Data in today's dynamic world undergoes constant change and evolution, spanning various formats such as text, websites, tweets, and sensor readings. Storing and referencing these diverse data types pose significant challenges due to data movement, changes in content or structure, and limited availability. Efficient data identification is crucial for speeding up scientific discovery and result validation, especially when data accessibility is guaranteed. Recent years have witnessed progress in data citation practices, with conferences mandating the inclusion of utilized and generated data. However, existing solutions primarily cater to static datasets, rendering them ineffective for dynamically evolving ones. This paper addresses this gap by providing a tailored dynamic data citation prototype for the International Soil Moisture Network, one of the largest scientific in situ soil moisture databases. Our work encompasses the implementation and evaluation of different data versioning strategies and a query store architecture that enables the citation, reproducibility, and verification of large sets of SQL queries to recreate data requests by users. By applying the RDA Dynamic Data Citation Guidelines, we assess the necessary needs for such a system and further measure the performance and storage impact of our proposed approaches.

Despite the current short temporal coverage of high spatial resolution SM maps estimated from Synthetic Aperture Radars such as Sentinel-1(S1), their evaluation is important in the context of the ESA CCI as potential future high resolution (HR) SM long time series, and also as benchmarking references for HR SM data sets that could be obtained by downscaling coarser resolution sensors. In this context, 1 km HR SM maps obtained making a synergistic use of S 1 and Sentinel 2 (or Sentinel 3) using the$S^{2}MP$algorithm were compared to the HR SM data sets from the Copernicus Global Land Service produced from S 1 data over three regions in Europe and one in Tunisia. In addition, the$S^{2}MP$maps were also compared to the SMAP+S 1 downscaled product in those regions and in two additional ones in North America and Australia. The HR SM maps show an overall good agreement for croplands and herbaceous land covers while showing significant differences for other land cover classes. All the 1 km SM maps data sets, in addition to coarse scale SMAP, SMOS and CCI data, were evaluated against in-situ measurements. The results show that the HR products are in good agreement but they show a lower correlation with respect to in-situ data than the coarse resolution products.

Remote sensing observations of variables related to vegetation at microwave and optical/infrared wavelengths are presented over three regions in Europe in the Iberian peninsula, northern Finland and central Europe. They include the instrumented sites of Las Majadas, Sodankyla and Reusel. The final goal is to better constrain land carbon cycle models using the complementarities of vegetation optical depth derived at different frequencies from active and passive instruments (related to vegetation water content and biomass) as well as optical data of the fraction of absorbed photosynthetically active radiation or solar induced fluorescence, closely linked to photosynthesis. The first results confirm this complementarity. For instance, time series of different variables exhibit positive correlations in some areas and negative correlations in other areas.

Constructing long time records of soil moisture (SM) requires the merging of data derived from different instruments while insuring the removing of the bias from different sensors time series. For instance, the ESA Climate Change Initiative (CCI) for SM currently uses the GLDAS v2.1 model as the reference to re-scale active and passive microwave time series. This paper discusses the possibility to use data from an L-band sensor as the reference in order to remove model dependency. AMSR-2 SM time series were re-scaled using different SMAP and SMOS datasets and evaluated against in-situ measurements. The results show that L-band data can be used to re-scale other sensor data with good performances. In addition, using the 11-years SMOS SM times series, the optimal length of the reference time series was studied.

The European Space Agency’s Climate Change Initiative (ESA CCI) Soil Moisture (SM) COMBINED product is a more than 40-year-long data record on global SM for climate studies and applications. It merges SM observations derived from multiple active and passive satellite remote sensing instruments in the microwave domain. Differences in sensor characteristics (such as frequency or polarization) can cause structural breaks in the product, which are not completely removed during the merging process. These artificially caused discontinuities can adversely affect studies using the long-term data set. In this article, we compare three adjustment methods in terms of reducing the number of detected breaks in the SM record. We investigate their impact on the data with multiple validation metrics. Their potential (negative) influence is examined by comparing trends in the data before and after homogenization. We find that all three presented methods can reduce the number of detected breaks in ESA CCI SM. Differences between the methods mainly concern their ability to handle inhomogeneities in variance. Evaluation of the corrected data shows the limited impact of homogenization in terms of quantitative validation metrics. Changes in SM trends due to removing breaks are found in some areas. We find that break correction overall improves the already rather homogeneous data set while preserving its climate describing characteristics. Quantile category matching is identified as the preferred method in terms of correcting breaks in ESA CCI SM.

