L-band satellite radiometry has emerged as an important tool for monitoring Earth's essential climate variables (ECVs). It relies on spaceborne radiometers operating in a protected band (1.4-1.427 GHz) to measure Earth's surface thermal microwave emission in brightness temperatures. Observations at this band experience minimal atmospheric attenuation and radio-frequency interference (RFI), and can be acquired continuously from day to night, ensuring a short global revisit time. Moreover, L-band radiation can partially penetrate natural materials, allowing for the assessment of subsurface state parameters. These features make L-band radiometry satellites valuable for continuous global climate monitoring, offering unique advantages in tracking specific ECVs that are difficult to measure using other remote sensing techniques. This article reviews recent advances in satellite microwave radiometry at L-band, with a focus on the contributions of key missions-SMOS, Aquarius and SMAP-in retrieving six critical ECVs: 1) surface soil moisture (SM), 2) soil freeze/thaw (FT) status, 3) vegetation aboveground biomass (AGB), 4) sea surface salinity (SSS), 5) sea surface wind (SSW) speed, and 6) sea ice thickness (SIT). It summarizes the rationale behind satellite microwave radiometry and its role in understanding of the spatiotemporal dynamics of these ECVs on a global scale based on more than 16 years of continuous data. Furthermore, it identifies the state-of-the-art status of the discussed ECV products, highlights current challenges, and outlines future directions for the application of microwave radiometry in monitoring ECVs.

CryoRad is a candidate satellite mission concept equipped with a broadband low-frequency microwave radiometer operating between 0.4 and 2 GHz with continuous frequency coverage. Its primary objective is to advance cryosphere science by delivering new geophysical variables critical for ocean and climate studies. CryoRad will provide temperature profiles of the Antarctic and Greenland ice sheets from surface to bedrock, previously accessible only through sparse borehole observations, and will address long-standing limitations of L-band radiometers in retrieving sea surface salinity (SSS) in cold waters. Furthermore, the mission aims to significantly improve estimates of sea ice thickness, volume, and salinity, thereby enabling a more comprehensive characterization of the polar ocean-sea ice system. To quantify the scientific return and support the definition of radiometer performance requirements, we combine forward modeling and sensitivity experiments with data from the ECMWF ORAS6 ocean reanalysis. ORAS6 includes a multicategory sea ice model with prognostic ice salinity, providing spatially and temporally consistent fields of sea ice concentration, thickness, volume, temperature, and salinity. These parameters serve as a physically consistent baseline for simulating CryoRad brightness temperatures using simplified one-layer emissivity models. Sensitivity analyses reveal the frequency-dependent impact of ice thickness, salinity, and ocean surface properties on measured brightness temperatures.

Sea surface salinity (SSS) is a key variable for understanding ocean circulation, freshwater fluxes, and climate variability. While L-band radiometry (1.4 GHz) has enabled global monitoring of SSS from space, the errors of retrieved SSS are two to three times larger in cold waters than in warm waters due to reduced sensitivity of the radiometric signal to SSS. To address this limitation, this paper investigates the potential of wideband passive microwave radiometry (0.4–2 GHz). Using a multi frequency Bayesian retrieval scheme, and two extreme instrumental designs, we show that wideband radiometry has the potential to reduce SSS uncertainties by a factor of 2 to 3 in cold and salty waters, and by more than a factor 4 for SSS around 20 pss. Wideband SSS retrievals would also be more resilient to radiometric calibration issues than L-band. Consequently, wideband radiometry could enable the detection of geophysical SSS anomalies in many polar regions where this is not possible with current Earth observation satellites. The lowest frequencies are the ones contributing the most to the uncertainty decrease. Our study highlights the need to pursue research into technical and methodological development for optimizing radiometric measurements in the low frequency range, and minimize the impact of Radio Frequency Interference, of the galactic signal and, during the day, of the sun. Wideband radiometry could represent a breakthrough with respect to L-Band radiometry for monitoring SSS variations in the polar regions where large climate variability is ongoing, under the interaction between the ocean, atmosphere and cryosphere.

