The European Space Agency's (ESA) BIOMASS mission is a pioneering Earth observation satellite mission launched on April 29, 2025. Utilizing a P-band synthetic aperture radar (SAR), the objective of BIOMASS is to deliver estimates of above-ground forest biomass, forest height (FH), and forest disturbance (FD), with unprecedented accuracy. The mission's primary scientific goal is to quantify the distribution and changes in forest biomass, thereby reducing uncertainties in carbon flux estimates and informing climate models. The satellite's advanced instrumentation and innovative approach allow it to penetrate dense forest canopies, capturing data even in challenging environments. The mission will operate in two distinct phases: the tomographic phase and the interferometric phase, which will support polarimetric interferometric SAR (Pol-InSAR) and tomographic SAR (TomoSAR) processing. Additionally, BIOMASS will provide valuable observational data for ice sheets, deserts, the ionosphere, below canopy topography, and other domains.

The Copernicus Imaging Microwave Radiometer (CIMR) is one of the six Copernicus Expansion Missions currently being implemented by the European Space Agency and the European Commission. The mission is specifically designed to provide measurement evidence in support of developing, implementing, and monitoring the impact of the European Integrated Policy for the Arctic. CIMR provides microwave imaging radiometry measurements at low frequency (L-, C-, X-, K- and Ka-band) with relatively high spatial resolution and high radiometric fidelity. In this paper, the current plan for the design, development and validation of the ESA CIMR Level-2 products is described.

BIOMASS is new ESA Earth Explorer, scheduled for launch in 2024. It will be the first P-band spaceborne Synthetic Aperture Radar (SAR), featuring full polarimetry, repeat pass interferometry and tomography. Its main objective is global routine monitoring of forested areas, its secondary objectives include exploration of glacier dynamics, subsurface geology and sub-canopy terrain topography. In order to correctly retrieve information, interferometric calibration has to address main disturbances affecting repeat pass P-band SAR: ionosphere, baseline errors and troposphere. In this paper we present the scientific design of BIOMASS interferometric calibration processor, detailing the state-of-the-art correction steps implemented. Some preliminary results obtained by processing simulated acquisitions are illustrated to motivate the proposed techniques.

ESA BIOMASS will be the first spaceborne P-band SAR, and is designed to produce annual, near-global maps of forest biomass. Biomass-independent scene properties can be mitigated by interferometric pre-processing of the acquisitions, after which backscatter is inverted by a model to estimate biomass, relying on some external reference biomass. The model is simple enough to be invertible, while still representing the spatiotemporal variability of parameters in a global scenario. However, it requires a reliable, global reference biomass dataset, with enough coverage to reflect the variability of the chosen model. The global algorithm is also computationally efficient, so can be deployed in the ground segment. In this paper we briefly discuss the design of the global estimation algorithm for BIOMASS and propose a reference biomass dataset for the algorithm. Then, we discuss the robustness of the proposed algorithm using some reference biomass data for three different parameter variability setups.

In order to help scientists in the above ground biomass community and to support the science behind the upcoming BIOMASS satellite missions, ESA is building a cloud-computing platform called Multi-Mission Algorithm and Analysis Platform (MAAP). The MAAP is jointly developed and implemented with NASA and will include not only data (satellite, airborne, in situ data and products), but also high performing computing capabilities and tools and algorithms developed to support this specific field of research. To best ensure that users are able to collaborate across the platform and to access needed resources, the MAAP requires all data, algorithms, and software to conform to open access and open-source policies. As one such example of best collaborative and open-source practices, the BIOMASS data processing algorithms are developed on MAAP under the umbrella of an open-source scientific software project called BioPAL. In addition to aiding researchers, the MAAP will focus on sharing data, science algorithms and compute resources in order to foster and accelerate scientific research.

The BIOMASS DEM Product Prototype Processor (BIO-DEMPP) is being developed in the frame of ESA's Earth Explorer BIOMASS mission. The prototype includes a complete interferometric SAR chain, from the stack co-registration until the mosaicking of the derived height products (Digital Elevation and Digital Terrain Models). This paper presents an overview of the BIODEMPP architectural design and its validation strategy, as well as first results obtained with simulated BIOMASS-like data.

