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Vegetation optical depth (VOD) is a model-based indicator of the total water content stored in the vegetation canopy derived from microwave Earth observations. As such, it is related to vegetation density, abundance, and above-ground biomass (AGB). Moesinger et al. (2020) introduced the global microwave VOD Climate Archive (VODCA v1), which harmonises VOD retrievals from several individual sensors into three long-term, multi-sensor VOD products in the C, X, and Ku frequency bands, respectively. VODCA v1 was the first VOD dataset spanning over 30 years of observations, thus allowing the monitoring of long-term changes in vegetation. Several studies have used VODCA in applications such as phenology analysis; drought monitoring; gross primary productivity monitoring; and the modelling of land evapotranspiration, live fuel moisture, and ecosystem resilience. This paper presents VODCA v2, which incorporates several methodological improvements compared to the first version and adds two new VOD datasets to the VODCA product suite. The VODCA v2 products are computed with a novel weighted merging scheme based on first-order autocorrelation of the input datasets. The first new dataset merges observations from multiple sensors in the C-, X-, and Ku-band frequencies into a multi-frequency VODCA CXKu product indicative of upper canopy dynamics. VODCA CXKu provides daily observations in a 0.25° resolution for the period 1987–2021. The second addition is an L-band product (VODCA L), based on the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, which in theory is more sensitive to the entire canopy, including branches and trunks. VODCA L covers the period 2010–2021 and has a temporal resolution of 10 d and a spatial resolution of 0.25°. The sensitivity of VODCA CXKu to the upper vegetation layer and that of VODCA L to above-ground biomass (AGB) are analysed using independent vegetation datasets. VODCA CXKu exhibits lower random error levels and improved temporal sampling compared to VODCA v1 single-frequency products. It provides complementary spatio-temporal information to optical vegetation indicators containing additional information on the state of the canopy. As such, VODCA CXKu shows moderate positive agreement in short vegetation (Spearman's R : 0.57) and broadleaf forests (Spearman's R : 0.49) with the fraction of absorbed photosynthetically active radiation from MODIS. VODCA CXKu also shows moderate agreement with the slope of the backscatter incidence angle relation of MetOp ASCAT in grassland (Spearman's R : 0.48) and cropland (Spearman's R : 0.46). Additionally, VODCA CXKu shows temporal patterns similar to the Normalized Microwave Reflection Index (NMRI) from in situ L-band GNSS measurements of the Plate Boundary Observatory (PBO) and sap flow measurements from SAPFLUXNET. VODCA L shows strong spatial agreement (Spearman's R : 0.86) and plausible temporal patterns with respect to yearly AGB maps from the Xu et al. (2021) dataset. VODCA v2 enables monitoring of plant water dynamics, stress, and biomass change and can provide insights, even into areas that are scarcely covered by optical data (i.e. due to cloud cover). VODCA v2 is open-access and available at 10.48436/t74ty-tcx62. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance. [ABSTRACT FROM AUTHOR]

