This letter describes a model-based algorithm for estimating tree height and other bio-physical land parameters from time series of synthetic aperture radar (SAR) interferometric coherence and backscatter supported by sparse lidar data. The random-motion-over-ground model (RMoG) is extended to time series and revisited to capture the short- and long-term temporal coherence variability caused by motion of the scatterers and changes in the soil and canopy backscatter. The proposed retrieval algorithm estimates first the spatially slow-varying RMoG model parameters using sparse lidar data, and subsequently the spatially fast-varying model parameters such as tree height. The recently published global Sentinel-1 (S-1) interferometric coherence and backscatter data set and sparse spaceborne GEDI lidar data are used to illustrate the algorithm. Results obtained for a small region over Spain show that the temporal coherence and backscatter time series have the potential to be used for global, model-based land parameter estimation.

ESA Forest Carbon Monitoring project (FCM) is developing Earth Observation based, user-centric approaches for forest carbon monitoring. Forest carbon accounting based on forest inventory requires precise and timely estimation of forest variables at various spatial levels accompanied by verifiable uncertainty information. In this paper, we present the algorithm trade-off and selection approach and preliminary results of the algorithm intercomparison exercise in the FCM project. The studies were performed over 7 European test sites located in Finland, Ireland, Romania, Spain and Switzerland, and one tropical forest site in Peru. EO datasets were represented by Sentinel-1, Sentinel-2, TanDEM-X and ALOS-2 PALSAR-2 imagery. Examined approaches include popular parametric and SAR/InSAR scattering physics based approaches, and nonparametric and machine learning approaches such as k-NN, random forests, support vector regression.

In this paper, we assess the estimation of bio-physical land parameters from time-series of interferometric SAR coherence supported by a physical model. The random-motion-over-ground model (RMoG) is revisited to partially capture the short- and long-term temporal variability of the coherence caused by motion of the scatterers and changes in their dielectric properties. The recently-published global Sentinel-1 interferometric coherence dataset is used to compare model predictions with observations and evaluate the need for additional model assumptions or ancillary data sets. Space-borne lidar data acquired by GEDI are also considered to further constrain the parameter estimation. This work is particularly relevant to upcoming SAR missions such as NISAR and ROSE-L that will generate global and dense time-series of interferometric temporal coherence at L-band.

The interferometric coherence of repeat-pass C-band synthetic aperture radar observations has shown potential for mapping forest above-ground biomass (AGB) when images were acquired with short temporal repeat intervals and under stable environmental conditions. Since 2014, Sentinel-1 collects repeated interferometric image pairs at global scale with repeat intervals of 6 or 12 days. For a semiarid forest site in California, we evaluated one year of Sentinel-1 6- and 12-day (60 and 59 images, respectively) repeat-pass coherence images with the objective of retrieving AGB. The retrieval was performed by inverting the semiempirical Interferometric Water Cloud Model. The retrieval based on individual coherence images was most accurate (~50% relative root-mean-square difference with respect to a Lidar-derived map) when images were acquired in extended periods of dry conditions. The retrieval accuracy improved by approximately 15% units when combining multiple coherence images in the retrieval. Finally, the accuracy of the retrieval with multitemporal 6- or 12-day repeat-pass coherence images differed by only 2% units. The results of this study advocate consideration of Sentinel-1 repeat-pass coherence in AGB mapping efforts in semiarid forest areas.

C-band scatterometer data are among the longest time record of spaceborne observations, dating back to 1991. Their use in the context of terrestrial carbon cycle studies has not been investigated yet. In spite of a weak sensitivity of the C-band backscatter to forest above-ground biomass (AGB), repeated observations have been shown to support the retrieval of biomass. In this paper, we investigate the use of C-band backscatter data at 25 km spatial resolution from the ERS WindScat and MetOp ASCAT sensors arranged in daily mosaics to estimate forest AGB. A retrieval approach based on self-calibration and regression between SAR backscatter and canopy density was developed, producing daily estimates of AGB that were found to well reproduce the global spatial patterns of AGB. A weighted average of the daily AGB estimates was then applied to generate annual maps of AGB. The evaluation of the AGB dataset with recent global datasets of AGB for 2010 confirms the overall reliability of our estimates.

