Satellite-derived Surface Research Articles

Interannual variability of spring and summer monsoon growing season carbon exchange at a semiarid savanna over nearly two decades

Eddy covariance measurements of land-atmosphere energy, carbon, and water exchange now span multiple decades at some sites, supporting an improved understanding of flux interannual variability (IAV) and its ecophysiological and physical controls. Most eddy covariance IAV studies have focused on temperate forest ecosystems, where carbon fluxes are large and flux records are longest – but also where IAV is much lower than in dryland regions, which have been identified as an essential driver of the trend and variability in the global terrestrial carbon sink. In this study, we leveraged 19 years of continuous micrometeorological measurements at the AmeriFlux US-SRM mesquite savanna site in southern Arizona, USA to quantify the IAV, trends, and drivers of carbon fluxes during the distinct spring and summer growing seasons. We also assessed the ability of modern satellite and land surface models to capture the IAV of seasonal water and carbon fluxes. Annual net ecosystem production (NEP) was small and highly variable (23 +/- 64 gC m − 2 yr−1). Precipitation and associated measures of water availability determined most of the variability in NEP, largely through their influence on annual and seasonal gross ecosystem productivity (GEP) as opposed to ecosystem respiration (ER). Root-zone soil moisture captured between 73% (spring) and 85% (summer) of GEP variability and between 73% (spring) and 58% (summer) of ER variability. Throughout the study period, soil moisture and greenness increased with associated increases in GEP, ER and NEP. These trends were strongly influenced by very productive and wet summer growing seasons during the last two years, which were characterized by abundant understory grass cover. Typically, less than half of the variability in growing season GEP and evapotranspiration was captured by satellite-based estimates and land surface model simulations with local site forcing and calibration, highlighting the ongoing utility of long-term datasets to support careful model testing and improvement.

Optimal initial conditions for science orbits around Ganymede

An important aspect concerning the dynamics of a probe around a natural satellite is represented by the probe lifetime. In fact, the science orbits, usually characterised by low altitude and high inclination, are subjected to an important increase in eccentricity, due to the mother planet’s gravitational attraction, which can lead the probe to collide with the surface of the natural satellite. In the present work, the issue is addressed at the Jupiter’s moon Ganymede, which represents one of most important targets in the exploration of the Solar System. Thus, taking advantage from theoretical aspects and analytical relationships concerning the evolution of the mean orbit elements of the probe, a numerical investigation aimed at determining the initial conditions of the probe orbit which allow the longest lifetime has been carried out.

Multi-Tempo Forecasting of Soil Temperature Data; Application over Quebec, Canada

The profound impact of soil temperature (TS) on crucial environmental processes, including water infiltration, subsurface movement, plant growth, and its influence on land–atmosphere dynamics, cannot be undermined. While satellite and land surface model-based data are valuable in data-sparse areas, they necessitate innovative solutions to bridge gaps and overcome temporal delays arising from their dependence on atmospheric and hydro–meteorological factors. This research introduces a viable technique to address the lag in the Famine Early Warning Network Land Data Assimilation System (FLDAS). Notably, this approach exhibits versatility, proving highly effective in analyzing datasets characterized by significant seasonal trends, and its application holds immense value in watershed-scaled hydrological research. Leveraging the enhanced state-space (SS) method for forecasting in the FLDAS, this technique harnesses TS datasets collected over time at various depths (0–10 cm, 10–40 cm, and 40–100 cm), employing a multiplicative SS model for modeling purposes. By employing the 1-step, 6-step, and 12-step-ahead models at different depths and 2 locations in Quebec, Canada, the outcomes showcased a performance with an average coefficient of determination (R2) of 0.88 and root mean squared error (RMSE) of 2.073 °C for the dynamic model, R2 of 0.834 and RMSE of 2.979 °C for the 6-step-ahead model, and R2 of 0.921 and RMSE of 1.865 °C for the 12-step-ahead model. The results revealed that as the prediction horizon expands and the length of the input data increases, the accuracy of predictions progressively improves, indicating that this model becomes increasingly accurate over time.

