this is typically inferred from AGB using allometric equations. Furthermore, the emphasis is on the AGB of forests, although a global data set of AGB in all biomes for the period 1993–2012 has been produced based on VOD data from global passive microwave sensors, hence with spatial resolution of 10 km or coarser . The AGB product is derived from a regression of VOD against observations of AGB from ground-based inventory data.

Using long time series of C-band radar data provided by the ESA Envisat satellite, the growing stock volume of Northern Hemisphere boreal and temperate forests has been estimated . Although available at 0.01∘ resolution, the accuracy of growing stock volume at this scale is comparatively poor, and spatial averaging provides more reliable results: at 0.5∘ spacing, estimated growing stock volume has a relative accuracy of 20–30 % when tested against inventory data . used this product to derive the carbon stock (above- and below-ground) in boreal, temperate mixed and broadleaf and temperate coniferous forests of forests above 30∘ N (40.7, 24.5 and 14.5 PgC respectively). These values have estimated accuracies of around 33–39 % under a conservative approach to estimate uncertainty. provide a high-resolution data set (0.01∘) over the Northern Hemisphere with a relative RMSE against National Forest Inventory between 12 and 45 %.

For tropical forests, the key sensor is the Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud and land Elevation Satellite (ICESat) which failed in 2009 . Its archive of forest height estimates was the core data set exploited to produce two pantropical biomass maps at grid scales of 1 km and 500 m respectively; also provide a map of the errors associated with the biomass estimates at each pixel. This is produced by combining measurement errors, allometry errors, sampling errors and prediction errors, which are treated as independent and spatially uncorrelated. Further details are given in the supplementary material to . In an attempt to resolve differences between these two maps, used an independent reference data set of field observations to remove the biases in the maps and then combined them to estimate the AGB in the tropical belt (23.4∘ S to 23.4∘ N). Testing against a reference data set not used in the fusion process indicated that the fused map had a RMSE 15–21 % lower than that of the input maps and nearly unbiased estimates.

However, there are unresolved questions about large-scale biomass patterns across the Amazon inferred from in situ and satellite data. Biomass maps derived from satellite data in and differ significantly from each other and from biomass maps derived from in situ plots distributed across Amazonia using kriging . Neither satellite product exhibits the strong increase in biomass from southwestern to northeastern Amazonia inferred from in situ data. attributed this to failure to account for gradients in wood density and regionally varying tree height–diameter relations when estimating biomass from the satellite data. reject this analysis and claim that the trends and patterns in are erroneous and a consequence of inadequate sampling. Resolving this disagreement is of fundamental importance since it raises basic questions on accuracy, uncertainty and representativeness for both in situ and satellite-derived biomass data.

The next 4–5 years will dramatically improve our global knowledge of biomass, with the launch of three missions aimed at measuring forest structure and biomass. The ESA BIOMASS mission , to be launched in 2021, is a P-band radar that will provide near-global measurements of forest biomass and height. Measurements from airborne sensors indicate that even in dense tropical forests affected by topography, the P-band frequency used by BIOMASS will give sensitivity to biomass up to 350–450 t ha-1 . Around the same time the NASA-ISRO SAR mission (NISAR) based on an L-band sensor will be deployed, providing measurements of biomass in lower biomass forests (up to 100 t ha-1). These highly complementary missions will be further complemented by the NASA Global Ecosystem Dynamics Investigation vegetation lidar to be placed on the International Space Station around 2019; this aims to provide the first global, high-resolution observations of the vertical structure of tropical and temperate forests, from which biomass may be estimated.

As well as limitations caused by mission lifetimes, satellite measurements of biomass are unlikely to be sensitive enough to measure biomass increment except in rapidly growing plantations and tropical forests. Hence an important ancillary data set for studies aiming to relate biomass to climate and environment is tree ring data (https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/tree-ring).

 Conclusions

In the context of carbon cycle data assimilation this paper reviews the requirements and summarises the availability and characteristics of some selected observations with a special focus on remotely sensed Earth observation data. Observations are key for understanding the carbon cycle processes and are an important component for any data assimilation system. In this context the provision of systematic and sustained observing systems on an operational basis is becoming more and more important.

An example for such an operational network for in situ data is the Integrated Carbon Observing System (ICOS; see also https://www.icos-ri.eu). ICOS is a pan-European infrastructure for carbon observations, which provides high-quality in situ observations (both fluxes as well as atmospheric concentrations) over Europe and over ocean regions adjacent to Europe with a long-term perspective. ICOS consists of central facilities for co-ordination, calibration and data in conjunction with networks of atmospheric, oceanic and ecosystem observations as well as a data distribution centre, the Carbon Portal, providing discovery of and access to ICOS data products such as derived flux information. Other (quasi-)operational networks measuring atmospheric CO2 concentrations are maintained, for instance, by the National Oceanic and Atmospheric Administration (NOAA) Climate Monitoring and Diagnostics Laboratory and the Scripps Institution of Oceanography, both USA, as well as the CSIRO Global Atmospheric Sampling Laboratory, Australia.

An example for an operational space-based Earth observing programme in Europe is the fleet of so-called Sentinel satellites of the Copernicus programme. Copernicus aims to provide Europe with continuous and independent access to Earth observation data and associated services (transforming the satellite and additional in situ data into value-added information by processing and analysing the data) in support of Earth system science . Currently, six different Sentinel missions are planned (and have partly been launched). So far, a dedicated mission for monitoring the carbon cycle, i.e. an instrument measuring the atmospheric CO2 composition, is not yet included in the Copernicus monitoring programme see; however, the series of Sentinel satellites will be extended in the future and will likely include a CO2 mission. Other operational EO programmes are operated by e.g. NOAA and the Japanese Aerospace Exploration Agency.

