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Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions.

Adverse childhood experiences (ACEs) have long been recognized for negatively influencing individual outcomes such that each additional ACE exposure increases the risk for negative health and behavioral outcomes. Little is known, however, about how the more recent accumulation of ACEs occurring in follow-up periods influence global functioning considering the past accumulation of ACEs reported at baseline by justice-involved adolescents. Participants were 851 adolescents who completed the Northwestern Juvenile Project (NJP), a longitudinal survey. OLS regression models were used to examine the influence of follow-up and baseline ACEs on the functional impairment of youth. Results indicate that both follow-up and baseline ACEs were associated with worse functioning over time with baseline ACEs demonstrating a greater effect. This study highlights the importance of assessing accumulations of ACEs over time for adolescents in the juvenile justice system and considering how youth of different characteristics and experiences may differently encounter functional impairment. Implications for offering trauma-informed services to disrupt the effects of adversity on adolescents’ functioning are discussed.

Dry season length strongly influences tropical rainforest vegetation and is largely determined by precipitation patterns1,2. Over the Amazon, the dry season length has increased since 1979 and severe short-term droughts have occurred3,4. However, similar changes have not been investigated for the world’s second largest rainforest, the Congo Basin, where long-term drying and large-scale declines in forest greenness and canopy water content were reported5. Here we present observational evidence for widespread increases in the boreal summer (June–August) dry season length over the Congo Basin since the 1980s, from both hydrological and ecological perspectives. We analysed both dry season onset and dry season end via multiple independent precipitation and satellite-derived vegetation datasets for the period 1979–2015. The dry season length increased by 6.4–10.4 days per decade in the period 1988–2013, primarily attributed to an earlier dry season onset and a delayed dry season end. The earlier dry season onset was caused by long-term droughts due to decreased rainfall in the pre-dry season (April–June). The delayed dry season end resulted from insufficiently replenished soil moisture, which postpones the start of the next wet season and hinders vegetation regrowth. If such changes continue, the enhanced water stress in a warming climate may affect the carbon cycle and alter the composition and structure of evergreen rainforest1,6. The length of the dry season over tropical forests is a determining factor of ecosystem health and drought risk. Hydroclimate and vegetation data show that dry season length has increased over the Congo rainforest since the 1980s, owing to both an earlier dry season onset and a delayed end.

Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur's Grassland Fire Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, and therefore ground-observed measurements are rather limited. In this study, we explore the possibility of whether adding satellite-observed data responding to vegetation water content (vegetation optical depth, VOD) will improve DOC prediction when compared with the existing satellite-observed data responding to DOC prediction models based on vegetation greenness (normalised difference vegetation index, NDVI). First, statistically significant relationships are established between selected ground-observed DOC and satellite-observed vegetation datasets (NDVI and VOD) with an r2 up to 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 of 0.44 to 0.55. Results suggest that VOD-based DOC estimation can reasonably reproduce ground-based observations in space and time and is comparable to the existing NDVI-based DOC estimation models.

We investigated the use of a recently developed satellite-based vegetation optical depth (VOD) data set to estimate fire severity and carbon emission over Australian tropical savannahs. VOD is sensitive to the dynamics of all aboveground vegetation and available nearly every two days. For areas burned during 2003–2010, we calculated the VOD change (ΔVOD) pre- and post-fire and the associated loss in the above ground biomass carbon. ΔVOD agreed well with the Normalized Burn Ratio change (ΔNBR) which is the metric used to estimate fire severity and carbon loss compared well with modelled emissions from the Global Fire Emissions Database (GFED). We found that the ΔVOD and ΔNBR are generally linearly related. The Pearson correlation coefficients (r) between VOD- and GFED-based fire carbon emissions for monthly and annual total estimates are very high, 0.92 and 0.96, respectively. A key feature of fire carbon emissions is the strong inter-annual variation, ranging from 21.1 Mt in 2010 to 84.3 Mt in 2004. This study demonstrates that a reasonable estimate of fire severity and carbon emissions can be achieved in a timely manner based on multiple satellite observations over Australian tropical savannahs, which can be complementary to the currently used approaches.

Since the 1960s, large-scale deforestation in the Amazon Basin has contributed to rising global CO2 concentrations and to climate change. Recent advances in satellite observations enable estimates of gross losses of above-ground biomass (AGB) stocks due to deforestation. However, because of simultaneous regrowth, the net contribution of deforestation emissions to rising atmospheric CO2 concentrations is poorly quantified. Climate change may also reduce the potential for forest regeneration in previously disturbed regions. Here, we address these points of uncertainty with a machine-learning approach that combines satellite observations of AGB with climate data across the Amazon Basin to reconstruct annual maps of potential AGB during 1993–2012, the above-ground C storage potential of the undisturbed landscape. We derive a 2.2 Pg C loss of AGB over the study period, and, for the regions where these losses occur, we estimate a 0.7 Pg C reduction in potential AGB. Thus, climate change has led to a decline of ~1/3 in the capacity of these disturbed forests to recover and recapture the C lost in disturbances during 1993–2012. Our approach further shows that annual variations in land use change mask the natural relationship between the El Niño/Southern Oscillation and AGB stocks in disturbed regions.