Deformation patterns of icy satellite crusts: Insights for tectonic balancing and fluid migration through structural analysis of terrestrial analogues

The icy satellites of the Solar System, such as Europa and Ganymede, show widespread evidence for tectonic structures that provide insights to infer the kinematics and the mechanical properties of their crusts. Their investigation is pivotal for the understanding of the regimes responsible for their formation and the connection with subsurface layers. Icy satellite tectonics is dominated by extension and shear regimes, while paucity evidence for compression represents an open issue. Structural investigation is constrained at regional-scale coverage of the remote sensing imagery. The study of analogues on Earth represents a strong support for the geologic analysis of the icy satellites. Glaciers represent optimal terrestrial analogues, showing deformation styles at multiple scale similar to those in the icy satellites, and being the excellent sites to further explore, verify and confirm what observed through remote sensing on the icy satellite geology. Although the formation processes differ, the similarity of their structures at surface allows quantifying and predicting the state of deformation in the icy satellites at different scales of investigation. Moreover, glacier deformation shows corridor-like pattern (i.e., shear zone with internal associated deformation), analogous to the main tectonic setting recognized in the icy satellites. This work aims to investigate and compare the tectonic structures of the glaciers with those on the icy satellites, by means of multi-scale approach of both remote-sensing and field survey. We present a structural investigation in the Russell and Isunguata Sermia glaciers, located at the western margin of the Greenland Ice Sheet, where field campaign has been conducted by the UPSIDES project under the Europlanet 2024 RI's Transnational Access field analogue in Kangerlussuaq. This project aims i) to achieve knowledge of the ice tectonic setting at local-scale, ii) to compare with that at regional-scale, and in turn iii) to better understand the tectonic styles and to characterize structures that are exclusively identified at regional-scale (such as in the icy satellites). Field measurements of brittle structures (fractures/faults), concerning the quantification of their azimuth, dip, length, width, throw and spacing, have been performed. In parallel, remote sensing analysis, concerning structural mapping on areas including the locations of the investigated outcrops, allowed to derive the same quantities at regional-scale. In this way, both local- and regional-scale tectonic setting has been investigated, and the stress analysis has been performed. Obtained results have been compared and in turn related with areas that show similar tectonic setting on Ganymede. In particular, the lack of detection of the regional-scale counterpart of the low-angle structures, and in particular the compressional ones that have been recognized at local-scale in the investigated glaciers, has been related to the lack of evidence of such structures in the icy satellite's surfaces. Such comparison allows us to infer a tectonic model that accounts for the existence of deep zones of balanced deformation depicted by diffuse and low-angle structures, and can explain their limited detection at surface and regional-scale investigations.

Improvement of satellite-derived surface solar irradiance estimations using spatio-temporal extrapolation with statistical learning

Estimations of solar surface irradiance (SSI) derived from meteorological satellites are widely used by various actors in the solar industry. However, even state-of-the-art empirical and physical SSI retrieval models exhibit significant errors; the estimations provided by these models are thus traditionally corrected using ground-based measurements of SSI as references. The literature is rich with such correction methods, often called adaptation techniques. Most of the proposed models, however, are local or site-specific, i.e., they do not extrapolate the correction in space and are only applicable to the location of the ground-based measurements.In this work, we propose a novel global adaptation technique, that can extrapolate the correction in both space and time. To that end, we leverage (1) a dense network of measurement stations across France, (2) a relatively large number of predictors, and (3) a non-linear, sophisticated regression algorithm, the Extreme Gradient Boosting. The model is applied to the HelioClim3 database; its performance is benchmarked against raw HelioClim3 estimations, and alternative, simpler adaptation techniques.Our analysis shows that this global model significantly improves satellite-derived SSI estimations from the HelioClim3 database, even when the evaluation is carried out on measurement stations that were not part of the training set of the algorithm. Our proposed model also outperforms all tested alternative global adaptation techniques.These results suggest that global adaptation techniques leveraging advanced machine learning and high dimensionality have the potential to significantly improve satellite-derived SSI estimations, notably more than traditional adaptation approaches. There is certainly room for improvement, but the development of such techniques is a promising research topic.

