ESA Climate Change Initiative Research Articles

Evaluating soil moisture retrieval in Arctic and sub-Arctic environments using passive microwave satellite data

ABSTRACT Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m 3 m − 3 ) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the τ − ω model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m 3 m − 3 and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.

OptiSAIL: A system for the simultaneous retrieval of soil, leaf, and canopy parameters and its application to Sentinel-3 Synergy (OLCI+SLSTR) top-of-canopy reflectances

This paper describes the selected algorithm for the ESA climate change initiative vegetation parameters project. Multi- and hyper-spectral, multi-angular, or multi-sensor top-of-canopy reflectance data call for an efficient generic retrieval system which can improve the consistent retrieval of standard canopy parameters as albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and their uncertainties, and exploit the information to retrieve additional parameters (e.g. leaf pigments). We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL (canopy reflectance), PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (soil reflectance anisotropy, moisture effect), and a cloud contamination model. The inversion is gradient based and uses codes created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fAPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. The system is computationally efficient with a rate of 150 pixel s−1 (7 ms per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals and puts real-time processing with this kind of system into reach.

Analyzing the coastal sea level trends from SCMR-reprocessed altimeter data: A case study in the northern South China Sea

With improved altimeter data near the coast, it is possible to use them in analysis of coastal sea level trends. In this study, we assess the sea level trends over January 2002-April 2022 with the SCMR-reprocessed altimeter dataset in the coastal zone of the northern South China Sea (SCS). The reliability of this dataset is confirmed by the inter-comparison with the CMEMS (Copernicus Marine Environment Monitoring Service) Level-3 along-track data product and the ESA CCI (European Space Agency Climate Change Initiative) v2.2 virtual station product, along with the validation against tide gauge records. Compared to two altimeter products, the SCMR has a higher number of data points for the analysis of sea level trends in the study region (28439 vs 1408 for CMEMS and 868 for ESA CCI). The monthly SLA time series from the SCMR dataset are more consistent with those from tide gauge records in terms of higher correlation coefficients (0.37–0.83) and lower root mean square of the differences (RMSD, 33.98–92.19 mm) between altimeter and tide gauge. The along-track sea level trends within the 0–20 km coastal strip range between 2–5 mm yr−1, and the degradation of sea level trends occurs when the along-track trends show significant fluctuations at the spatial scales of ∼ 300 m. Therefore, a further data editing strategy is recommended in this regard. We also find that the ENSO-related signals mainly impact on sea levels in the west of Luzon Strait and of the Philippine coast, but are dampened along the Chinese coast over 2002–2022. The regional mean sea level trend is reduced from 3.54 ± 0.85 mm yr−1 to 3.00 ± 0.84 mm yr−1 after removing the ENSO-related low-frequency variability. Finally, the variation of sea level trends from offshore oceans to coasts is found to be nonlinear, indicating the complexity of sea level changes in the study area. The blending of tide gauge records and the SCMR dataset has been successfully conducted in four tide gauges over a longer period of January 1993-April 2022, which would contribute to the analysis of long-term sea level trends at the local scales.

Cities in the heat: Unveiling the urbanized impacted surface urban heat island of South Korea's metropolises

The present study delves into the dynamics of SUHI in six major cities of South Korea. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), European Space Agency Climate Change Initiative (ESA-CCI) Land use land cover (LULC), wind speed, relative humidity (RH), city size, city mean elevation, and population density data. Findings of this study exhibited higher daytime LST compared to nighttime with evident trend in Seoul, Incheon, and Jeju. Similarly, a great influence of SUHI showed by all cities with averaged SUHI of daytime (1.94 °C) was higher than nighttime (1.23 °C). Notably, the daytime SUHI was more pronounced in inland cities (2.3 °C) whereas, the nighttime SUHI was more evident in ocean cities (1.29 °C). Findings of this study illuminated inverse relationship of wind speed, RH, city size (r2= 0.58, p<0.05) and city mean elevation (r2= 0.41, p<0.05) with SUHI whereas a positive and strong correlation exhibited by population density with SUHI (r2= 0.73/, p<0.05). Findings of this study contribute to a deeper understanding of SUHI patterns in South Korea, by shedding light on various environmental factors which can hold implications for urban planning and more informed decisions to combat with the adverse impacts of SUHI.