Elevated CO2 (eCO(2)) experiments provide critical information to quantify the effects of rising CO2 on vegetation 1-6 . Many eCO(2) experiments suggest that nutrient limitations modulate the local magnitude of the eCO(2) effect on plant biomass(1,3,5), but the global extent of these limitations has not been empirically quantified, complicating projections of the capacity of plants to take up CO27,9. Here, we present a data-driven global quantification of the eCO(2) effect on biomass based on 138 eCO(2) experiments. The strength of CO2 fertilization is primarily driven by nitrogen (N) in similar to 65% of global vegetation and by phosphorus (P) in similar to 25% of global vegetation, with N- or P-limitation modulated by mycorrhizal association. Our approach suggests that CO2 levels expected by 2100 can potentially enhance plant biomass by 12 +/- 3% above current values, equivalent to 59 +/- 13 PgC. The globalscale response to eCO(2) we derive from experiments is similar to past changes in greenness(9) and bio-mass(10) with rising CO2, suggesting that CO2 will continue to stimulate plant biomass in the future despite the constraining effect of soil nutrients. Our research reconciles conflicting evidence on CO2 fertilization across scales and provides an empirical estimate of the biomass sensitivity to eCO(2) that may help to constrain climate projections.

This study compared surface soil moisture from 11 separate remote sensing and modelled products across Australia in a common framework. The comparison was based on a correlation analysis between soil moisture products and in situ data collated from three separate ground-based networks: OzFlux, OzNet and CosmOz. The correlation analysis was performed using both original data sets and temporal anomalies, and was supported by examination of the time series plots. The interrelationships between the products were also explored using cluster analyses. The products considered in this study include: Soil Moisture Ocean Salinity (SMOS; both Land Parameter Retrieval Model (LPRM) and L-band Microwave Emission of the Biosphere (LMEB) algorithms), Advanced Microwave Scanning Radiometer 2 (AMSR2; both LPRM and Japan Aerospace Exploration Agency (JAXA) algorithms) and Advanced Scatterometer (ASCAT) satellite-based products, and WaterDyn, Australian Water Resource Assessment Landscape (AWRA-L), Antecedent Precipitation Index (API), Keetch-Byram Drought Index (KBDI), Mount's Soil Dryness Index (MSDI) and CABLE/BIOS2 model-based products. The comparison of the satellite and model data sets showed variation in their ability to reflect in situ soil moisture conditions across Australia owing to individual product characteristics. The comparison showed the satellite products yielded similar ranges of correlation coefficients, with the possible exception of AMSR2_JAXA. SMOS (both algorithms) achieved slightly better agreement with in situ measurements than the alternative satellite products overall. Among the models, WaterDyn yielded the highest correlation most consistently across the different locations and climate zones considered. All products displayed a weaker performance in estimating soil moisture anomalies than the original data sets ( i.e. the absolute values), showing all products to be more effective in detecting interannual and seasonal soil moisture dynamics rather than individual events. Using cluster analysis we found satellite products generally grouped together, whereas models were more similar to other models. SMOS (based on LMEB algorithm and ascending overpass) and ASCAT (descending overpass) were found to be very similar to each other in terms of their temporal soil moisture dynamics, whereas AMSR2 (based on LPRM algorithm and descending overpass) and AMSR2 (based on JAXA algorithm and ascending overpass) were dissimilar. Of the model products, WaterDyn and CABLE were similar to each other, as were the API/AWRA-L and KBDI/MSDI pairs. The clustering suggests systematic commonalities in error structure and duplication of information may exist between products. This evaluation has highlighted relative strengths, weaknesses, and complementarities between products, so the drawbacks of each may be minimised through a more informed assessment of fitness for purpose by end users.

The occurrence of wet and dry growing seasons in water-limited regions remains poorly understood, partly due to the complex role that these regions play in the genesis of their own rainfall. This limits the predictability of global carbon and water budgets, and hinders the regional management of natural resources. Using novel satellite observations and atmospheric trajectory modelling, we unravel the origin and immediate drivers of growing-season precipitation, and the extent to which ecoregions themselves contribute to their own supply of rainfall. Results show that persistent anomalies in growing-season precipitation - and subsequent biomass anomalies - are caused by a complex interplay of land and ocean evaporation, air circulation and local atmospheric stability changes. For regions such as the Kalahari and Australia, the volumes of moisture recycling decline in dry years, providing a positive feedback that intensifies dry conditions. However, recycling ratios increase up to 40%, pointing to the crucial role of these regions in generating their own supply of rainfall; transpiration in periods of water stress allows vegetation to partly offset the decrease in regional precipitation. Findings highlight the need to adequately represent vegetation-atmosphere feedbacks in models to predict biomass changes and to simulate the fate of water-limited regions in our warming climate.