Erosion evolution and mass loss of polyimide in AO environment using 3D Monte Carlo method

Polyimide films coated on the surface of satellites are exposed to atomic oxygen (AO) environment during service. This will suffer variation of microscopic structure and morphology, leading to corrosion and mass loss in macroscopic. In this work, a 3D undercutting model based on Monte Carlo method (MC) and Ray Tracing method was constructed, with which the AO related erosion feature of polyimide film was evaluated. Then, we predicted the evolution of erosion cross-section of the coated material with defect of different sizes and shapes exposed in higher AO fluence. The macroscopic mass loss was derived by the eroded microscopic volume and material density, during which the actual roughness was considered in our proposed undercutting model. This work provides insights into the study of microscopic 3D erosion morphology and connects with macroscopic mass loss in experiment at higher fluences.

A global Lagrangian eddy dataset based on satellite altimetry

Abstract. The methods used to identify coherent ocean eddies are either Eulerian or Lagrangian in nature, and nearly all existing eddy datasets are based on the Eulerian method. In this study, millions of Lagrangian particles are advected by satellite-derived surface geostrophic velocities over the period of 1993–2019. Using the method of Lagrangian-averaged vorticity deviation (LAVD), we present a global Lagrangian eddy dataset (GLED v1.0, Liu and Abernathey, 2022, https://doi.org/10.5281/zenodo.7349753). This open-source dataset contains not only the general features (eddy center position, equivalent radius, rotation property, etc.) of eddies with lifetimes of 30, 90, and 180 d, but also the trajectories of particles trapped by coherent eddies over the lifetime. We present the statistical features of Lagrangian eddies and compare them with those of the most widely used sea surface height (SSH) eddies, focusing on generation sites, size, and propagation speed. A remarkable feature is that Lagrangian eddies are generally smaller than SSH eddies, with a radius ratio of about 0.5. Also, the validation using Argo floats indicates that coherent eddies from GLED v1.0 exist in the real ocean and have the ability to transport water parcels. Our eddy dataset provides an additional option for oceanographers to understand the interaction between coherent eddies and other physical or biochemical processes in the Earth system.

Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images

Algal blooms are a widespread issue in eutrophic lakes. Compared with the satellite-derived surface algal bloom area and chlorophyll-a (Chla) concentration, algae biomass is a more stable way to reflect water quality. Although satellite data have been adopted to observe the water column integrated algal biomass, the previous methods mostly are empirical algorithms, which are not stable enough for widespread use. This paper proposed a machine learning algorithm based on Moderate Resolution Imaging Spectrometer (MODIS) data to estimate the algal biomass, which was successfully applied to a eutrophic lake in China, Lake Taihu. This algorithm was developed by linking Rayleigh-corrected reflectance to in situ algae biomass data in Lake Taihu (n = 140), and the different mainstream machine learning (ML) methods were compared and validated. The partial least squares regression (PLSR) (R2 = 0.67, mean absolute percentage error (MAPE) = 38.88 %) and support vector machines (SVM) (R2 = 0.46, MAPE = 52.02 %) performed poor satisfactory. In contrast, random forest (RF) and extremely gradient boosting tree (XGBoost) algorithms had higher accuracy (RF: R2 = 0.85, MAPE = 22.68 %; XGBoost: R2 = 0.83, MAPE = 24.06 %), demonstrating greater application potential in algal biomass estimation. Field biomass data were further used to estimate the RF algorithm, which showed acceptable precision (R2 = 0.86, MAPE < 7 mg Chla). Subsequently, sensitivity analysis showed that the RF algorithm was not sensitive to high suspension and thickness of aerosols (rate of change <2 %), and inter-day and consecutive days verification showed stability (rate of change <5 %). The algorithm was also extended to Lake Chaohu (R2 = 0.93, MAPE = 18.42 %), demonstrating its potential in other eutrophic lakes. This study for algae biomass estimation provides technical means with higher accuracy and greater universality for the management of eutrophic lakes.

Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer

We used the Swarm-C accelerometer data to invert the orbital atmospheric density in this study. First, the Swarm-C satellite mission data were obtained from the ESA’s public data platform, and preliminary data error correction was performed. This paper refers to the calibration method of GRACE-A satellite accelerometer data. It adds linear temperature correction on the original basis. Moreover, this study’s accelerometer data correction results were compared with the data correction results published by the ESA. In order to explore the influence of light radiation on the accelerometer, we established a geometric model of Swarm-C to simulate the physical shape of the satellite surface. The light radiation pressure model and the shadow area judgment model were established, the change in the light radiation acceleration during the transition process of the satellite from the umbra area to the penumbra area and then to the shadowless area was studied, and the state transition during the transition process was analyzed. Finally, the atmospheric drag coefficient was calculated based on the Sentman model. Atmospheric density inversion calculations were performed using the above data. We show the spatial distribution of atmospheric density at a fixed latitude, testing our results during geomagnetic storms. We compared the density results with existing research data, demonstrating the effectiveness of our approach.

Deep learning for daily spatiotemporally continuity of satellite surface soil Moisture over eastern China in summer

Except assimilated data of soil moisture (SM), there is always a circumstance that not every spatial point has valid value over a specific region in a day, and there are invalid values or gaps between days in a time series at a spatial point, especially the satellite and ground-based data. Thus, those data are not spatiotemporally seamless on daily time scale, and the seamless data of surface SM is fundamental for research on the land–atmosphere interactions on various time scales. Deep learning methods of multi-perceptron (MLP) and convolution (Conv) neural networks are applied for obtaining spatiotemporally seamless SM over eastern China during May-September of 2015–2020 to combine the advantages of ground-based and remote-sensed data. Firstly, taking atmospheric and land variables as inputs, MLP is trained against the satellite products, including the level-2 product of Chinese FengYun 3C (FY3C), level-3 product of the US Soil Moisture Active Passive (SMAP), and level-2 neural network product of the European Soil Moisture and Ocean Salinity (SMOS). After that, spatiotemporally seamless SM is predicted by MLP, which is stable during training and validation. Compared with the in-situ SM, MLP SM inherits the characteristics of the satellite products, though the quality is slightly improved. Secondly, Conv uses the MLP SM as inputs and is trained against in-situ SM. The unbiased errors are reduced notably that unbiased correlation increases and root mean square errors decreases. At last, from the pattern correlation between precipitation and SM anomalies, the error-reduced Conv SM can well reflect the relationships forced out by the major patterns of rainfall, but the satellite products cannot. In addition, during the evaluation, SMAP has the best quality over eastern China, and SMOS is generally better than FY3C. However, after processed by Conv, the differences of the quality among different satellites are negligible, except some differences in the mean patterns of Conv SM, which is mainly associated with the properties of the satellite products.

Sources of air pollution-related health impacts and benefits of radially applied transportation policies in 14 US cities

As the world becomes increasingly urbanized, growing populations are exposed to poor ambient air quality and at risk of the associated health outcomes. Urban air quality is affected both by local sources of air pollution and sources outside city borders. Policy-makers who develop air quality policies need to know whether it is most effective to focus on local policies or to spend resources fostering larger regional air quality management cooperation. Identifying the fraction of air pollution exposure from emissions as a function of distance from the city is a critical element of air quality management design. We estimate the health burden associated with exposure to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) from county-level anthropogenic sources in and around 14 US cities; this analysis is a test-bed to conduct future global analyses. We use adjoint sensitivities calculated from the chemical transport model GEOS-Chem, high resolution satellite-derived surface concentrations of PM2.5 and NO2, and health impact assessment methods. For the 70.2 million people living in these cities, we estimate that 27,740 PM2.5- and O3-related premature deaths and 126,600 NO2-related new asthma cases were attributable to air pollution exposure in 2011. Development within the GEOS-Chem adjoint framework enables sectoral attribution and policy analysis in addition to the rote assessment of impact. We find that 70% of deaths and nearly 100% of these asthma cases were attributable to anthropogenic emissions. There is great variability in the sources of the anthropogenically-related health impacts; within-urban emissions make up 5% in Austin to 56% in Los Angeles and Phoenix (median: 31%) of urban premature deaths and 18% in Austin to 82% in Los Angeles (median: 59.5%) of new asthma cases, with the remaining portions attributable to emissions from outside the urban area. For each city, we estimate the air quality related health benefits associated with the adoption of a vehicle-miles-traveled fee in that city and in multiple spatial regions surrounding the city. The findings suggest that the proportion of urban air pollution that is regional is greater for premature deaths than new asthma cases and for the eastern US than the western US.