Scheduled for launch in 2023, ESA's seventh Earth Explorer Mission, BIOMASS, will carry the first P-band synthetic aperture radar (SAR) to be flown in space, to gather fully polarimetric acquisitions over forested areas worldwide in interferometric and tomographic modes. This paper presents the algorithms developed to estimate biophysical parameters from BIOMASS measurements and their implementation in the BIOMASS level 2 (L2) prototype processor. The L2 processor will generate global maps of forest Above Ground Biomass (AGB), Forest Height (FH), Forest disturbance (FD). Accurate generation of these products requires the L2 processor to be closely inter-linked with the BIOMASS interferometric processor, in order to produce phase-calibrated interferometric stacks, retrieve sub-canopy terrain topography, and generate a 3D representation of forest structure by use of SAR tomography. AGB estimation results are here shown using BIOMASS-like acquisitions derived from campaign data acquired over six tropical forests in South America and Equatorial Africa.

The TomoSense experiment comprises campaign and research activities in support of future Synthetic Aperture Radar (SAR) mission concepts at P-, L-, and C-band by the European Space Agency (ESA). The research is intended to provide a quantitative basis for the evaluation of single-pass interferometry over temperate forests at L- and C-band and investigate potential synergies between C-band convoy mission concepts and future P- and L-band missions. SAR acquisitions include P- L-, and C-band data acquired at the Eifel National Park in Germany by flying approximately 25 trajectories to provide tomographic imaging capabilities. Land C-band data were acquired by simultaneously flying two aircraft to gather bistatic data with varying interferometric baselines. Field activities include forest census (dbh, tree height and species) at 80 plots and Terrestrial Laser Scanning (TLS). The dataset is complemented by small-footprint Airborne Lidar Scanning (ALS) and derived products. Preliminary results are here shown relative to polarimetric tomography at P-band and L-band imaging.

This paper describes the rationale and development of the estimation techniques for the level-2 data products of the European Space Agency's 7th Earth Explorer BIOMASS mission. BIOMASS is planned for launch in 2023 and will carry the first-ever P-band synthetic aperture radar (SAR) onboard a satellite. It has been designed to produce consistent global maps of the Earth's forests during a nominal five-year lifetime. Fully polarimetric SAR data will be collected and the satellite orbit will be selected for repeat-pass interferometry and tomography in separated mission phases. Mission requirements call for three level-2 data products: above-ground biomass, forest height and forest disturbance. The paper also discusses the expected limitations of the estimation techniques and remaining problems to be addressed.

Microwave brightness temperature (Tb) and backscatter (σ0) observations from the Soil Moisture and Ocean Salinity (SMOS) mission, Soil Moisture Active Passive (SMAP) mission, Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Advanced Scatterometer (ASCAT) instrument provide a wealth of operationally available satellite data for soil moisture retrieval and data assimilation purposes. To assist the synergistic and efficient use of such techniques, the soil moisture information content in the respective Level-1 observations needs to be determined. Within this context, we compare L-, C-, and X-band Tb and σ0signatures of the above-named sensors to in situ SM observations in Spain, Australia, and USA. We find that L-band Tb observations from SMOS and SMAP show the best overall performance given the considered diagnostics (correlation, anomaly correlation, and sensitivity), while all sensors provide significant soil moisture information. This finding is consistent across the analyzed incidence angle range for SMOS (25°-60°). The results are discussed with respect to physical processes governing the dynamics of Tb and σ0, noting dependencies on vegetation seasonality and land surface temperature.

The potential of interferometric synthetic aperture radar (InSAR) heights from TanDEM-X for vegetation canopy height and aboveground biomass (AGB) estimation has long been recognized. Penetration of X-band into the canopy affects these estimations. Thus, the canopy height and AGB retrieval from InSAR are typically biased and cannot be compared directly to estimates from other data sources. The objective of this letter was to apply a penetration depth model to compensate for height biases in TanDEM-X InSAR heights. The resulting canopy height estimates are subsequently converted to AGB estimates using regression models. The uncorrected InSAR heights of the forest canopy are biased due to the penetration of the signal into the canopy and differ substantially to light detection and ranging (LiDAR) canopy height estimates. The application of the penetration depth compensation results in unbiased forest canopy height estimates and AGB regression models that are comparable between InSAR and LiDAR. These results indicate that TanDEM-X InSAR and LiDAR technologies can be used to estimate AGB in complex tropical forests suggesting a synergistic use of these fundamentally different observation concepts.