Satellite-derived estimates of increases in terrestrial net primary productivity are less than half of those derived from Earth system models. This discrepancy is explained by over-sensitivity of Earth system models to atmospheric CO2 concentrations. Atmospheric mass balance analyses suggest that terrestrial carbon (C) storage is increasing, partially abating the atmospheric [CO2] growth rate1, although the continued strength of this important ecosystem service remains uncertain2,3,4,5,6. Some evidence suggests that these increases will persist owing to positive responses of vegetation growth (net primary productivity; NPP) to rising atmospheric [CO2] (that is, ‘CO2 fertilization’)5,6,7,8. Here, we present a new satellite-derived global terrestrial NPP data set9,10,11, which shows a significant increase in NPP from 1982 to 2011. However, comparison against Earth system model (ESM) NPP estimates reveals a significant divergence, with satellite-derived increases (2.8 ± 1.50%) less than half of ESM-derived increases (7.6 ± 1.67%) over the 30-year period. By isolating the CO2 fertilization effect in each NPP time series and comparing it against a synthesis of available free-air CO2 enrichment data12,13,14,15, we provide evidence that much of the discrepancy may be due to an over-sensitivity of ESMs to atmospheric [CO2], potentially reflecting an under-representation of climatic feedbacks16,17,18,19,20 and/or a lack of representation of nutrient constraints21,22,23,24,25. Our understanding of CO2 fertilization effects on NPP needs rapid improvement to enable more accurate projections of future C cycle–climate feedbacks; we contend that better integration of modelling, satellite and experimental approaches offers a promising way forward.

In this study we investigate vegetation changes using the normalized difference vegetation index (NDVI) in the Huai River basin during 1982–2013, where double cropping (DC) systems are widespread. The seasonal cycle of NDVI shows a bimodal distribution with two obvious peaks in May and August, respectively. By contrast, both precipitation and temperature reach their maxima in July, which suggests that the seasonal vegetation cycle is dominated by the DC ecosystems. During 1982–2013, annual NDVI has a significant (p

A method for combining two microwave satellite soil moisture products by maximizing the temporal correlation with a reference data set has been developed. The method was applied to two global soil moisture data sets, Japan Aerospace Exploration Agency (JAXA) and Land Parameter Retrieval Model (LPRM), retrieved from the Advanced Microwave Scanning Radiometer 2 observations for the period 2012–2014. A global comparison revealed superior results of the combined product compared to the individual products against the reference data set of ERA-Interim volumetric water content. The global mean temporal correlation coefficient of the combined product with this reference was 0.52 which outperforms the individual JAXA (0.35) as well as the LPRM (0.45) product. Additionally, the performance was evaluated against in situ observations from the International Soil Moisture Network. The combined data set showed a significant improvement in temporal correlation coefficients in the validation compared to JAXA and minor improvements for the LPRM product.

This study assesses two remotely sensed soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), a sensor onboard the Global Change Observation Mission 1 — Water (GCOM-W1) that was launched in May 2012. The soil moisture products were retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm and the Land Parameter Retrieval Model (LPRM) developed by the VU University Amsterdam, in collaboration with the National Aeronautics and Space Administration (NASA). The two products are compared at the global scale. In addition, the products are evaluated against field measurements from 47 stations from the COsmic-ray Soil Moisture Observing System (COSMOS) network which are located in the United States (36 stations), Australia (7 stations), Europe (2 stations) and Africa (2 stations). After examining the retrieval algorithms, it is hypothesized that four factors, namely, physical surface temperatures, surface roughness, vegetation and ground soil wetness conditions, may affect the quality of soil moisture retrievals. From the inter-comparisons at the global scale, the correlations of the two products highlight differences in the representation of the seasonal cycle of soil moisture, with negative correlations found for several regions. Correlations of the anomaly time series were generally strong (R > 0.6) as a result of soil moisture sensitivity to external meteorological forcing and possibly also random noise in the satellite observations. Due to the inherent differences in spatial coverage and measurement scale of the COSMOS and satellite data, the comparisons in terms of correlation coefficients are the most reliable. It was found that both products show rapid decreases in correlation coefficients under low mean temperature ( 0.3) and highly wetted conditions. These findings are further supported by the bias and RMSE estimates which show that JAXA has relatively better performance under dry conditions while the bias and RMSE of LPRM are generally smaller than JAXA, when considered against the four variables. These results provide information on appropriate parameterizations and model selection for the retrieval algorithms and a future research direction to improve the quality by leveraging the strengths of the JAXA and LPRM algorithms. With these, when a multi-year dataset is available, there will be more confidence in defining the seasonal cycle and the data can be decomposed to identify the anomalies where the bias is not relevant.