The validation of optical satellite data products is a central but challenging component of the space missions. In order to validate the satellite images, ground data is used for reference and allows also the assessment of the associated total uncertainty budget. Overall, when comparing ground data and satellite measurements three main uncertainty sources need to be considered: i) instrument characterisation, ii) algorithm retrieval performances and iii) spatial representativeness. These key components affect the proper comparison of ground measurements with satellite data and, thus, have to be carefully examined. JB devices (FloX and RoX) are hyperspectral instruments acquiring optical field data with standardized hardware and routines. They have collected a legacy of data for over half of a decade using a comprehensive and readily implemented open-source data processing chain, considering the individual laboratory characterization of each instrument’s optical performance. Thus, the instruments are capable of providing valuable data products for the purpose of satellite validation. In particular, the FloX (Fluorescence BoX, JB Hyperspectral Devices GmbH) is the first commercially available device for the measurement of solar-induced chlorophyll fluorescence (SIF). The instrument was developed with the support of the scientific community following the specification of the Fluorescence Explorer mission (FLEX) by the European Space Agency (ESA), expected to be launched in 2024. The FloX features a high performing spectrometer (FWHM: 0.3 nm, SSI: 0.15, SNR: 1000) and allows stand-alone measurement of SIF emission at canopy level on the ground. Furthermore, the FloX enables the continuous measurements of spectral down-welling and up-welling radiance in the VIS-NIR range using an additional spectrometer to cover a larger spectral range and allows the automatic computation of reflectance as well as various vegetation indices (VIs). The instrument synchronously acquires upwelling and downwelling radiance during each measurement cycle, automatically optimizes the integration time according to light conditions and acquires the dark current and internal quality flags to ensure high quality data products. In addition to SIF and VIs, the FloX produces time series of high-resolution radiometric parameters, suitable for the investigation of the optical properties from the monitored targets. In the last years over 60 FloX units have been deployed worldwide.Within a current ESA project, we are investigating the instrument uncertainty sources, with the final aim of defining a preliminary version of the FLEX validation plan. At the same time, currently deployed instruments in 10 location around the world were used to examine the agreement of the ground measurements with available satellite product (i.e. Sentinel-2). This approach reversed the common practice of validating satellite data with ground measurements by using the globally available, standardized L2A products of Sentiel-2 evaluating the conformance of ground-measured data products across a network of standardized instruments. An unprecedented alignment of satellite and ground data was achieved, confirming high validity of data products from the network of automated field spectrometers around the globe.In summary, in this contribution we provide an overview of how field spectroscopy systems can be used in the framework of specific activities with the purpose of satellite validation.

Advances in Earth observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water land-surface models with the capability to simultaneously assimilate several such data streams. The present article discusses the requirements for such a model and presents one such model based on the combination of the existing Data Assimilation Linked Ecosystem Carbon (DALEC) land vegetation carbon cycle model with the Biosphere Energy-Transfer HYdrology (BETHY) land-surface and terrestrial vegetation scheme. The resulting D&B model, made available as a community model, is presented together with a comprehensive evaluation for two selected study sites of widely varying climate. We then demonstrate the concept of land-surface modelling aided by data streams that are available from satellite remote sensing. Here we present D&B with four observation operators that translate model-derived variables into measurements available from such data streams, namely fraction of photosynthetically active radiation (FAPAR), solar-induced chlorophyll fluorescence (SIF), vegetation optical depth (VOD) at microwave frequencies and near-surface soil moisture (also available from microwave measurements). As a first step, we evaluate the combined model system using local observations and finally discuss the potential of the system presented for multi-stream data assimilation in the context of Earth observation systems.

The ensemble of data assimilations (EDA) method is employed to evaluate the expected impact of a wide range of potential future constellations of passive microwave (MW) sounders on small satellites for global numerical weather prediction. Such constellations are expected to become an important component of the future observing system, complementing a backbone of larger, high-performance platforms and allowing unprecedented temporal sampling. Twelve constellations are investigated to probe key aspects of the constellation design: number of satellites (ranging from 8 to 20), types of orbits (sun synchronous, mid-inclination), and channel complement (183 GHz humidity-sounding capabilities only versus combining these with temperature-sounding capability around 50 GHz). The small-satellite data and accompanying errors are simulated, using an all-sky framework, and the relative benefits from adding the different constellations to the observing system are measured by the reduction in the spread of the ensemble members, reflecting improvement to the forecast uncertainties. Results suggest continued benefit from adding MW-sounding observations beyond currently available observations. The EDA spread reduction for different variables (e.g., wind and geopotential height) and different pressure levels is already significant using the smallest constellation considered, adding eight satellites to a four-satellite baseline of existing MW-sounding instruments. The EDA spread reduction continues as further observations are added, although the rate of reduction slows significantly, especially where scenarios use only humidity-sounding channels. Use of temperature-sounding channels gives significant added benefit over humidity sounding only, generally 2-3 and 1.5-2 times larger in the extratropics and Tropics respectively. Different behaviour in the relative magnitudes and rates of EDA spread reduction is seen between the extratropics and Tropics, which can be attributed to different physical processes and different error growth. This study was unable to provide conclusive results on the choice of polar orbits only, mid-inclination orbits only, or a mix of orbit types.