A low-energy ion spectrometer with large field of view and wide energy range onboard a Chinese GEO satellite

Abstract A low-energy ion spectrometer (LEIS), onboard a Chinese navigation satellite in geosynchronous orbit, is one of the primary instruments for plasma detection. The LEIS is implemented by combining a top-hat electrostatic analyzer with a pair of angular scanning deflectors, which enables us to achieve in situ measurement of energetic ions in three-dimensional (3D) space with a large field of view of 360° × 90° and a wide energy range from 50 eV to 25 keV per charge. The key performance parameters including analyzer constant, geometric factor, linear relation function between elevation angles and deflector voltages, and the energy or angular resolutions have been determined by using detailed simulations and calibration experiments on the ground. The preliminary results from on-orbit observations demonstrate that the capability of the present LEIS payload can well meet the mission requirements for sampling the low-energy ion distributions in 3D space, measuring the negative satellite surface potential, and monitoring the magnetospheric storm or substorm activities.

CMIP6 GCM Validation Based on ECS and TCR Ranking for 21st Century Temperature Projections and Risk Assessment

Global climate models (GCMs) from the sixth Coupled Model Intercomparison Project Phases (CMIP6) have been employed to simulate the twenty-first-century temperatures for the risk assessment of future climate change. However, their transient climate response (TCR) ranges from 1.2 to 2.8 °C, whereas their equilibrium climate sensitivity (ECS) ranges from 1.8 to 5.7 °C, leading to large variations in the climatic impact of an anthropogenic increase in atmospheric CO2 levels. Moreover, there is growing evidence that many GCMs are running “too hot” and are hence unreliable for directing policies for future climate changes. Here, I rank 41 CMIP6 GCMs according to how successfully they hindcast the global surface warming between 1980 and 2021 using both their published ECS and TCR estimates. The sub-ensemble of GCMs with the best performance appears to be composed of the models with ECS ranging between 1.8 and 3.0 °C (which confirms previous studies) and TCR ranging between 1.2 and 1.8 °C. This GCM sub-ensemble is made up of a total of 17 models. Depending on the emission scenarios, these GCMs predict a 2045–2055 warming of 1.5–2.5 °C compared to the pre-industrial era (1850–1900). As a result, the global aggregated impact and risk estimates seem to be moderate, which implies that any negative effects of future climate change may be adequately addressed by adaptation programs. However, there are also doubts regarding the actual magnitude of global warming, which might be exaggerated because of urban heat contamination and other local non-climatic biases. A final section is dedicated to highlighting the divergences observed between the global surface temperature records and a number of alternative temperature reconstructions from lower troposphere satellite measurements, tree-ring-width chronologies, and surface temperature records based on rural stations alone. If the global warming reported by the climate records is overestimated, the real ECS and TCR may be significantly lower than what is produced by the CMIP6 GCMs, as some independent studies have already suggested, which would invalidate all of the CMIP6 GCMs.

An Assessment of the Lancaster Sound Polynya Using Satellite Data 1979 to 2022

Situated between Devon Island and Baffin Island, Lancaster Sound is part of Tallurutiup Imanga, which is in the process of becoming the largest marine conservation area in Canada. The cultural and ecological significance of the region is due, in part, to a recurring polynya in Lancaster Sound. The polynya is demarcated by an ice arch that generally forms in mid-winter and collapses in late spring or early summer. Advanced Very High Resolution imagery from 1979 to 2022 was analyzed to determine the position, formation and collapse of the Lancaster Sound ice arch. The location of the ice arch demonstrates high interannual variability, with 512 km between the eastern and western extremes, resulting in a polynya area that can fluctuate between 6000 km2 and 40,000 km2. The timing of the seasonal ice arch formation and collapse has implications with respect to ice transport through Lancaster Sound and the navigability of the Northwest Passage. The date of both the formation and collapse of the ice arch is variable from season to season, with the formation observed between November and April and collapse usually occurring in June or July. A linear trend from 1979 to 2022 indicates that seasonal ice arch duration has declined from 150 to 102 days. The reduction in ice arch duration is a result of earlier collapse dates over the study period and later formation dates, particularly from 1979 to 2000. Lancaster Sound normally freezes west to east each season until the ice arch is established, but there is no statistical relationship between the ice arch location and duration. Satellite surface temperature mapping of the region indicates that the polynya is characterized by sub-resolution leads during winter.