This paper investigates the sensitivity of Sentinel-1 (S-1) interferometric coherence to crop structure and near surface soil moisture (SSM) content. The study analyzes a data set collected in 2017 over the Apulian Tavoliere agricultural site (Southern Italy). The data set includes: i) in situ data over more than 600 agricultural fields monitored during the 2017 winter and spring growing seasons; ii) time-series of S-1 IW VV & VH backscatter & interferometric coherence; iii) time series of S-1 SSM maps. The temporal behavior of S-1 coherence and VH backscatter has been assessed over the monitored agricultural fields. Initial results indicate a stronger sensitivity of S-1 coherence than VH backscatter to crop geometric structure. In addition, an analysis at site scale, conducted before and after an important rain event, indicates a change of SSM from 0.18 to 0.30 m3/m3along with a change of S-1 coherence from 0.61 to 0.53.

The objective of this study is to cross-compare three algorithms for retrieving surface soil moisture (SSM) from ESA's Sentinel-1 (S-1) data. The context is provided by the large scientific and application interest in SSM products at high resolution and regional/continental scale that can be retrieved from S-l data alone or in combination with other missions such as NASA/SMAP and ESA/SMOS. Of the three investigated algorithms, one inverts a scattering model exploiting a Bayesian approach, whereas the other two are change detection approaches. The cross-comparison is carried out by using both simulated and experimental data. Strengths and weaknesses of the three algorithms are identified and discussed.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at “field scale” resolution, i.e. ~0.1 km. Case studies concerning agricultural sites located in Europe are presented.

We present an error model for forest cover mapping and change detection with L-band synthetic aperture radar (SAR), which considers measurement noise, forest height, number of images available, and imaging conditions. When applied to a multiseasonal set of Advanced Land Observing Satellite Phased-Array type L-band SAR images acquired over a forest site in southern Sweden, the error model, which is founded on a semiempirical model, suggests that a bitemporal set of cross-polarized L-band backscatter observations is sufficient to detect a forest cover loss of 50% at hectare scale for mature forests. The error probability increases when using co-polarization images, images acquired under adverse imaging conditions, or when detecting forest cover change in a forest of low height. The availability of multitemporal L-band observations is expected to improve forest cover retrieval and change detection, albeit highly correlated forest cover retrieval errors between images acquired within narrow time intervals (e.g., months) pose a limit on the improvements that can be achieved.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

The availability of reliable land cover information is crucial for a wide range of applications, like for example monitoring of land use change and land degradation as well as administrative matters in global, regional and local scales. In this paper the potential of SENTINEL-1 C-band SAR data for land cover applications, e.g. generating level-2 land cover classification products has been investigated. Therefore, the planned short revisit and dual polarization concept of SENTINEL-1 has been simulated using multi-temporal ERS-2 and ENVISAT ASAR AP C-band backscatter intensity data. For classification, several multi-temporal metrics and the minimum amount of SAR data acquired during one growing season have been analyzed to derive five basic land cover classes with accuracies greater than 85%.

Retrieval of forest stem volume from synthetic aperture (SAR) backscatter and interferometric SAR (InSAR) coherence is generally performed using a model-based approach, where in situ measurements are necessary to estimate the unknown model parameters. Problems arise when in situ data are either not available or of low quality or the observables present spatial variations. In this work we present three approaches for automatic modeling and inversion of forest backscatter and coherence to retrieve forest stem volume. The three approaches exploit statistical distributions of the observables to obtain estimates for the unknowns in the model. Results shows remarkable agreement with those obtained by means of traditional modeling approaches based on in situ data.

The accuracy of SAR interferometric coherence estimates depends on the precision of several processing steps. In particular decorrelation can occur if range common-band filtering does not perform optimally. Typically a planar surface is adopted which introduces additional decorrelation in case of sloped terrain. To take into account topographic variations a slope-adaptive range common-band filtering method has been developed. A DEM is used to simulate an unwrapped interferogram and the fringe rate is used as driver for the filter size in the range common-band filtering step. Tests with several spaceborne interferometric SAR datasets confirmed the robustness of the method. The improvement of the coherence increased for increasing perpendicular baseline. As a consequence, the fringe visibility also greatly improved. To quantify the improvement of the coherence estimates with the slope-adaptive range common-band filtering, we considered the variation of classification in forest mapping, i.e. an application in which accurate coherence estimates are needed. With improved coherence the classified forest stem volume agreed better with forest maps derived from other remote sensing datasets.