Drought events have multiple adverse impacts on environment, society, and economy. It is thus crucial to monitor and characterise such events. Here, we compare the ability of selected state-of-the-art long-term reanalysis and remote-sensing products to represent major seasonal and multi-year drought events in the 2000–2020 period globally. We focus on soil moisture (or agroecological) drought and place the results in the context of trends in dry-season soil moisture. We consider surface and root-zone soil moisture from ERA5, the related ERA5-Land, and MERRA-2 reanalysis products, the ESA CCI remote-sensing surface soil moisture products (encompassing an ACTIVE, a PASSIVE and a COMBINED product), as well as its near real-time counterpart produced within C3S. In addition, we use a new root-zone soil moisture dataset derived from the ESA CCI COMBINED product. Except for ESA CCI surface and root-zone soil moisture, the considered products offer opportunities for drought monitoring since they are available in near real-time. We analyse 18 documented drought events within predefined spatial and temporal bounds derived from scientific literature. Based on standardised daily anomalies of surface and root-zone soil moisture, the drought events are characterised by their severity (the time accumulated standardised anomalies), magnitude (the minimum of the standardised anomalies over time), duration, and spatial extent. Product deviations in drought severity and magnitude are then placed in the context of trends in dry-season soil moisture, and potential reasons for diverging global soil moisture trends in the products are further investigated. All investigated products capture the considered drought events. Overall, responses of surface soil moisture tend to be weakest for the ACTIVE remote-sensing products in all metrics, but most pronounced in the drought magnitudes. Also, MERRA‑2 shows lower magnitudes than the other products. Except for the COMBINED products, the remote-sensing products tend to underestimate the spatial extents of larger droughts. Product differences in drought severity and magnitude for single events are consistent with the differences in dry-season soil moisture trends. These trends are globally diverse and partly contradictory between products. ERA5, ERA5-Land and the COMBINED products show larger fractions of drying trends, MERRA-2 and the C3S ACTIVE and PASSIVE products more widespread wetting trends. MERRA-2 surface air temperature shows regionally negative biases in trends compared to a ground observational product, which suggests that this reanalysis product underestimates drought trends. Also, the comparison with trends in selected land-surface characteristics and bioclimatic indicators shows that dry-season soil moisture trends may be affected by retrieval or modelling artifacts in some cases. In the root zone (based on the reanalysis products and the ESA CCI root-zone soil moisture dataset), the droughts are dampened in magnitude and smaller in spatial extent but show a tendency to prolonged durations. Based on the overall observational evidence and the consideration of the respective limitations of the included products, the present analyses suggest a consistent tendency towards drying during the last two decades in some regions, namely in parts of central Europe, in a region north of the Black Sea/Caspian Sea, in southern Africa, and in parts of Australia, Siberia and South America. [ABSTRACT FROM AUTHOR]

Salt pans are unique wetland ecosystems. In the Austrian Seewinkel region, salt pans are in an increasingly vulnerable state due to groundwater drainage and heightened climatic pressures. It is crucial to model how seasonal and long-term hydrological and climatological variations affect the salt pan dynamics in Seewinkel, yet a comprehensive understanding of the driving processes is lacking. The goal of this study is to develop random forest machine learning models driven by hydrological and meteorological data that allow us to predict in early spring (March) of each year the inundation state in the subsequent summer and fall. We utilize Earth observation data from Landsat 5 (L5), 8 (L8), and 9 (L9) to derive the time series of the inundation state for 34 salt pans for the period 1984–2022. Furthermore, we demonstrate that the groundwater level observed in March is the strongest predictor of the salt pan inundation state in summer and fall. Utilizing local groundwater data yields a Matthews correlation coefficient of 0.59. Models using globally available meteorological data, either instead of or in addition to groundwater data, provide comparable results. This allows the global transfer of the approach to comparable ecosystems where no in situ data are available. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root-zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exist for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method's model parameter T , which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T -parameter values were calculated for four soil depth layers (0–10, 10–40, 40–100, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers, with the median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties in the EF method from ISMN ground reference measurements taken at the surface and at varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes and temporal variations. The product described here is, to the best of our knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth. [ABSTRACT FROM AUTHOR]

In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km 2 ) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%; spring barley 4%, 3%; sugar beets 2%, 21%; and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018. [ABSTRACT FROM AUTHOR]

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas. [ABSTRACT FROM AUTHOR]