Satellite observations from microwave sensors contain information about the vegetation covering the Earth and thus bare large potential to monitor its dynamics at the global scale. Yet, individual satellite missions are too short to allow for a consistent monitoring over long periods. In this study, we present a new series of long-term products of microwave vegetation optical depth (VOD), a model-based indicator that is closely related to the total water contained in the aboveground biomass. The products were created by fusing VOD products from various sensors operating in C-, X-, and Ku-band. The single-sensor level 2 products are combined by a statistical merging approach, which involves spatial and temporal matching and resampling, bias correction, and an optimal merging into homogenized global gridded products with a spatial sampling of 0.25°. Separate products were generated for C-, X-, and Ku- bands to preserve the unique response of each frequency to different vegetation characteristics. The resulting products cover the period 1987-2018, 1998-2018, and 2002-2018 for the Ku-, X, and C-band respectively. In this study, we provide an overview of the merging methodology, present the product characteristics of the novel products, and evaluate their spatial and temporal characteristics against independent leaf area index observations from optical remote sensing. Moreover, we show how VOD data can be used to provide estimates of gross primary production.

Soil moisture retrieval from microwave remote sensing brightness temperatures is in continuous development. In this study, the most recent and latest microwave remote sensing soil moisture products were evaluated against ground-based measurements. We compared, for the first time, the latest versions of SMOS (L2V650 and SMOS-IC V105), SMAP (L3V4), and CCI (V03.2) soil moisture products with respect to ground-based measurements obtained from ISMN (International Soil Moisture Network). Time series were plotted over some sites and it was found that all these products capture well the temporal dynamics over all the sites used in this study. However, CCI was wetter than the in situ measurements over Niger and both SMOS products (IC and L2) and SMAP were drier than the in situ observations over Biebrza site in Poland.

Satellite observations from active and passive microwave sensors are a valuable means to measure surface soil moisture at the global scale. More than a dozen historical and currently active missions have been used for this purpose, together spanning a period from 1978 to present. Within the Climate Change Initiative (CCI) of the European Space Agency (ESA), these separate missions are systematically combined into homogenized Climate Data Records (CDRs). Currently, the operational production and the near-real-time updating of these CDRs is being transferred to the EU Copernicus Climate Changes Services (C3S). In this study, we provide an overview of the characteristics of the most recent ESA CCI and C3S soil moisture products. In particular, we focus on the new merging procedure, which optimally weighs the individual missions based on their uncertainties estimated with triple collocation analysis.

Measurements of soil moisture are needed for a better global understanding of the land surface-climate feedbacks at both the local and the global scale. Satellite sensors operating in the low frequency microwave spectrum (from 1 to 10 GHz) have proven to be suitable for soil moisture retrievals. These sensors now cover nearly 4 decades thus allowing for global multi-mission climate data records. In this paper, we assess the possibility of using L-band (SMOS) and C-band (AMSR2, ASCAT) remotely sensed soil moisture time series for the global estimation of soil moisture persistence. A multi -output Gaussian process regression model is applied to ensure spatio-temporal coverage of the satellite data sets. It allows a robust computation of temporal autocorrelation and e- folding times. Results over a selection of catchments reveals general agreement between the response of in-situ and satellite microwave observations to hydrological processes. The response of the uppermost-modeled soil moisture layer of GLDAS-1-Noah agrees well with that of the observations, whereas major differences are displayed by MERRA2 reanalysis. The temporal dynamics of the three microwave sensors are shown to be consistent, close to in-situ and to GLDAS-1- Noah, which supports their combination for the global estimation soil moisture persistence.

We propose a method for merging soil moisture retrievals from spaceborne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from using triple collocation analysis (TCA). In regions where TCA is deemed unreliable, we use correlation significance levels ($p$-values) as indicator for retrieval quality to decide whether to use active data only, passive data only, or an unweighted average. We apply the proposed merging scheme to active retrievals from advanced scatterometer and passive retrievals from the Advanced Microwave Scanning Radiometer—Earth Observing System using Global Land Data Assimilation System-Noah to complement the triplet required for TCA. The merged time series is evaluated against soil moisture estimates from ERA-Interim/Land andin situmeasurements from the International Soil Moisture Network using the European Space Agency’s (ESA’s) current Climate Change Initiative—Soil Moisture (ESA CCI SM) product version v02.3 as benchmark merging scheme. Results show that the$p$-value classification provides a robust basis for decisions regarding using either active or passive data alone, or an unweighted average in cases where relative weights cannot be estimated reliably, and that the weights estimated from TCA in almost all cases outperform the ternary decision upon which the ESA CCI SM v02.3 is based. The proposed method forms the basis for the new ESA CCI SM product version v03.x and higher.