A new processing technique, i.e., ground cancellation, which removes the ground signal from a pair of interferometric synthetic aperture radar (SAR) images, is used to emphasize the response from above-ground targets. This technique is of particular interest when studying forest canopies using low-frequency signals able to reach the underlying ground, in which case the portion of the signal coming from the ground interferes with the recovery of information about the vegetation. We demonstrate that the power in ground-canceled P-band HV SAR data gives significantly higher correlations with above-ground biomass (AGB) than the interferometric images considered separately. In addition, a significant increase in the sensitivity of backscatter to AGB is observed. Ground-canceled power may then be modeled or regressed to estimate AGB; these possibilities are not discussed here as they will be the topic of forthcoming publications. The effectiveness of this technique is proven through simulations and analysis of real data gathered on tropical forests. The stability of the technique is analyzed under the digital terrain model and baseline control errors, and compensation strategies for these errors are presented.

The ESA BIOMASS mission will be the 7th Earth Explorer measuring the above-ground biomass (AGB) in the world's forests. The current ESA Level-2 (L2) implementation study focuses on defining and implementing the main algorithms for forest parameter retrieval from BIOMASS data. After the first year of L2 study innovative results were achieved: the development of ground cancellation, in particular, has proved to be huge value, since it removes from the data the effects of environmental variability and contributions unrelated to the forest carried in the ground scattering. In this paper the current processor implementation and validation activities of the L2 team will be described.

This paper provides a compact overview of Harmony, an Earth Explorer 10 mission candidate dedicated to the observation of dynamic deformations of ice, solid earth and ocean surfaces. Harmony consists of two receive-only small Synthetic Aperture Radar (SAR) satellites using Sentinel-1D as illuminator, which will alternate close formation phases, dedicated to single-pass cross-track interferometry, with StereoSAR phases dedicated to the study of ocean surface motion and 3-D land surface deformations.

In the context of innovative sensors and a changing ground segments, the concept of ESA-NASA multi-Mission Analysis Platform dedicated to the NISAR, GEDI and Biomass missions is proposed. This analysis platform will be a virtual open and collaborative environment. The goal is to bring together data centre (Earth Observation and non-Earth Observation data), computing resources and hosted processing, collaborative tools (processing tools, data mining tools, user tools, ...), concurrent design and test bench functions, application shops and market place functionalities, accounting tools to manage resource utilisation, communication tools (social network) and documentation.

The ESA BIOMASS mission will be the first spaceborne mission specifically dedicated to the study of forests. In this framework, ESA is running the “Level-2 implementation study”, focused on defining and implementing the main algorithms for forest parameters retrieval from BIOMASS data. During the first year, the science group involved in the study has carried out R&D activities in order to identify the most viable solutions. In this paper, an overview of the concepts and techniques behind the development of the BIOMASS L2-prototype processor are described, with a focus on each of the main mission products: biomass, forest height and forest disturbance map.

Low-frequency microwave brightness temperature and backscatter observations are sensitive to surface soil moisture, a key variable for applications like numerical weather prediction, drought monitoring and climate modeling. With ESA's Soil Moisture and Ocean Salinity (SMOS) mission and NASA's Soil Moisture Active Passive (SMAP) mission, two L-band radiometers dedicated to soil moisture monitoring are currently in orbit. In addition, operational soil moisture products exist based on EUMETSAT MetOp C-band advanced scatterometer (ASCAT) and JAXA GCOM-W multifrequency radiometer (AMSR2) data. This study aims to assess the information content of Level-l data from active and passive microwave sensors for soil moisture estimation. Specific focus lies on the comparison between frequencies (L-band, C-band, X-band) and sensor types (active and passive) as well as the added value of multi-angular brightness temperature observations (e.g. from SMOS) with respect to fixed incidence angle observations. The study will provide insights into the suitability of different sensor types for soil moisture estimation irrespective of the individual Level-2 retrieval algorithm specifics. The results shall assist the definition of new observation concepts and the identification of synergies between planned or existing satellite missions.