An integrated understanding of the biogeochemical consequences of climate extremes and land use changes is needed to constrain land-surface feedbacks to atmospheric CO2 from associated climate change. Past assessments of the global carbon balance have shown particularly high uncertainty in Southeast Asia. Here, we use a combination of model ensembles to show that intensified land use change made Southeast Asia a strong source of CO2 from the 1980s to 1990s, whereas the region was close to carbon neutral in the 2000s due to an enhanced CO2 fertilization effect and absence of moderate-to-strong El Niño events. Our findings suggest that despite ongoing deforestation, CO2 emissions were substantially decreased during the 2000s, largely owing to milder climate that restores photosynthetic capacity and suppresses peat and deforestation fire emissions. The occurrence of strong El Niño events after 2009 suggests that the region has returned to conditions of increased vulnerability of carbon stocks., The carbon balance in Southeast Asia is highly uncertain. Here, the authors show that land use changes and occurrence of strong El Niño control decadal shifts in the carbon balance of this region.

Unprecedented droughts hit southern Amazonia in 2005 and 2010, causing a sharp increase in tree mortality and carbon loss. To better predict the rainforest's response to future droughts, it is necessary to understand its behavior during past events. Satellite observations provide a practical source of continuous observations of Amazonian forest. Here we used a passive microwave-based vegetation water content record (i.e., vegetation optical depth, VOD), together with multiple hydrometeorological observations as well as conventional satellite vegetation measures, to investigate the rainforest canopy dynamics during the 2005 and 2010 droughts. During the onset of droughts in the wet-to-dry season (May–July) of both years, we found large-scale positive anomalies in VOD, leaf area index (LAI) and enhanced vegetation index (EVI) over the southern Amazonia. These observations are very likely caused by enhanced canopy growth. Concurrent below-average rainfall and above-average radiation during the wet-to-dry season can be interpreted as an early arrival of normal dry season conditions, leading to enhanced new leaf development and ecosystem photosynthesis, as supported by field observations. Our results suggest that further rainfall deficit into the subsequent dry season caused water and heat stress during the peak of 2005 and 2010 droughts (August–October) that exceeded the tolerance limits of the rainforest, leading to widespread negative VOD anomalies over the southern Amazonia. Significant VOD anomalies were observed mainly over the western part in 2005 and mainly over central and eastern parts in 2010. The total area with significant negative VOD anomalies was comparable between these two drought years, though the average magnitude of significant negative VOD anomalies was greater in 2005. This finding broadly agrees with the field observations indicating that the reduction in biomass carbon uptake was stronger in 2005 than 2010. The enhanced canopy growth preceding drought-induced senescence should be taken into account when interpreting the ecological impacts of Amazonian droughts.

Vegetation change plays a critical role in the Earth's carbon (C) budget and its associated radiative forcing in response to anthropogenic and natural climate change. Existing global estimates of aboveground biomass carbon (ABC) based on field survey data provide brief snapshots that are mainly limited to forest ecosystems. Here we use an entirely new remote sensing approach to derive global ABC estimates for both forest and non-forest biomes during the past two decades from satellite passive microwave observations. We estimate a global average ABC of 362 PgC over the period 1998-2002, of which 65% is in forests and 17% in savannahs. Over the period 1993-2012, an estimated '0.07 PgC yr '1 ABC was lost globally, mostly resulting from the loss of tropical forests ('0.26 PgC yr '1) and net gains in mixed forests over boreal and temperate regions (+0.13 PgC yr '1) and tropical savannahs and shrublands (+0.05 PgC yr '1). Interannual ABC patterns are greatly influenced by the strong response of water-limited ecosystems to rainfall variability, particularly savannahs. From 2003 onwards, forest in Russia and China expanded and tropical deforestation declined. Increased ABC associated with wetter conditions in the savannahs of northern Australia and southern Africa reversed global ABC loss, leading to an overall gain, consistent with trends in the global carbon sink reported in recent studies.