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model

GNSS Reflectometry (GNSS-R) is an emerging technique for the remote sensing of the environment. Traditional GNSS-R bio-geophysical parameter retrieval algorithms and deep learning models utilize observables derived from only the peak power of the delay-Doppler maps (DDMs), discarding the rest. This reduces the data available, which potentially hinders estimation accuracy. In addition, reflections from water bodies dominate the signal amplitude, and using only the peak power in those areas is challenging. Motivated by all the above, we propose a novel deep learning retrieval model for biomass estimation that uses the full DDM of surface reflectivity. Experiments using CYGNSS data have illustrated the improvements achieved when using the full DDM of surface reflectivity. Our proposed model was able to estimate biomass, trained using the ESA Climate Change Initiative (CCI) biomass map, outperforming the model that used peak reflectivity. Global and regional analysis is provided along with an illustration of how biomass estimation is achieved when using the full DDM around water bodies. GNSS-R could become an efficient method for biomass monitoring with fast revisit times. However, an elaborate calibration is necessary for the retrieval models, to associate GNSS-R data with bio-geophysical parameters on the ground. To achieve this, further developments with improved training data are required, as well as work using in situ validation data. Nevertheless, using GNSS-R and deep learning retrieval models has the potential to enable fast and persistent biomass monitoring and help us better understand our changing climate.

Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning

Abstract. Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of its rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture, and terrestrial water storage present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterize vegetation functioning through the near-infrared reflectance of vegetation (NIRv) and compare its relation to (i) near-surface soil moisture from the ESA's Climate Change Initiative (CCI) and (ii) total water storage from the Gravity Recovery and Climate Experiment (GRACE) mission at a monthly timescale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy- and other water-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than that of near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. At the same time, we note that this comparison is hampered by different noise levels in these satellite data streams. In line with this, an attribution analysis that examines the relative importance of soil water storage for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of the ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation–water interactions in land surface models to support more accurate climate change projections.

Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977

Abstract. The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km2, respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at https://doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).

Inferring vegetation response to drought at multiscale from long-term satellite imagery and meteorological data in Afghanistan

Drought caused by climate change has significantly increased vegetation vulnerability in Afghanistan during the last decades. This paper investigates vegetation response to drought at multiple scales across the country based on historical data from 1980 to 2020. It explores the multiscale relationships between drought as indicated by the grid-based standardised precipitation evapotranspiration index (SPEI) and vegetation condition as represented by the satellite-derived vegetation anomaly index (VAI). It also examines the links of dominant land cover with drought and their implications for agriculture. We assess the spatiotemporal correlations by integrating TerraClimate grids with timeseries data sourced from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer) images and ESA CCI (European Space Agency Climate Change Initiative) land cover maps. We evaluated the effect of cumulative drought on predominant land covers. Our results show years 2000–2001 and 2017–2018 as the driest in the last four decades, and vegetation substantially correlated to drought with spatial and temporal variations across Afghanistan. The most sensitive vegetation response months are June to July and the most significant cumulative drought impacts on vegetation are 6 to 8 months. The predominant land covers more prone to negative effects under severe drought are (in order) shrubland, rainfed cropland, grassland, trees, and irrigated cropland. These findings suggest the sensitivity of vegetation to drought that future national scale drought management should be focused on. This study also demonstrates the usefulness of open-access data to researchers and planners in data-poor countries.

Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV–Vis satellite products

Abstract. Ozone is a potent air pollutant in the lower troposphere and an important short-lived climate forcer (SLCF) in the upper troposphere. Studies using satellite data to investigate spatiotemporal variability of troposphere ozone (TO3) have predominantly focussed on the tropospheric column metric. This is the first study to investigate long-term spatiotemporal variability in lower tropospheric column ozone (LTCO3, surface–450 hPa sub-column) by merging multiple European Space Agency–Climate Change Initiative (ESA-CCI) products produced by the Rutherford Appleton Laboratory (RAL) Space. We find that in the LTCO3, the degree of freedom of signal (DOFS) from these products varies with latitude range and season and is up to 0.8, indicating that the retrievals contain useful information on lower TO3. The spatial and seasonal variation of the RAL Space products are in good agreement with each other, but there are systematic offsets of up to 3.0–5.0 DU between them. Comparison with ozonesondes shows that the Global Ozone Monitoring Experiment (GOME-1, 1996–2003), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2010) and the Ozone Monitoring Instrument (OMI, 2005–2017) have stable LTCO3 records over their respective periods, which can be merged together. However, GOME-2 (2008–2018) shows substantial drift in its bias with respect to ozonesondes. We have therefore constructed a robust merged data set of LTCO3 from GOME-1, SCIAMACHY and OMI between 1996 and 2017. Comparing the LTCO3 differences between the 1996–2000 and 2013–2017 5-year averages, we find sizeable positive increases (3.0–5.0 DU) in the tropics/sub-tropics, while in the northern mid-latitudes, we find small-scale differences in LTCO3. Therefore, we conclude that there has been a substantial increase in tropical/sub-tropical LTCO3 during the satellite era, which is consistent with tropospheric column ozone (TCO3) records from overlapping time periods (e.g. 2005–2016).

On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake

Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of ‘transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact.

Making the Best Use of GRACE, GRACE‐FO and SMAP Data Through a Constrained Bayesian Data‐Model Integration

AbstractThe Gravity Recovery and Climate Experiment (GRACE, 2003–2017) and its Follow‐On mission GRACE‐FO (2018‐now) provide global estimates of the vertically integrated Terrestrial Water Storage Changes (TWSC). Since 2015, the Soil Moisture Active Passive (SMAP) radiometer observes global L‐band brightness temperatures, which are sensitive to near‐surface soil moisture. In this study, we introduce our newly developed Constrained Bayesian (ConBay) optimization approach to merge the TWSC of GRACE/GRACE‐FO along with SMAP soil moisture data into the ∼10 km resolution W3RA water balance model. ConBay is formulated based on two hierarchical multivariate state‐space models to (I) separate land hydrology compartments from GRACE/GRACE‐FO TWSC, and (II) constrain the estimation of surface soil water storage based on the SMAP data. The numerical implementation is demonstrated over the High Plain (HP) aquifer in the United States between 2015 and 2021. The implementation of ConBay is compared with an unconstrained Bayesian formulation, and our validations are performed against in‐situ USGS groundwater level observations and the European Space Agency (ESA)'s Climate Change Initiative (CCI) soil moisture data. Our results indicate that the single GRACE/GRACE‐FO assimilation improves particularly the groundwater compartment. Adding SMAP data to the ConBay approach controls the updates assigned to the surface storage compartments. For example, correlation coefficients between the ESA CCI and the ConBay‐derived surface soil water storage (0.8) that are considerably higher than those derived from the unconstrained experiment (−0.3) in the North HP. The percentage of updates introduced to the W3RA groundwater storage is also decreased from 64% to 57%.

Soil Moisture and Streamflow Data Assimilation for Streamflow Prediction in the Narmada River Basin