The objective of this paper is to provide a better understanding of tomographic capabilities to estimate above ground biomass (AGB) in dense forested areas at P-band. The analysis is carried out on airborne data acquired over sites in French Guyana and in Gabon during the ESA campaigns TropiSAR and AfriSAR 2015, respectively. Over both sites, P-band tomography allows us to retrieve the vertical structure of the forest, to better characterize the ground and/or volume scattering mechanisms and to provide a unique solution for the AGB retrieval over the full range of biomass. The relationship between AGB and tomography data was found to be highly similar for forests across continents and sites: Paracou (French Guiana), Lope, Rabi and Mondah (Gabon). The developed metrics derived from the tomographic data have been found highly correlated to reference in situ AGB estimates (R2=0.85) and the root mean square error was 16% (for AGB ranging from 0 to 500 t/ha). These results have strong implications for the tomographic phase of the BIOMASS spaceborne mission.

Earth Explorers are the backbone of the science and research element of ESA's Living Planet Programme, providing an important contribution to the understanding of the Earth system. Following a User Consultation Meeting on 5-6 March 2013, the Earth Science Advisory Committee (ESAC) has recommended implementing Biomass as the 7th Earth Explorer Mission within the frame of the ESA Earth Observation Envelope Programme. This paper will give an overview of the overarching mission requirements, the satellite system and its payload status at the end of Phase-B.

The concept of a passive receiving small satellite flying in formation with an active satellite for bistatic Synthetic Aperture Radar (SAR) imaging has been the subject of numerous national and international studies. In 2013, ESA received an offer from the National Commission for Space Activities of Argentina (CONAE) to launch a small satellite with the SAOCOM-1b satellite, and to collaborate during the mission exploitation phase. The main SAOCOM-1b payload is an L-band polarimetric SAR instrument. This offer, provides a unique opportunity to the Earth observation science community to demonstrate at global scale the science capabilities of an L-band active-passive satellite constellation through dedicated in-orbit experiments and to secure valuable feedback within a short time frame on the scientific potential of L-band SAR mission constellations.

The current paper aims to provide a brief overview of the ESA activities relevant to Polarimetric SAR missions mission development and exploitation, present achievements and discuss future opportunities for research.

Earth Explorers are the backbone of the science and research element of European Space Agency (ESA)'s Living Planet Programme, providing an important contribution to the understanding of the Earth system. Following the User Consultation Meeting held in Graz, Austria on 5-6 March 2013, the ESA Program Board has decided implementing Biomass as the 7thEarth Explorer Mission within the frame of the ESA Earth Observation Envelope Programme. This paper will give an overview of the satellite system and its payload. The system technical description presented here is based on the results of the work performed during parallel Phase A system studies by two industrial consortia led by Airbus Space and Defence Ltd. and Thales Alenia Space Italy. Two implementation concepts (respectively A and B) are described and provide viable options capable of meeting the mission requirements.

The 7-th ESA Earth Explorer, BIOMASS is a synthetic aperture radar (SAR) which will collect data from employing a multiple baseline orbit during the initial phase of its lifetime. This data can be used for tomographic SAR (TomoSAR) processing resulting in a vertical resolution of about 20 m, sufficient to decompose the backscatter from most tropical forests into two to three layers. A recent study using airborne data from the TropiSAR campaign at the site of Paracou, French Guiana, showed that this information significantly improves the retrieval of forest above-ground biomass (AGB), resulting in an accuracy of about 10% of AGB at a resolution of 1.5-ha. In this paper, we generalize this result, by applying the same algorithm to the Nouragues test site in central French Guiana. This site is characterized by a hilly terrain and an AGB ranging from 150 to 600 t/ha. The relationship between AGB and TomoSAR data at Nouragues was found to be highly similar to the one observed at Paracou. We found that the best correlation between the backscatter signal and AGB is held in the upper canopy layer (i.e. 20-40 m). Cross validation using training plots from Nouragues and validation plots from Paracou, and vice versa, resulted in an accuracy of about 16%-18% of AGB using 1-ha plots. This result suggests that the TomoSAR AGB retrieval method is generalizable to other study sites. In addition we show that, TomoSAR can be used to estimate the canopy height with an error of less than 4 m with forest height ranging from 20 m-40 m.