Consistent forest loss estimates are important to understand the role of forest loss and deforestation in the global carbon cycle, for biodiversity studies, and to estimate the mitigation potential of reducing deforestation. To date, most studies have relied on optical satellite data and new efforts have greatly improved our quantitative knowledge on forest dynamics. However, most of these studies yield results for only a relatively short time period or are limited to certain countries. We have quantified large-scale forest loss over a 21-year period (1990–2010) in the tropical biomes of South America using remotely sensed vegetation optical depth (VOD). This passive microwave satellite-based indicator of vegetation water content and vegetation density has a much coarser spatial resolution than optical data but its temporal resolution is higher and VOD is not impacted by aerosols and cloud cover. We used the merged VOD product of the Advanced Microwave Scanning Radiometer (AMSR-E) and Special Sensor Microwave Imager (SSM/I) observations, and developed a change detection algorithm to quantify spatial and temporal variations in forest loss dynamics. Our results compared reasonably well with the newly developed Landsat-based Global Forest Change (GFC) maps, available for the 2001 onwards period (r2 = 0.90 when comparing annual country-level estimates). This allowed us to convert our identified changes in VOD to forest loss area and compute these from 1990 onwards. We also compared these calibrated results to PRODES (r2 = 0.60 when comparing annual state-level estimates). We found that South American forest exhibited substantial interannual variability without a clear trend during the 1990s, but increased from 2000 until 2004. After 2004, forest loss decreased again, except for two smaller peaks in 2007 and 2010. For a large part, these trends were driven by changes in Brazil, which was responsible for 56 % of the total South American forest loss area over our study period according to our results. One of the key findings of our study is that while forest loss decreased in Brazil after 2005, increases in other countries partly offset this trend suggesting that South American forest loss as a whole decreased much less than that in Brazil.

Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur's Grassland Fire 10 Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, therefore, ground observed measurements are rather limited. In this study, we used satellite observed vegetation greenness (Normalised Difference Vegetation Index, NDVI) and vegetation water content (Vegetation Optical Depth, VOD) information to improve the accuracy of the DOC estimation. First, a statistically 15 significant relationship is established between selected ground observed DOC and satellite observed vegetation datasets (NDVI and VOD) with an r2 of 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 of 0.55. Results suggest that satellite based DOC estimation can reasonably reproduce ground based observations in space and time. Comparison with currently available satellite based DOC products shows that our model has a comparable and arguably more balanced performance.

The conventional and established estimates of the amount of carbon taken up by the European terrestrial biosphere from the Atlantic to the Urals rely on two conceptually different types of ground-based measurements. On the one hand, in situ measurements of atmospheric CO2 concentrations are globally obtained at about 100 sites on a regular basis. On the other hand, conventional bottom-up estimates of surface carbon fluxes are obtained from field measurements. Additional in situ measurement sites are needed to better constrain the surface fluxes of the northeastern part of Europe with inverse models, where the strongest uptake is expected. Field campaigns in this region, including flux and biomass measurements, can contribute to bottom-up estimates and serve as an additional anchor point for ABC satellite measurements. Regularly updated inventories and land cover classification are also essential for reliable bottom-up estimates. Likewise, reliable estimates of the flux uncertainties from bottom-up methods that should include all kinds of upscaling uncertainties and propagated measurement errors are essential. In addition to the continuation of existing satellite missions, new satellite missions are needed to provide denser and more accurate and precise measurements of the atmospheric CO2 concentration.

Soil moisture retrievals from China’s recently launched meteorological Fengyun-3B satellite are presented. An established retrieval algorithm – the Land Parameter Retrieval Model (LPRM) – was applied to observations of the Microwave Radiation Imager (MWRI) onboard this satellite. The newly developed soil moisture retrievals from this satellite mission may be incorporated in an existing global microwave-based soil moisture database. To reach consistency with an existing data set of multi-satellite soil moisture retrievals, an intercalibration step was applied to correct brightness temperatures for sensor differences between MWRI and the radiometer of the Tropical Rainfall Measuring Mission’s (TRMM’s) Microwave Imager (TMI), resulting from their individual calibration procedures. The newly derived soil moisture and vegetation optical depth product showed a high degree of consistency with parallel retrievals from both TMI and WindSat, the two satellites that are observing during the same time period and are already part of the LPRM database. High correlation (R > 0.60 at night-time) between the LPRM and official MWRI soil moisture products was shown over the validation networks experiencing semiarid climate conditions. The skills drop below 0.50 over forested regions, with the performance of the LPRM product slightly better than the official MWRI product. To demonstrate the promising use of the MWRI soil moisture in drought monitoring, a case study for a recent and unusually dry East Asian summer Monsoon was conducted. The MWRI soil moisture products are able to effectively delineate the regions that are experiencing a considerable drought, highly in agreement with spatial patterns of precipitation and temperature anomalies. The results in this study give confidence in the soil moisture retrievals from the MWRI onboard Fengyun-3B. The integration of the newly derived products into the existing database will allow a better understanding the diurnal, seasonal and interannual variations, and long-term (35 year) changes of soil moisture at the global scale, consequently enhancing hydrological, meteorological, and climate studies.