The paper also briefly recapitulates the assimilation systems capable of integrating these data: a more comprehensive description of the underlying formalism is given in while discuss the implementation strategies for a multiple data assimilation system and their impacts on the results. To take maximum advantage of these data streams in carbon cycle data assimilation studies it is of utmost importance to have the appropriate knowledge of the uncertainty characteristics of the observational data, here with a focus on satellite products. This includes an understanding of the observable and its representativeness in order to develop the appropriate observation operator see also but also the structure of any biases, random errors and error covariances (that is both the diagonal and off-diagonal elements quantifying the error correlations between different observations).

The benefit of using multiple data streams in a CCDAS lies in the complementarity of the data and thus in the ability to constrain different components of the underlying process model. In fact, because of the model internal interactions and feedbacks among the components the simultaneous assimilation of complementary observations has synergistic effects such that the constraint is larger than the sum of the individual constraints, as shown for instance by , who assimilated observations of FAPAR and latent heat flux.

As a final remark one important aspect of observational data is their continuity, since much of the important information is contained in response to climate anomalies. Fortunately, the set up of operational observing systems such as ICOS for in situ data or Copernicus for satellite data has created the necessary infrastructure to ensure a long-term perspective in the provision of Earth observations.

No data sets were used in this article.

 List of acronyms ACE-FTS Atmospheric Chemistry Experiment – Fourier transform spectrometer AGB Above-ground biomass AIRS Atmospheric Infrared Sounder AMSR2 Advanced Microwave Scanning Radiometer 2 AMSR-E Advanced Microwave Scanning Radiometer – Earth observing system ASCAT Advanced scatterometer ATSR Along Track Scanning Radiometers AVHRR Advanced Very High Resolution Radiometer CCDAS Carbon cycle data assimilation system CCI Climate Change Initiative ECMWF European Centre for Medium-Range Weather Forecasts ECV Essential climate variable EO Earth observation (in this form generally understood as from space) ESA European Space Agency FAPAR Fraction of absorbed photosynthetically active radiation FLEX FLuorescence EXplorer GCOM-W1 Global Change Observation Mission 1st-Water GLAS Geoscience Laser Altimeter System GLEAM Global Land Evaporation Amsterdam Model GOME-2 Global Ozone Monitoring Experiment-2 GOSAT Greenhouse Gases Observing Satellite GPP Gross primary productivity IASI Infrared Atmospheric Sounding Interferometer ICOS Integrated Carbon Observing System ICESat Ice, Cloud and land Elevation Satellite ISRO Indian Space Research Organisation JAXA Japan Aerospace Exploration Agency JRC-MGVI Joint Research Centre – MERIS Global Vegetation Index JRC-TIP Joint Research Centre – Two-stream Inversion Package LAI Leaf area index MERIS Medium Resolution Imaging Spectrometer MIPAS Michelson Interferometer for Passive Atmospheric Sounding MISR Multi-angle Imaging SpectroRadiometer MODIS Moderate Resolution Imaging Spectroradiometer NASA National Aeronautics and Space Administration NDVI Normalised difference vegetation index NIR Near infrared NOAA National Oceanic and Atmospheric Administration Obs4Mips Observations for Model Intercomparisons Project OCO-2 Orbiting Carbon Observatory 2 OE Optimal estimation PDF Probability density function SAR Synthetic aperture radar SCIAMACHY SCanning Imaging Absorption spectroMeter for Atmospheric CHartography SeaWiFS Sea-viewing Wide Field-of-view Sensor SEVIRI Spinning Enhanced Visible and InfraRed Imager SIF Sun-induced fluorescence SMAP Soil Moisture Active Passive SMOS Soil Moisture Ocean Salinity SWIR Shortwave infrared TANSO-FTS Thermal And Near infrared Sensor for carbon Observations – Fourier Transform Spectrometer TCA Triple collocation analysis TCCON Total Carbon Column Observing Network TCOS Terrestrial Carbon Observation System TROPOMI TROPOspheric Monitoring Instrument VI Vegetation index VOD Vegetation optical depth

The authors declare that they have no conflict of interest.

This article is part of the special issue “Data assimilation in carbon/biogeochemical cycles: consistent assimilation of multiple data streams” (BG/ACP/GMD interjournal SI). It is not associated with a conference.

Acknowledgements

Michael Buchwitz has received funding from ESA via the GHG-CCI project. Wouter Dorigo is supported by the TU Wien Wissenschaftspreis 2015, a personal grant awarded by the Vienna University of Technology. Figure 4 was kindly provided by Philipp Köhler, California Institute of Technology. We acknowledge the support from the International Space Science Institute (ISSI). This publication is an outcome of the ISSI's working group on “carbon cycle data assimilation: how to consistently assimilate multiple data streams”. Edited by: Victor Brovkin Reviewed by: Natasha MacBean and Thomas Kaminski

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Hydrometeorol., 17, 1725–1743, 10.1175/JHM-D-15-0213.1, 2016. </app></app-group></back> <!--<article-title-html>Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems</article-title-html> <abstract-html><p class="p">The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO<sub>2</sub> and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).</p><p class="p">We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al.(2005), emphasising the rapid advance in relevant space-based observations.</p></abstract-html> <ref-html id="bib1.bib1"><label>Al-Yaari et al.(2016)</label><mixed-citation> Al-Yaari, A., Wigneron, J., Kerr, Y., de Jeu, R., Rodriguez-Fernandez, N., van der Schalie, R., Bitar, A. A., Mialon, A., Richaume, P., Dolman, A., and Ducharne, A.: Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations, Remote Sens. 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