The next ESA Earth Explorer Core Mission BIOMASS is envisaged to collect multiple baselines on selected areas during the initial phase of its lifetime. Such data will allow to image the vertical structure of the vegetation layer to within a vertical resolution of about 20 m, sufficient to decompose the backscattered power from a tropical forest into two-three layers. The information provided by tomography has recently been shown to be strictly linked to above ground biomass (AGB) in tropical forest, therefore providing a valuable tool for ABG estimation. The aim of this paper is to present a bird-eye overview of BIOMASS Tomography, along with the main experimental results from airborne campaigns flown during Phase-A BIOMASS activities.

In the frame of the Biomass mission activities, this paper presents the challenges and recent results in the retrieval of forest biomass from polarimetric (PolSAR) and interferometric (PolInSAR) P-band SAR data. During the mission Phase A, critical issues in the biomass retrieval algorithms in boreal and tropical forests have been identified and addressed. In boreal forest, multi polarization backscatter data can be used to mitigate much of the variability due to environment effects. In high biomass tropical forest, because of the low sensitivity of the backscatter to biomass, appropriate correction methods were developed to mitigate the disturbing effects. Also to enhance the retrieval results, a combination of PolSAR and PolInSAR methods was proposed.

The European Space Agency (ESA) is preparing candidates for the next Earth Explorer Core mission with the aim to select the 7th Earth Explorer mission to be launched towards the end of this decade. Earth Explorers are the backbone of the science and research element of ESA's Living Planet Programme, providing an important contribution to the global endeavor of understanding the Earth's system, particularly in view of global climate change. Six candidate missions were selected and investigated in the preliminary feasibility studies (Phase 0). A further down-selection was made after the User Consultation Meeting held in Lisbon, Portugal, in January 2009. Three candidate missions (Biomass, CoReH2O and PREMIER) were selected for further feasibility investigations (phase A). Each of the candidate missions has been defined in detail through two parallel and competing industrial studies and many complementary science and technology studies, aiming to the final down-selection in 2013, followed by the mission implementation with a planned launch in 2019. This paper will give an overview of the observation requirements, satellite system, payload and general status of the Biomass mission.

The mapping and monitoring of sea ice regions represents a key application area for spaceborne synthetic aperture radar (SAR) missions. The European Space Agency (ESA) is under-taking the development of Sentinel-1, an imaging radar mission at C-Band for the continuation of SAR operational applications. Monitoring sea ice zones and the arctic environment is one of the major application areas supported by Sentinel-1. Other spaceborne SAR missions are being developed or considered by a number of space organizations. The study described herein focuses on evaluating the contribution of SAR constellations to operational and scientific monitoring of sea ice conditions through the analysis of multi-parameter SAR datasets (airborne, spaceborne) and through the development and exploitation of a simulation tool able to predict sea ice radar signatures as a function of the ice type and condition and of sensor parameters.

The BIOMASS mission is designed to provide unique information on the biomass in the world's forests at spatial and temporal resolutions suitable for characterizing their dynamics and their contribution to carbon cycle estimates. To achieve this it combines biomass estimates from direct inversion of polarimetric backscattering coefficients with Pol-InSAR forest height estimates. The mission will also support important secondary objectives, including sub-surface imaging in arid zones, production of a bare-earth DTM and ice applications, and is optimized to be robust against environmental and ionospheric disturbances.

The BIOMASS mission is designed to map the full range of the world's above-ground forest biomass, for the needs of national scale inventory and global carbon flux calculations. This objective is achieved with advanced P-band SAR techniques. The P-band biomass measurement concept was based on previous work over the past two decades. During the preparatory phase, new campaigns have been conducted to address critical issues on the biomass retrieval algorithms, over tropical and boreal forests. The collected datasets comprise accurate and complete sets of in situ data and advanced P-band SAR data. This paper presents the retrieval algorithms developed using the collected datasets.

The 23 papers in this special issue are grouped into the following headings: 1) Instrumentation, Calibration, Validation, and Processing; 2) Ocean Applications; 3) Land Applications (soil moisture, vegetation, and land freeze/thaw cycles); 4) Polar Snow and Ice Applications.

