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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. [ABSTRACT FROM AUTHOR]

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 and in situ measurements 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. [ABSTRACT FROM PUBLISHER]

Recently, the slope and curvature estimation of the backscatter-incidence angle relationship within the TU Wien retrieval algorithm has been improved. Where previously only climatologies of the slope and curvature parameters were available, i.e., one value for every day of year, slope and curvature are now calculated for every day. This enables the retrieval of time series of vegetation optical depth (τa) from backscatter observations. This study demonstrates the ability to detect interannual variability in vegetation dynamics using τa derived from backscatter provided by the advanced scatterometer on-board Metop-A. τa time series over Australia for the period 2007-2014 are compared to leaf area index (LAI) from SPOT-VEGETATION by calculating the rank correlation coefficient (τa) for original time series and anomalies. High values for τa are found over bare soil and sparse vegetation in central Australia with median τa values of 0.78 and 0.58, respectively. Forests and ephemeral lakes and rivers impact the retrieval of τa, and the negative values for τa are found in these areas. Looking at the annual averages of τa, LAI, and surface soil moisture, significantly high values are found for the anomalously wet years 2010 and 2011. Patterns in the increased τa correspond to regions with increased soil moisture and LAI. Values for τa and LAI are anomalous especially in sparsely vegetated regions, where the flush of grasses increases τa and LAI. Regions with enough precipitation and higher woody vegetation component show a smaller increase in 2010 and 2011. This study demonstrates the skill of τa, and subsequently of scatterometers, to monitor the vegetation dynamics thanks to the multiincidence angle observation capability. [ABSTRACT FROM PUBLISHER]

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 \taua using a water-cloud model. The spatial and temporal behaviors of the newly developed \taua are compared with the optical depth retrieved from passive microwave observations with the land parameter retrieval model \taup. Spatial patterns correspond well, although low values for \taua are 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 \taua and 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

The Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) mission has provided an unprecedented opportunity to study the potential of hyperspectral multiangular data for improved vegetation characterisation at a fine spatial scale. This study exploits to what degree the spectral anisotropy contained in the data can contribute to a more robust retrieval of chlorophyll and leaf area index of cotton compared to retrievals based on single angle observations. To do so, a simplified automated radiative transfer model (RTM) inversion scheme was applied both to the single observation angles and to various combinations of multiple view angles. It is shown that significant increase in accuracy is obtained when directional information is included. However, the improvement obtained by assimilating a certain observation angle appears to be strongly related to the ability of the RTM in reconstructing canopy reflectance for this constellation. Hence, due to the deficiency of the 1-D RTM to reconstruct observed reflectance for the row crop in the extreme forward looking direction, the result strongly deteriorated when this view angle was included. Model inversion based on all but the extreme forward looking view angle provided a root-mean-square error of 18.5% (6.6\,\mu \g/\cm^2) and 32.7% (0.55\ \m^2/\m^2) for chlorophyll and leaf area index, respectively. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]