Variability of DMS in the East China Sea and its response to different ENSO categories

Dimethylsulfide (DMS) produced by marine phytoplankton is the most important volatile sulfur compound in ocean surface seawater and has a distinct contribution to global climate change. To assess DMS-climate feedbacks, the interannual variations in seawater DMS concentrations in the East China Sea (ECS) were examined, and their periodic characteristic patterns related to different El Niño-Southern Oscillation (ENSO) categories were discussed. Based on satellite surface seawater temperature (SST) and chlorophyll-a (Chl-a) datasets, a 23-year (1998–2020) dataset of DMS concentrations in the ECS was hindcasted by using a generalized additive mixed model (GAMM). The ENSO events from 1998 to 2020 were selected and divided into persistent, recession and transformed types according to their development process. The variability of surface seawater DMS concentration in the Yangtze River Estuary and its adjacent seas, the coast of Zhejiang and Fujian, and the coast of northern Jiangsu are more strongly influenced by ENSO events than the remainder of the ECS. Furthermore, ENSO alters the seasonal variability in DMS concentrations in the Yangtze River Estuary. The DMS concentrations of Apr-Jul were reduced (enhanced) by approximately 3.05 (0.61) nmol/L during Persistent El Niño (PEN) (Persistent La Niña (PLN)) events, which were reduced by approximately 0.51 and 1.10 nmol/L during recessionary El Niño (REN) event and recessionary La Niña (RLN) event, respectively. However, the DMS concentrations were enhanced by approximately 0.53 (1.29) nmol/L during El Niño-La Niña transition (ELT) (La Niña-El Niño transition (LET)) events. ENSO events affected the DMS concentrations in the surface seawater of the study area by influencing Chl-a and SST, where the short-period interannual variability (2–3 years) of DMS was mainly affected by SST, while the interannual variability of the long period (6 years) was mainly affected by Chl-a. The findings can be applied to the development of DMS concentration prediction methods.

Application of deep convolutional networks for improved risk assessments of post-wildfire drinking water contamination

Climate change continues to increase the frequency of wildfires in the western United States, driven by land use change and prolonged and intensified droughts. Simultaneously, an ongoing extension of communities into wildland areas has been observed over the last decades, increasing the wildfire threat to populations in the wildland–urban interface. In recent years, the problem of increased levels of volatile organic compounds in drinking water distribution systems after wildfires has gained attention as it poses a significant health threat to communities in those wildfire-prone areas. No adequate deterministic process models to predict the risk of high levels of volatile organic compounds in drinking water distribution systems after wildfires are available at this point, leaving data-driven machine learning approaches as solutions for addressing this problem. Here we build on a preceding study and enhance the assessment of maximum contamination exceedance probability predictions by applying deep convolutional neural networks trained with water samples from communities affected by wildfires in California and Oregon. We used satellite surface reflectance imagery in combination with gridded information of topography, fuel load, meteorology, and infrastructure as model inputs. The results show that the ensemble of deep convolutional neural networks increases the accuracy of predictions of post-wildfire water contamination by 4.1% compared to preceding models based on Bayesian regularized shallow feedforward networks, reaching an overall accuracy of 92% for the test dataset. The method provides a practical approach for emergency planning and pre-fire allocation of resources to support the provision of safe drinking water for wildfire-prone communities.

Studying the temperature dependence of NIR reflectance spectra of selected hydrated salts dissolved in water: The case of natron, mirabilite and epsomite as representative for icy-world surfaces

Hydrated salts are thought to be a surface component of many different solid bodies in the solar system such as the icy-world satellites, especially the large ones of Jupiter and Saturn. In this context, three hydrated salts of interest in planetary sciences – natron (Na2CO3·10H2O), mirabilite (Na2SO4·10H2O), and epsomite (MgSO4·7H2O) – were selected and their NIR (0.8–3.6 μm) reflectance properties were studied as a function of temperature after diluting them in water. The main goal of these experiments is to characterize the evolution induced in the hydrated salt NIR spectra by the mixing and melting with water followed by freezing of the samples down to 95 K. Our results show that mixing of hydrated salts with water induces a complex phenomenology characterized by the formation of new bonds and absorption features. In addition, the shape and minimum position of the NIR spectral features here analyzed are temperature-dependent. The present laboratory study will be extremely useful to interpret the high-resolution data of Jupiter's icy satellites surfaces which will be available in the next future thanks to the MISE and MAJIS instruments aboard NASA Europa Clipper and ESA JUICE spacecraft, respectively.

Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale

Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through plant physiological processes, soil moisture tightly regulates the carbon cycle in the environment. Therefore, accurate observations of soil moisture may enhance the modeling of carbon fluxes in a model–data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36-year satellite-derived surface soil moisture observations in combination with flask samples of atmospheric CO2 concentrations. We find that, compared to the default model, the performance of optimized net ecosystem productivity (NEP) and gross primary productivity (GPP) has increased with the RMSEs reduced by 1.62 gC/m2/month and 10.84 gC/m2/month, which indicates the added value of the ESA-CCI soil moisture observations as a constraint on the terrestrial carbon cycle. Additionally, the combination of soil moisture and CO2 concentration in this study improves the representation of inter-annual variability of terrestrial carbon fluxes as well as the atmospheric CO2 growth rate. We thereby investigate the ability of the optimized GPP in responding to drought by comparing continentally aggregated GPP with the drought index. The assimilation of surface soil moisture has been shown to efficiently capture the influences of the sub-annual (≤9 months drought durations) and large-scale (e.g., regional to continental scales) droughts on GPP. This study highlights the significant potential of satellite soil moisture for constraining inter-annual models of the terrestrial biosphere’s carbon cycle and for illustrating how GPP responds to drought at a continental scale.

SMAP soil moisture data assimilation impacts on water quality and crop yield predictions in watershed modeling

Model soil moisture predictions are routinely updated with independent observations using data assimilation techniques. While the impacts of these updates on modeled hydrology are well studied, impacts on predicted water quality and crop yield are unresolved. In this study, we evaluated data assimilation effects for a range of predictions in the Soil and Water Assessment Tool (SWAT). We used satellite surface soil moisture data products from the Soil Moisture Active/Passive (SMAP) mission to update SWAT soil moisture for two U.S. experimental watersheds. We addressed possible limitations to the vertical transfer of surface soil moisture updates to deeper layers in the model by additionally testing a modified soil percolation approach that relies on relative saturation rather than the original thresholding behavior of SWAT. Results at both watersheds demonstrated that data assimilation greatly impacted water quality and crop yield predictions. Modifying the soil percolation algorithm, however, did not improve assimilation results. Assimilation increased median soil moisture (+2% to +6 %) which in turn increased total streamflow (+0.43 to +1.70 m3/s daily). Critically, streamflow alone was not a sufficient predictor for the assimilation changes to water quality predictions as flow component contributions and seasonality differed between sites. A varied response in annual water quality predictions to soil moisture updates was identified (+1.70 to +123 kg/ha total nitrogen; −0.11 to −0.57 kg/ha total phosphorous; +8.1 to +50.0 kg/ha sediment) that was dependent upon the timing of the updates, site characteristics, and change to specific transport and transformation processes. Crop yield predictions were similarly impacted by data assimilation from changes to both water and nutrient availability that varied by crop type. The overall strong and diverse response of water quality and crop yield predictions to soil moisture updates provides evidence that assimilation can impact a range of model predictions. Efforts to improve soil moisture simulations, therefore, have potential for meaningful improvements in targeted, non-hydrologic predictions.

Discharge and electromagnetic radiation behind the hole of simulated charging satellite surface under impact

To evaluate the threat of the impact of micrometeoroids and orbital debris on the satellite, we designed an unequal potential charging device on the satellite surface in the ground experiment. The unequal potential charging device is placed on a metal box to simulate the satellite shell. A small hole is opened on the metal box to study the coupling effect between the electromagnetic pulse generated by the impact and the small hole. We recorded the impact experiment's discharge current and voltage curves and detected the electromagnetic and plasma signals on both sides of the hole. The experimental results show that the plasma entering the metal box through the small hole will release the voltage difference inside and outside the metal box and cause the discharge. The electromagnetic field probe in the metal box also detected the electromagnetic radiation signal of 20∼100 MHz.