Current and future vegetation imaging spectroscopy satellites will bring a new data stream of information, of high scientific value to refine existing remote sensing products, and develop new ones. The sensors on board ESA's Fluorescence Explorer (FLEX) will cover the entire 500-780 nm range, designed to track the photosynthetic energy partitioning based on the key pigment players in the light reactions. To quantify the actual photosynthetic efficiency unambiguously, the dynamic pigment absorption behavior is crucial to complement the fluorescence information. An important role is given by the xanthophylls, regulating the non -photosynthetic quenching (NPQ) behavior which affects the 500-600 nm range. Therefore, this work focused on the development of a non -negative least squares (NNLS) spectral unmixing algorithm for reflectance (500-780 nm) retrieving the effective absorbance of individual pigments, i.e., Chlorophyll (Chl) a and b, beta -Carotene and xanthophylls. The NNLS fitting was applied to fit the total effective absorbance at leaf level, linearly composed of pigment and background absorption coefficient spectra. The model succeeded to obtain spectral fitting errors generally below 20% across the 500-780 nm range. Further, we focused on the further use of the effective absorbance by Chlorophyll a to calculate the absorbed photosynthetically active radiation or APAR Chl a. This product was combined with the total emitted fluorescence flux (670-780 nm), expressed in photon flux units, to obtain the fluorescence quantum efficiency (FQE), capturing the stress -related fluorescence quenching in the light reactions. Finally, we applied the leaf -based algorithm to foreseen FLEX image products simulated by the FLEX End -to -End Scene Generator. We were able to retrieve APAR Chl a with a RMSE of 85.5 mu mol m- 2 s- 1 (NNLS with additional upper fitting constraints) and 158.2 mu mol m- 2 s- 1 (regular NNLS), and FQE retrievals could be obtained with an R2 of 0.93 and 0.90, respectively. While the subtle xanthophyll absorption could be meaningfully fitted at the leaf scale, further improvements to the algorithms and an understanding of the physiological mechanisms are needed to deal with the complexity at larger scales. Given several challenges to be overcome, the proposed bottom -up strategy using specific pigment absorbance unmixing for (imaging) spectroscopy demonstrates the ongoing developments to complement the fluorescence product, with the aim to provide an unambiguous estimate on the actual carbon sequestration of vegetation.

A physics-based microwave emission model for layered vegetation (MEMLV) is developed to simulate the vegetation optical depth (VOD) and scattering albedo of coniferous forests from P- to Ka-band (0.4 GHz-37 GHz). This study aims to use physics-based forward modeling to guide and support multifrequency VOD retrieval. The MEMLV consists of three major components: 1) the single-layer discrete scatter model (SL-DSM) that calculates the VOD and single scattering albedo of a single layer; 2) a new Tree Structure Model (TSM) that represents forests by stratified multilayer media; and 3) the Two-Stream microwave emission model (2S-MEM) that combines SL-DSM and TSM to calculate the brightness temperature of forests and their corresponding effective VOD, tau(eff), and effective scattering albedo, omega(eff). Simulation of an exemplary coniferous forest shows that tau(eff) increases as frequency increases until reaching saturation at K-band. Meanwhile, omega(eff) increases rapidly at low frequencies until reaching a peak at S-band, then decreases until reaching X-band, and finally increases monotonically as frequency increases. Notably, omega(eff) is negligible at P-band for most cases. Sensitivity analyses demonstrate the saturation of tau(eff) when canopy height is substantial, and the decreasing trend of omega(eff) as canopy height or the areal fraction of canopy gap increases. The MEMLV has the potential to improve the parameterization of retrieval algorithms and to enhance the understanding of the retrieved VOD over a wide frequency range.