In microwave remote sensing of the Earth's surface, the satellite signal holds information on both soil moisture and vegetation. This necessitates a correction for vegetation effects when retrieving soil moisture. This paper assesses the strengths and weaknesses of the existing vegetation correction as part of the Vienna University of Technology (TU-Wien) method for soil moisture retrieval from coarse-scale active microwave observations. In this method, vegetation is based on a multiyear climatology of backscatter variations related to phenology. To assess the plausibility of the correction method, we first convert the correction terms for retrievals from the Advanced Scatterometer (ASCAT) into estimates of vegetation optical depth τausing a water-cloud model. The spatial and temporal behaviors of the newly developed τaare compared with the optical depth retrieved from passive microwave observations with the land parameter retrieval model τp. Spatial patterns correspond well, although low values for τaare found over boreal forests. Temporal correlation between the two products is high (R = 0.5), although negative correlations are observed in drylands. This comparison shows that τaand thus the vegetation correction method are sensitive to vegetation dynamics. Effects of the vegetation correction on soil moisture retrievals are investigated by comparing retrieved soil moisture before and after applying the correction term to modeled soil moisture. The vegetation correction increases the quality of the soil moisture product. In areas of high interannual variability in vegetation dynamics, we observed a negative impact of the vegetation correction on the soil moisture, with a decrease in correlation up to 0.4. It emphasizes the need for a dynamic vegetation correction in areas with high interannual variability.

Performances of two global satellite-based surface soil moisture (SSM) retrievals with respect to model-based SSM derived from the MERRA (Modern-Era Retrospective analysis for Research and Applications) rea-nalysis were explored in this paper: (i) Soil Moisture and Ocean Salinity (SMOS; passive) Level-3 SSM (SMOSL3) and (ii) the Advanced Scatterometer (ASCAT; active) SSM. Temporal correlation was used to investigate the performance of SMOSL3 and ASCAT SSM products during the period 05/2010–2012 on a global basis. Both SMOSL3 and ASCAT (slightly better) captured well (R>0.70) the long-term variability of the modelled SSM, particularly, over the Indian subcontinent, the Great Plains of North America, and the Sahel. However, ASCAT had negative correlations in arid regions, in particular across the Sahara and the Arabian Peninsula. This may be due to complex scattering mechanisms over very dry surfaces. To explore the land cover dependence of the analyzed statistical indicators, the global correlation results were averaged per biome extracted from a global map of biomes. In general, SMOSL3 and ASCAT performances behaved differently from one biome to another. For SMOSL3, the highest average correlation was observed over “tropical semi-arid” (R = ∼ 0.5) and “temperate semi-arid” biomes, whereas for ASCAT, the highest correlations were observed over “tropical semi-arid” (R = ∼ 0.7) and “tropical humid” biomes. The poorest agreement for both SMOSL3 and ASCAT was generally found over “tundra” and “desert temperate” biomes, particularly for ASCAT. This study showed that the performance of both SMOSL3 and ASCAT is highly dependent on vegetation. We also showed that both of them provide complementary information on SSM, which implies a potential for data fusion which would be pertinent for the ESA climate change initiative (CCI).

In this study we evaluate five different retrieval algorithms, applied on MetOp-A ASCAT backscatter data, in their ability to retrieve soil moisture on a global scale. Correlation and triple collocation analysis are performed using in situ and land surface model data as a reference. Results do not clearly identify one best algorithm. We therefore conclude that future work should focus on the exploitation of the strengths and weaknesses of different modelling approaches in a synergetic way rather than trying to find one model that suits every possible situation.

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.

The assimilation of satellite soil moisture data into rainfall-runoff modelling represents an important issue not only for research purposes but also for hydrological application addressing flood forecasting. Notwithstanding the large effort made in the last three decades, only few studies demonstrated a benefit deriving from the use of satellite soil moisture data in hydrology. This matter can be ascribed to the differences in the quality of the assimilated data, in the climatic conditions and in the data assimilation techniques that have been adopted. Based on that, this study compares different satellite soil moisture products in different catchments worldwide to shed light about the more suitable products and climatic conditions that should be employed for improving runoff prediction. The results reveal that the employed soil moisture products can be conveniently used to improve runoff prediction. However, reliability differs according to the climatic region and the accuracy of satellite retrievals.

Within the Climate Change Initiative of the European Space Agency a multi-satellite soil moisture product covering the period 1979-2010 was released. In this study we first assess its quality by comparing it with soil moisture from ground-based stations and several land surface model estimates. Secondly, the dynamics in the dataset were assessed using trend analysis and comparisons with ancillary data sets of precipitation and vegetation. Significant changes over time were found that largely correspond to changes in precipitation and vegetation vigorousness. However, the influence of changing observation density and data set quality over time need to be better understood for a more precise interpretation of the observed trends.

This paper describes the design and characteristics of a new in situ soil moisture network in Austria, developed for the validation of spaceborne and modeled soil moisture products. The carefully chosen design of the network enables upscaling of the point measurements to the scale of a satellite footprint. As a first assessment soil moisture time series from an existing station are compared to both ASCAT and GLDAS surface soil moisture data. Results of this comparison show that both modeled and remotely sensed soil moisture corresponds with the in situ measurements. However, the overall higher soil moisture values for the in situ station, which are represented in the bias, emphasize the need for a carefully designed and calibrated soil moisture network.