Abstract An accurate streamflow forecast is vital for flood prediction and early warning systems. Notwithstanding the rising frequency and intensity of floods during the summer monsoon season in India, efforts to examine the utility of data assimilation (DA) for streamflow prediction remain limited. We examine soil moisture and streamflow DA to improve streamflow simulations in the Narmada River basin, considered a testbed. Data assimilation was performed using the Variable Infiltration Capacity (VIC) model at four gauge stations in the basin. First, we used the ensemble Kalman filter (EnKF) to assimilate the satellite soil moisture from the European Space Agency Climate Change Initiative (ESA-CCI) to the initial state of the VIC model. We examined the usefulness of observed streamflow from the India Water Resources Information System (India-WRIS) to improve the initial hydrological conditions of the VIC model in the streamflow DA during the summer monsoon (June–September) season from 1980 to 2018. The assimilation of ESA-CCI soil moisture showed less improvement in percent error reduction (PER) and efficiency index (EFF) (less than 2%) than the streamflow DA at all of the four gauge locations in the Narmada basin. On the other hand, the streamflow DA showed a significant improvement in PER and EFF (more than 10%) at all the gauge stations for both mean and high-flow conditions. Streamflow data assimilation improved errors in the magnitude and timing for the major floods in 1994 and 2013. Significance Statement India is considerably affected by floods during the summer monsoon. Floods have increased and are projected to become more frequent under the warming climate. Flood prediction and early warning systems are essential in reducing their impacts and exposure. In the present study, we are interested in examining the role of soil moisture and streamflow data assimilation on flood prediction in the Narmada River basin. Our results show that streamflow data assimilation is more effective in reducing errors and bias in streamflow prediction than soil moisture data assimilation. The hydrologic framework used for the Narmada basin can be extended and evaluated to other flood-prone basins in India.

Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States

ABSTRACT Accurate large-scale soil moisture (SM) retrievals using the daily NASA MERRA-2’ climate reanalysis and ESA’ Climate Change Initiative (CCI) remote sensing datasets are compromised by temporal and spatial ambiguities. To address this deficiency, we assess the accuracy of the above-mentioned datasets against the Soil Climate Analysis Network (SCAN) in-situ measurements at nine sites across the contiguous United States (CONUS) during 2014–2016. The sites are selected to represent different climate regions over the CONUS. SM dynamics from NASA MERRA-2 and ESA CCI are compared with those of SCAN, and an SM dataset is developed based on a spatiotemporal analysis. Our results show that the NASA MERRA-2 and ESA CCI SM datasets have different accuracies at the nine SCAN sites in different seasons. The MERRA-2 and CCI SM datasets have the highest accuracy at sites with the lowest number of extreme events, indicating that both datasets cannot robustly capture extremum soil moisture values. The highest (lowest) agreement between the MERRA-2/CCI and SCAN SM data is observed in April (February) with the nine-site average unbiased root-mean-square-difference (ubRMSD) of 0.0638 cm3/cm3 (0.0914 cm3/cm3). In all the nine sites, the CCI SM data are more accurate than those of MERRA-2 in April–October. The CCI SM data shows greater accuracy in the sites with lower SM values and/or higher SM variability. MERRA-2 and CCI SM datasets show higher and lower accuracy at the sites with pasture and agricultural vegetation, respectively. Finally, a new SM dataset is created by using the more accurate SM data from MERRA-2 and CCI in each site and season.

Impact of Design Factors for ESA CCI Satellite Soil Moisture Data Assimilation over Europe

Abstract 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.

Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model

Abstract. Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process.

Easy come, easy go: Short-term land-use dynamics vis à vis regional economic downturns

The present study postulates distinctive land-use dynamics along the economic cycle, and tests against diverging trends over time of urban and non-urban land-uses with characteristic economic potential. Short-term land-use changes over seven time windows encompassing the last three decades (1992–2020) were investigated in metropolitan Athens (Greece), a mono-centric region experiencing complex economic downturns. Based on diachronic land-use maps with homogeneous spatial resolution and nomenclature derived from ESA Climate Change Initiative (ESA-CCI), a change detection analysis was run considering mean patch size, distance from downtown, and specific entropy-based metrics of landscape diversification (Shannon-Wiener H’ diversity index and Pielou J evenness index). Results of a canonical correlation analysis document differential intensity and spatial direction of change during expansions and recessions associated with distinctive socio-demographic profiles. Metropolitan growth followed a radio-centric (land-saving) model during economic expansions with intense urbanization of fringe land. A more dispersed settlement model – reflecting urban sprawl – was associated with economic stagnations, involving land at progressively distant locations from downtown. Landscape diversification was higher under stagnations and lower during expansions.