During the tandem phase of Sentinel-3A and Sentinel-3B in summer 2018 the Ocean and Land Colour Imager (OLCI) mounted on the Sentinel-3B satellite was reprogrammed to mimics ESA's eighth Earth Explorer, the FLuorescence EXplorer (FLEX). The OLCI in FLEX configuration (OLCI-FLEX) had 45 spectral bands between 500 and 792 nm. The new data set with high-spectral-resolution measurements (bandwidth: 1.7-3.7 nm) serves as preparation for the FLEX mission. Spatially co-registered measurements of both instruments are used for the atmospheric correction and the retrieval of surface parameters, e.g. the fluorescence or the leaf area index. For such combined products, it is essential that both instruments are radiometrically consistent. We developed a transfer function to compare radiance measurements from different optical sensors and to monitor their consistency.In the presented study, the transfer function shifts information gained from high-resolution "FLEX-mode" settings to information convolved with the spectral response of the normal (lower) spectral resolution of the OLCI sensor. The resulting reconstructed low-resolution radiance is representative of the high-resolution data (OLCI-FLEX), and it can be compared with the measured low-resolution radiance (OLCI-A measurements). This difference is used to quantify systematic differences between the instruments. Applying the transfer function, we could show that OLCI-A is about 2 % brighter than OLCI-FLEX for most bands of the OLCI-FLEX spectral domain. At the longer wavelengths (> 770 nm) OLCI-A is about 5 % darker. Sensitivity studies showed that the parameters affecting the quality of the comparison of OLCI-A and OLCI-FLEX with the transfer function are mainly the surface reflectance and secondarily the aerosol composition. However, the aerosol composition can be simplified as long as it is treated consistently in all steps in the transfer function.Generally, the transfer function enables direct comparison of instruments with different spectral responses even with different observation geometries or different levels of observation. The method is sensitive to measurement biases and errors resulting from the processing. One application could be the quality control of the FLEX mission; presently it is also useful for the quality control of the OLCI-FLEX data.

Future space missions of Earth Observation propose to use night-time remote sensing to target new observables that represent a long-standing observational gap or address urgent scientific and societal research questions. The key challenges to enable such missions are related to the capability of the payload to measure very low radiances and with an extremely large dynamic range, ranging from night-time radiances to day-time radiances. In this paper, we derive the flow-down for such missions: from the scientific objectives, we derive the measurement requirements, on which we perform trade-offs based on mission analysis and radiometric budget. Subsequently, we identify potential detectors for these missions, and we identify the gaps in current state-of-the-art technology.

The detection of solar induced chlorophyll fluorescence (SIF) in the field with spectrometers is based on the depth of the solar Fraunhofer or oxygen absorption lines in the upwelling radiance compared to that in the downwelling irradiance. This relative depth enables the differentiation of SIF from the reflected radiation. Recent studies have shown that if oxygen bands are used to retrieve SIF from tower-based measurements, then atmospheric correction is required. This study presents a band shape fitting (BSF) approach to retrieve both the relative optical path length (deepening) and SIF (infilling) from field measurements at the same time, using information in the measured spectral shape of the O2 feature. This approach is an alternative to using radiative transfer process models for estimating atmospheric transmittance. The method was applied to measurements taken from 100 m elevation above a forest, yielding plausible results for SIF in the O2A and O2B bands. The sensitivity to combined atmospheric and instrument characteristics prohibits application at much greater distances from the surface.

The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), together with NASA’s Soil Moisture Active Passive (SMAP) mission, is providing a wealth of information to the user community for a wide range of applications. Although both missions are still operational, they have significantly exceeded their design life time. For this reason, ESA is looking at future mission concepts, which would adequately address the requirements of the passive L-band community beyond SMOS and SMAP. This article proposes one mission concept, TriHex, which has been found capable of achieving high spatial resolution, radiometric resolution, and accuracy, approaching the user needs. This is possible by the combination of aperture synthesis, formation flying, the use of general circular orbits, and alias-free imaging.

The upcoming Fluorescence Explorer (FLEX) satellite mission aims to provide high quality radiometric measurements for subsequent retrieval of sun-induced chlorophyll fluorescence (SIF). The combination of SIF with other observations stemming from the FLEX/Sentinel-3 tandem mission holds the potential to assess complex ecosystem processes. The calibration and validation (cal/val) of these radiometric measurements and derived products are central but challenging components of the mission. This contribution outlines strategies for the assessment of in situ radiometric measurements and retrieved SIF. We demonstrate how in situ spectrometer measurements can be analysed in terms of radiometric, spectral and spatial uncertainties. The analysis of more than 200 k spectra yields an average bias between two radiometric measurements by two individual spectrometers of 8%, with a larger variability in measurements of downwelling radiance (25%) compared to upwelling radiance (6%). Spectral shifts in the spectrometer relevant for SIF retrievals are consistently below 1 spectral pixel (up to 0.75). Found spectral shifts appear to be mostly dependent on temperature (as measured by a temperature probe in the instrument). Retrieved SIF shows a low variability of 1.8% compared with a noise reduced SIF estimate based on APAR. A combination of airborne imaging and in situ non-imaging fluorescence spectroscopy highlights the importance of a homogenous sampling surface and holds the potential to further uncover SIF retrieval issues as here shown for early evening acquisitions. Our experiments clearly indicate the need for careful site selection, measurement protocols, as well as the need for harmonized processing. This work thus contributes to guiding cal/val activities for the upcoming FLEX mission.