Soil moisture is crucial in regulating vegetation productivity and controlling terrestrial carbon uptake. This study aims to quantify the impact of soil moisture on vegetation at large spatial and long-term temporal scales using independent satellite observations. We used a newly developed satellite-derived soil moisture product and the Normalized Difference Vegetation Index (NDVI) to investigate the impact of soil moisture on vegetation across mainland Australia between 1991 and 2009. Our approach relied on multiple statistical methods including: (i) windowed cross correlation; (ii) quantile regression; (iii) piecewise linear regression. We found a strong positive relationship between soil moisture and NDVI, with NDVI typically lagging behind soil moisture by one month. The temporal characteristics of this relation show substantial regional variability. Dry regions with low vegetation density are more sensitive to soil moisture for the high end of the distribution of NDVI than moist regions, suggesting that soil moisture enhances vegetation growth in dry regions and in the early stage in wet regions. Using piecewise linear regression, we detected three periods with different soil moisture trends over the 19. years. The changes in NDVI trends are significant (p < 0.01) with turning points of soil moisture in the beginning of 2000 and the end of 2002. Our findings illustrate the usefulness of the new soil moisture product by demonstrating the impacts of soil moisture on vegetation at various temporal scales. This analysis could be used as a benchmark for coupled vegetation climate models. © 2013 Elsevier Inc.

The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.

Aim Vegetation optical depth (VOD) is an indicator of the water content of both woody and leaf components in terrestrial aboveground vegetation biomass that can be derived from passive microwave remote sensing. VOD is distinct from optical vegetation remote sensing data such as the normalized difference vegetation index in that it is: (a) less prone to saturation in dense canopies; (b) sensitive to both photosynthetic and non-photosynthetic biomass; and (c) less affected by atmospheric conditions. Our primary objective was to analyse a recently developed longterm VOD record and investigate how the vegetation water content of various land-cover types responded to environmental changes and human influences from 1988 to 2008. Location Global. Methods We first conducted Mann‐Kendall trend tests on annual average VOD to identify regions with significant changes over the period 1988‐2008.To diagnose the underlying cause of the observed changes, patterns for these identified regions were further compared with independent datasets of precipitation, crop production, deforestation and fire occurrence.

Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

We modelled the carbon (C) cycle in Mexico with a process-based approach. We used different available products (satellite data, field measurements, models and flux towers) to estimate C stocks and fluxes in the country at three different time frames: present (defined as the period 2000–2005), the past century (1901–2000) and the remainder of this century (2010–2100). Our estimate of the gross primary productivity (GPP) for the country was 2137 ± 1023 Tg C yr−1 and a total C stock of 34 506 ± 7483 Tg C, with 20 347 ± 4622 Pg C in vegetation and 14 159 ± 3861 in the soil. Contrary to other current estimates for recent decades, our results showed that Mexico was a C sink over the period 1990–2009 (+31 Tg C yr−1) and that C accumulation over the last century amounted to 1210 ± 1040 Tg C. We attributed this sink to the CO2 fertilization effect on GPP, which led to an increase of 3408 ± 1060 Tg C, while both climate and land use reduced the country C stocks by −458 ± 1001 and −1740 ± 878 Tg C, respectively. Under different future scenarios the C sink will likely continue over 21st century, with decreasing C uptake as the climate forcing becomes more extreme. Our work provides valuable insights on relevant driving processes of the C-cycle such as the role of drought in marginal lands (e.g. grasslands and shrublands) and the impact of climate change on the mean residence time of C in tropical ecosystems.

Tropical forests are global epicentres of biodiversity and important modulators of climate change, and are mainly constrained by rainfall patterns. The severe short-term droughts that occurred recently in Amazonia have drawn attention to the vulnerability of tropical forests to climatic disturbances. The central African rainforests, the second-largest on Earth, have experienced a long-term drying trend whose impacts on vegetation dynamics remain mostly unknown because in situ observations are very limited. The Congolese forest, with its drier conditions and higher percentage of semi-evergreen trees, may be more tolerant to short-term rainfall reduction than are wetter tropical forests, but for a long-term drought there may be critical thresholds of water availability below which higher-biomass, closed-canopy forests transition to more open, lower-biomass forests. Here we present observational evidence for a widespread decline in forest greenness over the past decade based on analyses of satellite data (optical, thermal, microwave and gravity) from several independent sensors over the Congo basin. This decline in vegetation greenness, particularly in the northern Congolese forest, is generally consistent with decreases in rainfall, terrestrial water storage, water content in aboveground woody and leaf biomass, and the canopy backscatter anomaly caused by changes in structure and moisture in upper forest layers. It is also consistent with increases in photosynthetically active radiation and land surface temperature. These multiple lines of evidence indicate that this large-scale vegetation browning, or loss of photosynthetic capacity, may be partially attributable to the long-term drying trend. Our results suggest that a continued gradual decline of photosynthetic capacity and moisture content driven by the persistent drying trend could alter the composition and structure of the Congolese forest to favour the spread of drought-tolerant species.