Land Use/Cover Change, Fragmentation, and Driving Factors in Nepal in the Last 25 Years

Because of the influence of climate change and human activities, an in-depth analysis of land use/cover change (LUCC) and its drivers in Nepal is important for local community forestry management and sustainable development. This paper analyzed the direction, magnitude, and rate of LUCCs and their spatial aggregation, as well as landscape fragmentation in Nepal, from 1995 to 2020 using the ESA/CCI (European Space Agency Climate Change Initiative) dataset. A total of 10 factors including population, socioeconomic development, climate factors, and forest management factors were selected to determine the dominant driving factors affecting LUCC in Nepal by Principal component analysis (PCA) and linear regression analysis. Our study showed that climate change, human activities, and forest management (e.g., community forestry) all influenced LUCC. In Nepal, land use/cover shifted among forest, shrub, grassland, and cropland from 1995 to 2020, mainly from forest to cropland. The most significant LUCC in recent decades has been caused by the expansion of cropland and urbanization. The area of coniferous and broadleaf forests decreased from 1995 to 2001 due to deforestation and forest degradation, and recovered gradually after 2001, which was attributed to the successful practice of community forestry in Nepal. Accelerated urbanization was also found in Nepal, and the significant expansion of construction land came mainly came from cropland. Land fragmentation in Nepal was severe and exhibited spatial aggregation characteristics. Human activities played a greater role in LUCC in Nepal than climate factors. The community forestry, GDP growth, and precipitation were positive driving factors for increases in forest area, while the development of the services sector and rising temperature were negative driving factors.

Spatiotemporal features of the soil moisture across Northwest China using remote sensing data, reanalysis data, and global hydrological model

Soil moisture is an important factor affecting the change of land surface hydrological processes and the distribution of material and energy exchanges between the land and atmosphere and vegetation’s temporal and spatial distributions, especially in arid and semi-arid regions. This paper focuses on soil moisture features across Northwest China, the core region of the Silk Road Economic Belt. Six soil moisture datasets from the period 1981–2020 were employed, which included ERA5 (the European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis 5), ESA-CCI (European Space Agency Climate Change Initiative), GLDAS (Global Land Data Assimilation System), MERRA-2 (The Modern-Era Retrospective Analysis for Research and Applications, Version 2), RSSSM (A Remote Sensing-based global 10-day resolution Surface Soil Moisture dataset), and SSM-Feng (Regional multimodal fusion of surface soil moisture data in China). The temporal and spatial variation of the linear trend and abrupt change characteristics at seasonal and annual scale were explored. The results are as follows: 1) ESA-CCI, GLDAS, and MERRA-2 showed a slow increase in annual soil moisture tendency at a rate of less than 0.001 m3/m3/year, while ERA5 and SSM-Feng showed a significant decreasing linear trend at a rate of 1.31 × 10−4 m3/m3/year and 1.01 × 10−4 m3/m3/year (p &amp;lt; 0.05), respectively. 2) In autumn and winter, only GLDAS and MERRA-2 showed significant increasing trends. In the growing season (i.e., from April to October), the soil moisture of ESA-CCI, GLDAS, and MERRA-2 significantly increased at the rates of 3.29 × 10−4 m3/m3/year, 3.30 × 10−4 m3/m3/year, and 6.64 × 10−4 m3/m3/year (p &amp;lt; 0.05), respectively. 3) ERA5 and ESA-CCI have frequent abrupt changes in 1984, 1987, and 2006 for ERA5, 2010–2012 and 2019–2020 for ESA-CCI. 4) In terms of spatial variations, most datasets show that soil moisture has increased across most regions. The ERA5, ESA-CCI, GLDAS, MERRA-2, and SSM-Feng datasets show decreased soil moisture in the Tarim Basin. The conclusions of this study deepen the understanding of temporal and spatial variation in soil moisture in arid areas of Northwest China. Through these conclusions, a certain theoretical basis can be provided for the complex water cycle process in the arid region.

A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution

Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m3/m3) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far.