Fires are a pervasive feature of the terrestrial biosphere and contribute large carbon emissions within the earth system. Humans are responsible for the majority of fire ignitions. Physical and empirical models are used to estimate the future effects of fires on vegetation dynamics and the Earth's system. However, there is no consensus on how human-caused fire ignitions should be represented in such models. This study aimed to identify which globally available predictors of human activity explain global fire ignitions as observed by satellites. We applied a random forest machine learning framework to state-of-the-art global climate, vegetation, and land cover datasets to establish a baseline against which influences of socioeconomic data (cropland fraction, gross domestic product (GDP), road density, livestock density, grazed lands) on fire ignition occurrence were evaluated. Our results showed that a baseline random forest without human predictors captured the spatial patterns of fire ignitions globally, with hotspots over Sub-Saharan Africa and South East Asia. Adding single human predictors to the baseline model revealed that human variables vary in their effects on fire ignitions and that of the variables considered GDP is the most vital driver of fire ignitions. A combined model with all human predictors showed that the human variables improve the ignition predictions in most regions of the world, with some regions exhibiting worse predictions than the baseline model. We concluded that an ensemble of human predictors can add value to physical and empirical models. There are complex relationships between the variables, as evidenced by the improvement in bias in the combined model compared to the individual models. Furthermore, the variables tested have complex relationships that random forests may struggle to disentangle. Further work is required to detangle the complex regional relationships between these variables. These variables, e.g., population density, are well documented to have substantial effects on fire at local and regional scales; we determined that these variables may provide more insight at more continental scales. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exists for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method’s model parameter T, which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers with median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally-dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties of the EF method from ISMN ground reference measurements taken at the surface and in varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes as well as temporal variations. The product described here is, to our best knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth. [ABSTRACT FROM AUTHOR]

Very high-resolution (∼10 –100 m) surface soil moisture (SM) observations are important for applications in agriculture, among other purposes. This is the original goal of the S 2MP (Sentinel-1/Sentinel-2-Derived Soil Moisture Product) algorithm, which was designed to retrieve surface SM at the agricultural plot scale by simultaneously using Sentinel-1 (S1) backscatter coefficients and Sentinel-2 (S2) NDVI (Normalized Difference Vegetation Index) as inputs to a neural network trained with Water Cloud Model simulations. However, for many applications, including hydrology and climate impact assessment at regional level, large maps with a high resolution (HR) of around 1 km are already a significant improvement with respect to most of the publicly available SM datasets, which have resolutions of about 25 km. In this study, the S 2MP algorithm was adapted to work at 1 km resolution and extended from croplands to herbaceous vegetation types. A target resolution of 1 km also allows the evaluation of the interest in using NDVI derived from Sentinel-3 (S3) instead of S2. Two sets of SM maps at 1 km resolution were produced with S 2MP over six regions of ∼104 km 2 in Spain, Tunisia, North America, Australia, and the southwest and southeast regions of France for the whole year of 2019. The first set was derived from the combination of S1 and S2 data (S1 + S2 maps), while the second one was derived from the combination of S1 and S3 (S1 + S3 maps). S1 + S2 and S1 + S3 SM maps were compared to each other, to those of the 1 km resolution Copernicus Global Land Service (CGLS) SM and Soil Water Index (SWI) datasets, and to those of the Soil Moisture Active Passive (SMAP) + S1 product. The S 2MP S1 + S2 and S1 + S3 SM maps are in very good agreement in terms of correlation (R≥0.9), bias (≤0.04 m 3 m -3), and standard deviation of the difference (SDD≤0.03 m 3 m -3) over the six domains investigated in this study. In a second step, the S1 + S3 S 2MP maps were compared to the other HR maps. S1 + S3 SM maps are well correlated to the CGLS SM maps (R∼0.7 –0.8), but the correlations with respect to the other HR maps (CGLS SWI and SMAP + S1) drop significantly over many areas of the six domains investigated in this study. The highest correlations between the HR maps were found over croplands and when the 1 km pixels have a very homogeneous land cover. The bias among the different maps was found to be significant over some areas of the six domains, reaching values of ±0.1 m 3 m -3. The S1 + S3 maps show a lower SDD with respect to CGLS maps (≤0.06 m 3 m -3) than with respect to the SMAP + S1 maps (≤0.1 m 3 m -3) for all the six domains. Finally, all the HR datasets (S1 + S2, S1 + S3, CGLS, and SMAP + S1) were also compared to in situ measurements from five networks across five countries, along with coarse-resolution (CR) SM products from SMAP, SMOS, and the European Space Agency Climate Change Initiative (CCI). While all the CR and HR products show different bias and SDD, the HR products show lower correlations than the CR ones with respect to in situ measurements. The discrepancies in between the different HR datasets, except for the more simple land cover conditions (homogeneous pixels with croplands) and the lower performances with respect to in situ measurement than coarse-resolution datasets, show the remaining challenges for large-scale HR SM mapping. [ABSTRACT FROM AUTHOR]

Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter vegetation optical depth (VOD) and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellite sensors operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation at large scales. VOD has been used to determine above-ground biomass, monitor phenology, or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scales. Several VOD products exist, differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as the normalized difference vegetation index, leaf area index, gross primary production, biomass, vegetation height, or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select VOD at the most suitable wavelength for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) in the Ku, X, and C bands from the harmonized Vegetation Optical Depth Climate Archive (VODCA) dataset and L-band VOD derived from Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) sensors. The leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 93 % and 95 % of the variance (Nash–Sutcliffe model efficiency coefficient) in 8-daily and monthly VOD within a multi-variable random forest regression. Thereby, the regression reproduces spatial patterns of L-band VOD and spatial and temporal patterns of Ku-, X-, and C-band VOD. Analyses of accumulated local effects demonstrate that Ku-, X-, and C-band VOD are mostly sensitive to the leaf area index, and L-band VOD is most sensitive to above-ground biomass. However, for all VODs the global relationships with vegetation properties are non-monotonic and complex and differ with land cover type. This indicates that the use of simple global regressions to estimate single vegetation properties (e.g. above-ground biomass) from VOD is over-simplistic. [ABSTRACT FROM AUTHOR]

Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience. [ABSTRACT FROM AUTHOR]

The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread, and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over past decadal timescales. Therefore, our objectives are: (1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; (2) to test different model structures to estimate LFMC from VOD; and (3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations, but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance in estimating LFMC. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status, drought conditions, and fire dynamics. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e.\ without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in either observations or simulations, or both. We perform bias-blind and bias-aware DA of the Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–019 period, and evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. Bias-blind LAI DA can also lead to unrealistic shifts in soil moisture climatologies, for example when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between update steps. This model drift also propagates to short-term estimates of GPP and ET, and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements of GPP anomalies from the bias-blind DA, but forego improvements in the root mean square deviation (RMSD) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance. [ABSTRACT FROM AUTHOR]

The Okavango River system in southern Africa is known for its strong interannual variability of hydrological conditions. Here, we present how this is exposed in surface soil moisture, land surface temperature, and vegetation optical depth as derived from the Land Parameter Retrieval Model, using an inter-calibrated, long-term, multi-sensor passive microwave satellite data record (1998–2020). We also investigate how these interannual variations relate to state-of-the-art climate reanalysis data from ERA5-Land. We analysed both the upstream river catchment and the Okavango delta, supported by independent data records of discharge measurements, precipitation, and vegetation dynamics observed by optical satellites. The seasonal vegetation optical depth anomalies have a strong correspondence with the MODIS leaf area index (correlation catchment: 0.74, delta: 0.88). Land surface temperature anomalies derived from passive microwave observations match best with those of ERA5-Land (catchment: 0.88, delta: 0.81) as compared to MODIS nighttime land surface temperature (LST) (catchment: 0.70, delta: 0.65). Although surface soil moisture anomalies from passive microwave observations and ERA5-Land correlate reasonably well (catchment: 0.72, delta: 0.69), an in-depth evaluation over the delta uncovered situations where passive microwave satellites record strong fluctuations, while ERA5-Land does not. This is further analysed using information on inundated area, river discharge, and precipitation. The passive microwave soil moisture signal demonstrates a response to both the inundated area and precipitation. ERA5-Land however, which, by default, does not account for any lateral influx from rivers, only shows a response to the precipitation information that is used as forcing. This also causes the reanalysis model to miss record low land surface temperature values as it underestimates the latent heat flux in certain years. These findings demonstrate the complexity of this hydrological system and suggest that future land surface model generations should also include lateral land surface exchange. Also, our study highlights the importance of maintaining and improving climate data records of soil moisture, vegetation, and land surface temperature from passive microwave observations and other observation systems. [ABSTRACT FROM AUTHOR]