The European Space Agency (ESA) released a Call for Proposals for the next Earth Explorer Core Mission in March 2005, with the aim to select the 7thEarth Explorer (EE-7) mission for launch in the next decade. Twenty-four proposals were received and subject to scientific and technical assessment. Six candidate missions were selected and further investigated in the preliminary feasibility studies (Phase 0). A further down-selection was made after the User Consultation Meeting held in Lisbon, Portugal in January 2009. Three candidate missions were selected for further feasibility investigations (phase A). Each of the candidate missions is now being defined in detail through two parallel and competing industrial studies and many complementary science and technology studies, aiming to the final down-selection in 2011/12, followed by the mission implementation with a planned launch in the 2016/17 timeframe.

The European Space Agency (ESA) released a Call for Proposals for the next Earth Explorer Core Mission in March 2005, with the aim to select the 7thEarth Explorer (EE-7) mission for launch in the next decade. Twenty-four proposals were received and subject to scientific and technical assessment. Six candidate missions were selected and further investigated in the preliminary feasibility studies (Phase 0). One of these missions is BIOMASS, which has recently been selected to proceed to Phase-A. BIOMASS is a response to the urgent need for greatly improved mapping of global biomass and the lack of any current space systems capable of addressing this need.

Recently Triple Collocation (TC) was adopted for soil moisture application. Results from a first application indicated that the method could be useful to estimate global error patterns. Here we test the method with new data sets. The results show that the method is robust and that it allows to derive objective error estimates.

The scatterometers onboard the European Remote Sensing satellites (ERS-1 & ERS-2) and the METeorological OPerational satellite (METOP) have been shown to be useful for surface soil moisture retrieval using the so-called TU-Wien change detection method. This paper presents an improved soil moisture retrieval algorithm based on the existing TU-Wien method but with new parameterization as well as a series of modifications. The new algorithm, WAter Retrieval Package 5 (WARP5), copes with some limitations identified in the earlier method WARP4 and provides the possibility of migrating soil moisture retrieval from ERS-SCAT to METOP-ASCAT data. The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters. Cross-comparisons of WARP4 and WARP5 data sets with the Oklahoma Mesonetinsituobservations and also with European Centre of Medium Range Weather Forecast (ECMWF) ReAnalysis (ERA-Interim) global modeled data show that the new algorithm has a better performance and effectively corrects retrieval errors in certain areas.

Triple collocation error estimation is a powerful tool to simultaneously estimate the error structure and calibrate a set of independent observations. In this study, we use this technique to estimate the errors of a passive microwave (TRMM-TMI) derived, an active microwave (ERS-2 scatterometer) derived and a modelled (ERA-Interim reanalysis) soil moisture data sets.

Studies of the Earth's land surface involving scatterometers are becoming an increasingly important application field of microwave remote sensing. Similarly to scatterometer observations of ocean waves, the backscattering coefficient (sigma0) response of land surfaces depends on both the incidence and azimuth angle under which the observations are made. In order to retrieve geophysical parameters from scatterometer data, it is necessary to account for azimuthal-modulation effects of the backscattered signal. In the present study, this paper localizes the regions affected by a strong azimuthal signal dependence when observed with the European Remote Sensing Satellite Scatterometer and the SeaWinds Scatterometer on QuikSCAT (QSCAT). The possible physical reasons for the azimuthal effects, relating the very detailed QSCAT azimuthal response to the spatial orientation of special topographic features and land cover within the sensor footprint, were then discussed. Different methods for normalizing the backscattering coefficient with respect of observation azimuth angle were also proposed and evaluated. First, the mean local incidence angle of the sensor footprint using the shuttle radar topography mission digital elevation model (DEM) were modeled and concluded that the resolution of the DEM is too coarse to characterize most of the observed azimuthal effects. A more effective way of normalizing the backscatter with respect to azimuth is then found to be by using historical backscatter observations to statistically determine the expected backscatter at each observation azimuth and incidence angle as well as time of the year. The efficiency of this method is limited to the availability of past measurements for each location on the Earth