The land and ocean act as a sink for fossil-fuel emissions, thereby slowing the rise of atmospheric carbon dioxide concentrations. Although the uptake of carbon by oceanic and terrestrial processes has kept pace with accelerating carbon dioxide emissions until now, atmospheric carbon dioxide concentrations exhibit a large variability on interannual timescales, considered to be driven primarily by terrestrial ecosystem processes dominated by tropical rainforests. We use a terrestrial biogeochemical model, atmospheric carbon dioxide inversion and global carbon budget accounting methods to investigate the evolution of the terrestrial carbon sink over the past 30 years, with a focus on the underlying mechanisms responsible for the exceptionally large land carbon sink reported in 2011 (ref. 2). Here we show that our three terrestrial carbon sink estimates are in good agreement and support the finding of a 2011 record land carbon sink. Surprisingly, we find that the global carbon sink anomaly was driven by growth of semi-arid vegetation in the Southern Hemisphere, with almost 60 per cent of carbon uptake attributed to Australian ecosystems, where prevalent La Niña conditions caused up to six consecutive seasons of increased precipitation. In addition, since 1981, a six per cent expansion of vegetation cover over Australia was associated with a fourfold increase in the sensitivity of continental net carbon uptake to precipitation. Our findings suggest that the higher turnover rates of carbon pools in semi-arid biomes are an increasingly important driver of global carbon cycle inter-annual variability and that tropical rainforests may become less relevant drivers in the future. More research is needed to identify to what extent the carbon stocks accumulated during wet years are vulnerable to rapid decomposition or loss through fire in subsequent years. © 2014 Macmillan Publishers Limited.

Drylands, covering nearly 30% of the global land surface, are characterized by high climate variability and sensitivity to land management. Here, two satellite-observed vegetation products were used to study the long-term (1988–2008) vegetation changes of global drylands: the widely used reflective-based Normalized Difference Vegetation Index (NDVI) and the recently developed passive-microwave-based Vegetation Optical Depth (VOD). The NDVI is sensitive to the chlorophyll concentrations in the canopy and the canopy cover fraction, while the VOD is sensitive to vegetation water content of both leafy and woody components. Therefore it can be expected that using both products helps to better characterize vegetation dynamics, particularly over regions with mixed herbaceous and woody vegetation. Linear regression analysis was performed between antecedent precipitation and observed NDVI and VOD independently to distinguish the contribution of climatic and non-climatic drivers in vegetation variations. Where possible, the contributions of fire, grazing, agriculture and CO2 level to vegetation trends were assessed. The results suggest that NDVI is more sensitive to fluctuations in herbaceous vegetation, which primarily uses shallow soil water, whereas VOD is more sensitive to woody vegetation, which additionally can exploit deeper water stores. Globally, evidence is found for woody encroachment over drylands. In the arid drylands, woody encroachment appears to be at the expense of herbaceous vegetation and a global driver is interpreted. Trends in semi-arid drylands vary widely between regions, suggesting that local rather than global drivers caused most of the vegetation response. In savannas, besides precipitation, fire regime plays an important role in shaping trends. Our results demonstrate that NDVI and VOD provide complementary information and allow new insights into dryland vegetation dynamics.

Satellite observations identify the Mongolian steppes as a hotspot of global biomass reduction, the extent of which is comparable with tropical rainforest deforestation. To conserve or restore these grasslands, the relative contributions of climate and human activities to degradation need to be understood. Here we use a recently developed 21-year (1988-2008) record of satellite based vegetation optical depth (VOD, a proxy for vegetation water content and aboveground biomass), to show that nearly all steppe grasslands in Mongolia experienced significant decreases in VOD. Approximately 60% of the VOD declines can be directly explained by variations in rainfall and surface temperature. After removing these climate induced influences, a significant decreasing trend still persists in the VOD residuals across regions of Mongolia. Correlations in spatial patterns and temporal trends suggest that a marked increase in goat density with associated grazing pressures and wild fires are the most likely non-climatic factors behind grassland degradation. © 2013 Liu et al.

[1] Vegetation optical depth (VOD) retrievals from three satellite-based passive microwave instruments were merged to produce the first long-term global microwave-based vegetation product. The resulting VOD product spans more than two decades and shows seasonal cycles and inter-annual variations that generally correspond with those observed in the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI). Some notable differences exist in the long-term trends: the NDVI, operating in the optical regime, is sensitive to chlorophyll abundance and photosynthetically active biomass of the leaves, whereas the microwave-based VOD is an indicator of the vegetation water content in total above-ground biomass, i.e., including wood and leaf components. Preliminary analyses indicate that the fluctuations in VOD typically correlated to precipitation variations, and that the mutually independent VOD and NDVI do not necessarily respond in identical manners. Considering both products together provides a more robust structural characterization and assessment of long-term vegetation dynamics at the global scale.