In this paper we present L-band data usage for Numerical Weather Prediction applications and Emergency Services at the European Centre for Medium-Range Weather Forecasts (ECMWF).

Abstract Lightning‐caused wildfires are a significant contributor to burned areas, with lightning ignitions remaining one of the most unpredictable aspects of the fire environment. There is a clear connection between fuel moisture and the probability of ignition; however, the mechanisms are poorly understood and predictive methods are underdeveloped. Establishing a lightning–ignition relationship would be useful in developing a model that would complement early warning systems designed for fire control and prevention. A machine learning (ML) approach was used to define a predictive model for wildfire ignition based on lightning forecasts and environmental conditions. Three different binary classifiers were adopted: a decision tree, an AdaBoost and a Random Forest, showing promising results, with both ensemble methods (Random Forest and AdaBoost) exhibiting an out‐of‐sample accuracy of 78%. Data provided by a Western Australia wildfire database allowed a comprehensive verification on over 145 lightning‐ignited wildfires in regions of Australia during 2016. This highlighted that in a minimum of 71% of the cases the ML models correctly predicted the occurrence of an ignition when a fire was actually initiated. The super‐learner developed is planned to be used in an operational context to the enhance information connected to fire management.

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

In the framework of its Earth Observation Programmes the European Space Agency (ESA) carries out ground based and airborne campaigns to support geophysical algorithm developments, calibration/validation activities, simulation of future space-borne earth observation missions, as well as application developments related to remote sensing of the atmosphere, land, oceans, solid earth and cryosphere. ESA has conducted over 150 airborne and ground based measurement campaigns in the last 37 years, of which more than 80 were carried out since 2005. During this period a large number of campaigns have supported the validation of ESA’s satellite missions including for example SMOS and CryoSat. Ongoing activities are focusing on e.g. Sentinel-5Precursor and the preparation of upcoming Earth Explorer missions such as BIOMASS, FLEX, and FORUM. These validation campaigns aim to provide fundamental information about the confidence of data products and their required uncertainties One challenge in this context is a comprehensive understanding and characterization of measurement uncertainty of the validation dataset and the spatial and temporal support or representativity of these. We will provide an overview of applied strategies to tackle these aspects for existing satellite missions and outline concepts for future missions, and how these integrate into broader earth observation science strategies. In addition, we will highlight recent activities and outline planned activities for the coming years.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is of growing interest for the scientific community due to the inherent link of SIF with vegetation photosynthetic activity. An increasing number of in situ and airborne fluorescence spectrometers has been deployed worldwide to advance the understanding and usage of SIF for ecosystem studies. Particularly, a number of sites has been instrumented with the FloX (J&B Hyperspectral Devices, Germany), an automated instrument that houses two high resolution spectrometers covering the visible and near infrared spectral regions, one specifically optimized for fluorescence retrieval, the other for plant trait estimation. In this contribution we explore the feasibility to consistently retrieve plant traits and SIF from canopy level FloX measurements through the numerical inversion of a light version of the SCOPE model. The optimization approach was specifically adapted to work with the high- frequency time series produced by the FloX. In this context, a strategy for optimal retrieval of plant traits at daily scale is discussed, together with the implementation of an emulator of the radiative transfer model in the retrieval scheme. The retrieval strategy was applied to site measurements across Europe and the US that span a variety of natural and agricultural ecosystems. The full spectrum of canopy SIF, the fluorescence quantum efficiency, and main plant traits controlling light absorption and reabsorption were retrieved concurrently and evaluated over the growing season in comparison with site-specific ancillary data. Improvements and challenges of this method compared to other retrievals are discussed, together with the potential of applying a similar retrieval scheme to airborne datasets acquired with e.g. the HyPlant sensor, or the reconfigured “FLEX mode” data acquired with the recently launched Sentinel-3B during its commissioning phase.

We report a unique multiyear L-band microwave radiometry dataset collected at the Maqu site on the eastern Tibetan Plateau and demonstrate its utilities in advancing our understandings of microwave observations of land surface processes. The presented dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. Measurement cases in winter, pre-monsoon, monsoon and post-monsoon periods are presented.