Vegetation conditions can be monitored on a global scale using remote sensing observations in various wavelength domains. In the microwave domain, data from various spaceborne microwave missions are available from the late 1970s onwards. From these observations, vegetation optical depth (VOD) can be estimated, which is an indicator of the total canopy water content and hence of above-ground biomass and its moisture state. Observations of VOD anomalies would thus complement indicators based on visible and near-infrared observations, which are primarily an indicator of an ecosystem's photosynthetic activity. Reliable long-term vegetation state monitoring needs to account for the varying number of available observations over time caused by changes in the satellite constellation. To overcome this, we introduce the standardized vegetation optical depth index (SVODI), which is created by combining VOD estimates from multiple passive microwave sensors and frequencies. Different frequencies are sensitive to different parts of the vegetation canopy. Thus, combining them into a single index makes this index sensitive to deviations in any of the vegetation parts represented. SSM/I-, TMI-, AMSR-E-, WindSat- and AMSR2-derived C-, X- and Ku-band VODs are merged in a probabilistic manner resulting in a vegetation condition index spanning from 1987 to the present. SVODI shows similar temporal patterns to the well-established optical vegetation health index (VHI) derived from optical and thermal data. In regions where water availability is the main control on vegetation growth, SVODI also shows similar temporal patterns to the meteorological drought index scPDSI (self-calibrating Palmer drought severity index) and soil moisture anomalies from ERA5-Land. Temporal SVODI patterns relate to the climate oscillation indices SOI (Southern Oscillation index) and DMI (dipole mode index) in the relevant regions. It is further shown that anomalies occur in VHI and soil moisture anomalies before they occur in SVODI. The results demonstrate the potential of VOD to monitor the vegetation condition, supplementing existing optical indices. It comes with the advantages and disadvantages inherent to passive microwave remote sensing, such as being less susceptible to cloud coverage and solar illumination but at the cost of a lower spatial resolution. The index generation is not specific to VOD and could therefore find applications in other fields. The SVODI products are open-access under Attribution 4.0 International and available at Zenodo, 10.5281/zenodo.7114654. [ABSTRACT FROM AUTHOR]

Land surface properties, such as vegetation cover and soil moisture, influence the partitioning of radiative energy between latent and sensible heat fluxes in daytime hours. During dry periods, soil-water deficit [...]

Many widely-used observational data sets are comprised of several overlapping instrument records. While data intercalibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study, and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process, rather than actual changes in the dynamical properties of the system, is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as the Vegetation Optical Depth and the Normalized Difference Vegetation Index from different satellite sources. We find that varying satellite noise levels and data aggregation schemes can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor noise levels. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience. [ABSTRACT FROM AUTHOR]