Understanding the error structures of remotely sensed soil moisture observations is essential for correctly interpreting observed variations and trends in the data or assimilating them in hydrological or numerical weather prediction models. Nevertheless, a spatially coherent assessment of the quality of the various globally available datasets is often hampered by the limited availability over space and time of reliable in-situ measurements. As an alternative, this study explores the triple collocation error estimation technique for assessing the relative quality of several globally available soil moisture products from active (ASCAT) and passive (AMSR-E and SSM/I) microwave sensors. The triple collocation is a powerful statistical tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three linearly related data sources with independent error structures. Prerequisite for this technique is the availability of a sufficiently large number of timely corresponding observations. In addition to the active and passive satellite-based datasets, we used the ERA-Interim and GLDAS-NOAH reanalysis soil moisture datasets as a third, independent reference. The prime objective is to reveal trends in uncertainty related to different observation principles (passive versus active), the use of different frequencies (C-, X-, and Ku-band) for passive microwave observations, and the choice of the independent reference dataset (ERA-Interim versus GLDAS-NOAH). The results suggest that the triple collocation method provides realistic error estimates. Observed spatial trends agree well with the existing theory and studies on the performance of different observation principles and frequencies with respect to land cover and vegetation density. In addition, if all theoretical prerequisites are fulfilled (e.g. a sufficiently large number of common observations is available and errors of the different datasets are uncorrelated) the errors estimated for the remote sensing products are hardly influenced by the choice of the third independent dataset. The results obtained in this study can help us in developing adequate strategies for the combined use of various scatterometer and radiometer-based soil moisture datasets, e.g. for improved flood forecast modelling or the generation of superior multi-mission long-term soil moisture datasets.

Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m3 m−3) retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA) algorithm with simulations from the Community Land Model (CLM) over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005), both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events, which results in overestimated θ when cracks are present. The hydrological influence of soil physical changes are expected to propagate through the modeled system, such that modeled infiltration, evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution over these soil types when cracks might be present. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models are expected to improve the representation of vertisol hydrology.

[1] The spatiotemporal behavior of soil and vegetation moisture over mainland Australia was analyzed using passive microwave observations by four satellites going back to late 1978. Differences in measurement specifications prevented merging the data directly. A continuous product was developed for Australia by scaling percentiles of the cumulative moisture distribution within each grid cell to the percentiles of a reference sensor. The coefficient of correlation and root-mean-square error between rescaled values and the reference generally suggest good agreement. Using the merged data product, a strong El Nino–Southern Oscillation signal in near-surface hydrology across Australia was confirmed. Spatial patterns of trends in annual averages show that western and northwestern Australia have experienced an increase in vegetation moisture content, while the east and southeast experienced a decrease. Soil moisture showed a similar spatial pattern but with larger regions experiencing a decrease. This could be explained by decreasing rainfall and increasing potential evapotranspiration during the extended winter period (May–September). The results give us reasonable confidence in the time series of soil and vegetation moisture derived by the scaling method developed in this study. Development of a global data set along these lines should enable better estimation of hydrological variables and should increase understanding of the impacts of ocean circulations on terrestrial hydrology and vegetation dynamics.

Woody vegetation in global tropical drylands is of significant importance for both the inter-annual variability of the carbon cycle and local livelihoods. Satellite observations over the past decades provide a unique way to assess the vegetation long-term dynamics across biomes worldwide. Yet, the actual changes in the woody vegetation are always hidden by inter-annual fluctuations of the leaf density, because the most widely used remote sensing data are primarily related to the photosynthetically active vegetation components. Here, we quantify the temporal trends of the non-photosynthetic woody components (i.e. stems and branches) in global tropical drylands during 2000–2012 using the vegetation optical depth (VOD), retrieved from passive microwave observations. This is achieved by a novel method focusing on the dry season period to minimize the influence of herbaceous vegetation, and using MODIS (MODerate resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) data to remove the inter-annual fluctuation of the woody leaf component. We revealed significant trends (p < 0.05) in the woody component (VODwood) in 35% of the areas characterized by a non-significant trend in the leaf component (VODleaf modeled from NDVI), indicating pronounced gradual growth/decline in woody vegetation not captured by traditional assessments. The method is validated using a unique record of ground measurements from the semi-arid Sahel and shows a strong agreement between changes in VODwood and changes in ground observed woody cover (r2 = 0.78). Reliability of the obtained woody component trends is also supported by a review of relevant literatures for eight hot-spot regions of change. The proposed approach is expected to contribute to an improved assessment of e.g. changes in dryland carbon pools. This article is protected by copyright. All rights reserved.