ENVISAT's ASAR Global Monitoring mode offers exciting capabilities for monitoring highly dynamic processes of the hydrosphere. Based on examples from different ecoregions (Africa, Siberia, Spain) we highlight the potential of this sensor system for monitoring wetlands and soil moisture. The hydrosphere is often called the sphere as it includes all the earth's water that is found in the oceans, streams, lakes, the soil, groundwater, and in the air. Water is continually cycled between these various reservoirs through processes such as precipitation, melting, runoff, infiltration, and evaporation. Particularly these exchange processes and the highly dynamic water reservoirs (soil moisture, snow, streams, sea-ice) hold the key to our understanding of how the hydrosphere will react to global warming and increasing human pressure on the environment. Effective monitoring of these highly dynamic processes should therefore be a priority in earth observation. Unfortunately, we currently lack earth observation techniques which allow daily monitoring of the hydrosphere at a high spatial resolution ( 1000 km), but are severely hampered by cloud cover and illumination conditions. Therefore, the effective temporal sampling interval is highly irregular, necessitating multi-temporal composting over time periods of one week or longer. Microwave systems are not restricted in this respect and, in addition, exhibit a high sensitivity to water. So while in principle they appear to be ideally suited, system design limitations have so far prevented widespread use. Microwave radiometers and scatterometers are characterized by short repeat intervals ( 20 km). Therefore their use is limited to large-scale applications. Synthetic Aperture Radars (SARs) offer a high spatial resolution (< 100 m) but have so far been characterized by short duty cycles (acquisition time per orbit) and short swath width (< 100 km). Therefore, large-scale operational monitoring at acceptable time intervals has been out of question. ENVISAT offers now for the first time ScanSAR capabilities with a duty cycle of up to 100 %. However a relatively short swath width (405 km) and a large number of competing imaging modes results in sub-optimal temporal sampling intervals. Still, ENVISAT's ASAR Global Monitoring mode is well suited to explore the potential of ScanSAR techniques for monitoring the hydrosphere at scales compatible with AVHRR, MERIS or MODIS.

We present and assess a diurnal difference indicator that is related directly to the seasonal freeze-thaw effects, focusing, in this paper, primarily on the onset of snowmelt and terrestrial thawing. In order to be able to provide a level of certainty with the indicator our approach is based upon the development, and application, of a noise model that accounts for instrument noise, speckle, spatial heterogeneity, "environmental" noise and the influence of azimuth angle at which the measurement was acquired

A method to determine planting dates in semi-arid regions is presented, based on Ku-band spaceborne scatterometer data. The planting date analysis was performed for Mali, a region with a broad range of vegetation cover with tropical forest in the south and desert in the north. The Ku-band data was acquired by the Sea Winds scatterometer onboard the QuikSCAT satellite during the time from January 2000 to December 2003. Climate data from meteorological stations was compared with scatterometer time series of data colocated from a circular area of a specific size. The comparison shows that the evolution of the backscatter signal is highly correlated with the vegetation cycle triggered in turn by the rain season. An accurate date for the onset of the growing season and therefore a basic planting date can be determined from noise-filtered backscatter time series using a simple threshold method. The temporal variations of the backscatter time series are mainly caused by vegetation growth and changes of surface soil moisture. An increased backscatter signal indicates therefore more and more sufficient growing conditions. For the estimation of the contribution of surface soil moisture, the backscatter was additionally compared within situdata from test sites within the Duero basin in Spain, covered by the soil moisture measurement network of the University of Salamanca. The comparison showed a significant influence of surface soil moisture on the microwave backscatter

C- and K/sub u/-band scatterometer backscatter time series are analysed and compared to meteorological data for two biomes, the African Steppe and the Scandinavian Boreal Forest. Observed characteristics of large scale scattering are inferred and discussed.

Soil moisture is a key variable in a number of geophysical and ecological processes. Despite its importance, availability of information on soil moisture is limited. Only recently it could be demonstrated that low resolution radar data in combination with a change detection method can resolve this constraint. Experience gained in a number of successful pilot projects, lead to an initiative, setting up a global soil moisture archive.

Water is a critical resource in western Africa, and droughts are a serious threat to the population and the environment. A technique to retrieve soil moisture from ERS scatterometer data is applied over a large region covering southern Mali and Burkina Faso. How the method accounts for the wide range of climatic and physiographic conditions encountered in the study area is discussed. An analysis of monthly soil moisture maps covering six years of data shows that the climatic conditions are well reflected in the remotely sensed data.