There is limited understanding of how vegetation responds to intraseasonal modes of rainfall variability despite their importance in many tropical regions. We use observations of precipitation and X‐band Vegetation Optical Depth (VOD) from 2000 to 2018 to assess the relationships between rainfall and vegetation water content on 25–60‐day timescales. Cross‐spectral analysis identifies coherent intraseasonal relationships between precipitation and VOD, mostly in arid or semi‐arid regions where vegetation is water‐limited. Changes in VOD tend to lag anomalous rainfall, usually within 7 days. The fastest vegetation response is observed in sparsely vegetated areas (median 3 days). Following strong intraseasonal wet events, anomalously high VOD can persist for 2 months after the rainfall peak. This vegetation response can feed back onto the atmosphere, so improved representation of vegetation responses in models has the potential to improve subseasonal‐to‐seasonal forecasts. Plain Language Summary: It is difficult to predict temperature and rainfall more than 2 weeks in advance. Predictions at this timescale are helped by some common patterns of rainfall which cause wet and dry spells lasting from a few weeks to 2 months. Here, we use satellite measurements of rainfall and a vegetation metric that is strongly related to vegetation water content to study the relationships between these rainfall patterns and vegetation across the world. We find many regions where increased rainfall is followed by an increase in vegetation water content, usually with a delay of less than a week. This is most commonly seen in drier locations, where vegetation has a limited supply of water. After a wet spell, vegetation water content can persist above normal levels for over 2 months. This means that changes in vegetation caused by a wet or dry spell have the potential to affect rainfall in the next wet or dry spell, via changes in the energy and water transfers between the land and the atmosphere. Therefore, representing these vegetation responses correctly in models may lead to improvements in temperature and rainfall prediction weeks ahead. Key Points: Vegetation Optical Depth responds to intraseasonal precipitation variability in arid and semi‐arid regionsThe phase difference of this response is usually less than 7 days, and is shortest for sparse vegetationAfter a wet intraseasonal period, the vegetation response can persist for over 2 months [ABSTRACT FROM AUTHOR]

High-resolution (HR) surface soil moisture (SM) observations are important for applications in hydrology and agriculture, among other purposes. For instance, the S2MP (Sentinel-1/Sentinel-2 derived Soil Moisture Product) algorithm was designed to retrieve surface SM at agricultural plot scale using simultaneously Sentinel-1 (S1) backscatter coefficients and Sentinel-2 (S2) NDVI (Normalized Difference Vegetation Index) as inputs to a neural network trained with Water Cloud Model simulations. However, for many applications, including future climate impact assessment at regional level, a resolution of 1 km is already a significant improvement with respect to most of the publicly available SM data sets, which have resolutions of about 25 km. Therefore, in this study, the S2MP algorithm was adapted to work at a resolution of 1 km and extended from croplands (cereals and grasslands) to herbaceous vegetation types. A target resolution of 1 km also allows to explore the use of NDVI derived from Sentinel-3 (S3) instead of S2. The algorithm improvements are evaluated both over Europe and other regions of the globe, for which S1 coverage is poorer. Two sets of SM maps at 1-km resolution were produced with S2MP over six regions of ~ 104 km2 in the southwest and southeast of France, Spain, Tunisia, North America, as well as Australia from 2017 to 2019. The first set of maps was derived from the combination of S1 and S2 data (S1+S2 maps), while the second one was derived from the combination of S1 and S3 (S1+S3 maps). S1+S2 and S1+S3 SM maps were compared to each other and to those of the 1-km resolution Copernicus Global Land Service (CGLS) SM and Soil Water Index (SWI) data sets as well as to the SMAP+S1 product. The S2MP S1+S2 and S1+S3 SM maps are in very good agreement in terms of correlation (R = 0.9), bias (= 0.04 m3 m-3) and standard deviation of the difference (STDD = 0.03 m3 m-3) over the 6 domains investigated in this study. The S2MP maps are well correlated to those from the CGLS SM product (R ~ 0.7-0.8), but the correlations with respect to the other HR maps (CGLS SWI and SMAP+S1) drop significantly over many areas of the 6 domains investigated in this study. In addition, higher correlations between the HR maps were found over croplands and when the 1-km pixels have a very homogeneous land cover. The bias in between the different maps was found to be significant over some areas of the six domains, reaching values ± 0.1 m3 m-3. The S1+S2 maps show a lower STDD with respect to CGLS maps (= 0.06 m3 m-3) than with respect to the SMAP+S1 maps (= 0.1 m3 m-3) for all the 6 domains. Finally, all the HR data sets were also compared to in situ measurements from 5 networks across 5 countries along with coarse resolution (CR) SM products from SMAP, SMOS and the ESA Climate Change Initiative (CCI). While all the CR and HR products show different bias and STDD, the HR products show lower correlations than the CR ones with respect to in situ measurements. [ABSTRACT FROM AUTHOR]

Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter Vegetation Optical Depth (VOD), and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellites operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation status at large scales. VOD has been used to determine aboveground biomass, monitor phenology or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scale. Several VOD products exist differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as normalised difference vegetation index, leaf area index, gross primary production, biomass, vegetation height or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select certain VOD wavelengths for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) of Ku-, X- and C-bands of the harmonised VODCA dataset and level 3 L-band derived from SMOS and SMAP sensors. Within a multivariable regression random forest model for simulating these VOD signals, leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 0.95 of the variance (coefficient of determination). Thereby, the variance in L-band VOD is reproduced spatially and for Ku-, X- and C-band VOD spatially as well as temporally. Analyses of accumulated local effects demonstrate that Ku-, X- and C-band VOD is mostly sensitive to leaf area index and L-band VOD to above-ground biomass. However, for all VODs the global relationships with vegetation properties are non-monotonic and complex and differ with land cover type. This indicates that the use of simple global regressions to estimate single vegetation properties (e.g. above-ground biomass) from VOD is over-simplistic. [ABSTRACT FROM AUTHOR]

The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over decadal time series. Therefore, our objectives are 1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; 2) to test different model structures to estimate LFMC from VOD; and 3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status and fire dynamics. [ABSTRACT FROM AUTHOR]

The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors that are widely used for retrieving such information are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors can potentially overcome such limitations. However, water extent retrieval from SAR data is often impacted by environmental factors, such as wind on water surfaces. Hence, robust retrieval methods are required to reliably monitor water extent over longer time periods . The aim of this study was to develop a robust approach for classifying open water extent in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the hydrometeorological context. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of waterbodies and the median area per waterbody were computed. The validation of the retrieved water maps yielded producer’s accuracies between 84 % and 95 % for calm days and between 74 % and 88 % for windy days. User’s accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information. The observed dynamics of total water area displayed both intra-annual and inter-annual patterns. In addition to differences in seasonality between small (< 1 ha) and large (> 1 ha) waterbodies due to the effect of evaporation during summer, these size classes also responded differently to an extremely wet period from 2019 to 2020 in terms of the increase in the number of waterbodies and the total area covered. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations of this method are related to wind inhibiting the correct water extent retrieval and to the rather long acquisition interval of 12 d over the PPR, which is a result of the observation strategy of Sentinel-1. [ABSTRACT FROM AUTHOR]

Long-term global monitoring of terrestrial gross primary production (GPP) is crucial for assessing ecosystem responses to global climate change. In recent decades, great advances have been made in estimating GPP and many global GPP datasets have been published. These datasets are based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. Although these approaches are well established within the scientific community, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of vegetation optical depth (VOD) to estimate GPP at the global scale for the period 1988–2020. VODCA2GPP applies a previously developed carbon-sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et al., 2020; Zotta et al., 2022​​​​​​​), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and compared against largely independent state-of-the-art GPP datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust performance of VODCA2GPP with only a small bias and good temporal agreement. The comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP exhibits very similar spatial patterns across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but less well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale but rather suggests regionally different long-term changes in GPP. For the shorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and the TRENDY-v7 ensemble, significant increases in global GPP were found. VODCA2GPP can complement existing GPP products and is a valuable dataset for the assessment of large-scale and long-term changes in GPP for global vegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (10.48436/1k7aj-bdz35, Wild et al., 2021). [ABSTRACT FROM AUTHOR]

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. 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. [ABSTRACT FROM AUTHOR]

Many widely-used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study, and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process, rather than actual changes in the dynamical properties of the system, is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as the Vegetation Optical Depth and the Normalized Difference Vegetation Index from different satellite sources. We find that varying satellite noise levels and data aggregation schemes can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor noise levels. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience. [ABSTRACT FROM AUTHOR]