Satellite observations from active and passive microwave sensors have been successfully used to infer the status of soil water at a global scale. The individual instruments that can be used for this purpose cover a period of more than 30 years. Combining the separate missions into a single homogenized dataset would provide a unique opportunity to study the dynamics of soil moisture over space and time. This study presents a recently developed method for combining global soil moisture datasets with different specifications into a merged data record. The approach profits from the advantages of the various products and retrieval techniques while explicitly addressing the uncertainties related to vegetation density. Initially, products from two active and four passive microwave mission were combined. The merged dataset was analyzed with respect to trends and connects with various climate modes.

With the increasing number of remotely sensed soil moisture products a need for their standardized evaluation becomes necessary. This work comprises of two major parts. First, it provides a brief overview on three advanced evaluation measures commonly used to evaluate soil moisture products. Second, it investigates the performance of the triple collocation (TC) and error propagation (EP) methods when these are used to evaluate the medium resolution ASAR GM soil moisture product. The study highlights the fact that each evaluation method has limitations and presumptions that may not always be fully achievable. It further demonstrates that a combination of only two evaluation methods can shed more light on the performance quality of the methods and may lead to an improved understanding of the error structures of the product.

Due to the strong contrast of the dielectric properties of dry and wet soil at microwave frequencies soil moisture can be retrieved on a global scale from active and passive microwave remote sensing instruments. Recent validation studies carried out over a number of in situ networks in Europe, the US and Australia have demonstrated that the soil moisture retrieval skill achieved with the Soil Moisture and Ocean Salinity (SMOS) mission and the Advanced Scatterometer (ASCAT) compares well in many regions of the world. But of course, there are also areas where the retrieved soil moisture values from these instruments do not match well. In this study global SMOS and ASCAT soil moisture data are compared with the aim to identify the areas of agreement and disagreement. The analysis confirms the findings of the previous studies that over most regions worldwide the temporal evolution of SMOS and ASCAT retrievals compare quite well. Notable exceptions are arid environments where the ASCAT retrievals fail to reproduce the real soil moisture trends, while the SMOS retrievals perform as expected. More work is required to understand the contrasting behavior of the ASCAT and SMOS soil moisture retrievals in these environments.

The triple collocation technique, which retrieves the error variances of three sets of measurements of the same parameter, is applied to soil moisture records in central Spain: ASCAT remote sensing observations, REMEDHUS in-situ probes, and the ERA Interim model. The objective is the estimation of the temporal variability of the error of ASCAT. The three data sets have to be calibrated with respect to each other as they show different mean values and dynamic ranges. The time-variant estimation of both the error and the calibration parameters is shown to be very sensitive to the extents of the temporal windows used and the calibration procedure. Due to the temporal fluctuations of the calibration constants, artefacts such as seasonal variations and extreme values are introduced. This case study shows that the temporal analysis of the errors using the collocation technique can lead to spurious results when the data sets have to be referenced with respect to one another.

A time-efficient solution to estimate the error of satellite surface soil moisture from the land parameter retrieval model is presented. The errors are estimated using an analytical solution for soil moisture retrievals from this radiative-transfer-based model that derives soil moisture from low-frequency passive microwave observations. The error estimate is based on a basic error propagation equation which uses the partial derivatives of the radiative transfer equation and estimated errors for each individual input parameter. Results similar to those of the Monte Carlo approach show that the developed time-efficient methodology could substitute computationally intensive methods. This procedure is therefore a welcome solution for near-real-time data assimilation studies where both the soil moisture product and error estimate are needed. The developed method is applied to the C-, X-, and Ku-bands of the Aqua/Advanced Microwave Scanning Radiometer for Earth Observing System sensor to study differences in errors between frequencies.

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.

It is well known that the adjacency effect has to be taken into account during the retrieval of surface reflectance from high spatial resolution satellite imagery. The effect results from atmospheric scattering, depends on the reflectance contrast between a target pixel and its large-scale neighborhood, and decreases with wavelength. Recently, ground reflectance field measurements were published, claiming a substantial influence of the adjacency effect at short distance measurements (< 2 m), and an increase of the effect with wavelength. The authors repeated similar field measurements and found that the adjacency effect usually has a negligible influence at short distances, decreasing with wavelength in agreement with theory, but can have a small influence in high-reflectance contrast environments. Radiative transfer calculations were performed to quantify the influence at short and long distances for cases of practical interest (vegetation and soil in a low-reflectance background). For situations with large reflectance contrasts, the atmospheric backscatter component of the adjacency effect can influence ground measurements over small-area targets, and should therefore be taken into account. However, it is not possible to draw a general conclusion, since some of the considered surfaces are known for exhibiting strong directional effects