Satellite Observations Research Articles

Greenhouse gas column observations from a portable spectrometer in Uganda

Abstract. The extensive terrestrial ecosystems of tropical Africa are a significant store of carbon and play a key but uncertain role in the atmospheric budgets of carbon dioxide and methane. As ground-based observations in the tropics are scarce compared with other parts of the world, recent studies have instead made use of satellite observations assimilated into atmospheric chemistry and transport models to conclude that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for up to a third of global methane growth, and that the tropical Africa region dominates net carbon emission across the tropics. These studies critically rely on the accuracy of satellite datasets, such as those from the Orbiting Carbon Observatory-2 (OCO-2), the Greenhouse gases Observing SATellite (GOSAT), and the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), along with results from atmospheric transport models, over a geographical region where there are little independent data to test the robustness of published results. In this paper we present the first ground-based observations of greenhouse gas column concentrations over East Africa, obtained using a portable Bruker EM27/SUN Fourier transform infrared (FTIR) spectrometer during a deployment covering the first few months of 2020 in Jinja, Uganda. We operated the instrument near autonomously by way of an automated weatherproof enclosure and observed total atmospheric column concentrations of the greenhouse gases carbon dioxide and methane, as well as carbon monoxide, a useful proxy for emissions from incomplete combustion processes in the region. We discuss the performance of the combined enclosure and spectrometer system that we deployed in Jinja to obtain these data and show comparisons of our ground-based observations with satellite datasets from OCO-2 and Orbiting Carbon Observatory-3 (OCO-3) for carbon dioxide and TROPOMI for methane and carbon monoxide, whilst also comparing our results with concentration data from the GEOS-Chem and Copernicus Atmosphere Monitoring Service (CAMS) atmospheric inversions that provide a means of increasing spatial and temporal coverage where satellite data are not available. For our measurement period, we find mean differences in XCO2 between OCO-2 and the EM27/SUN of −0.29 % and between OCO-3 and the EM27/SUN of −0.28 %. In the case of TROPOMI, the mean difference in XCH4 that we find between TROPOMI and the EM27/SUN is −0.44 %, whilst for XCO the mean difference is −5.65 %. In each of these cases, the mean difference observed between the satellite and ground-based column concentrations is either close to or within the precision and accuracy requirements for the respective missions. With regard to the model and reanalysis comparisons with the EM27/SUN column concentrations, we see mean differences from the EM27/SUN of a global GEOS-Chem inversion for XCO2 of −0.08 %, a regional high-resolution GEOS-Chem inversion for XCH4 of −0.22 %, and the CAMS global reanalysis for XCO of −9.79 %. Our results demonstrate the value of ground-based observations of total column concentrations and show that the combined EM27/SUN and enclosure system employed would be suitable for acquisition of the longer-term observations needed to rigorously evaluate satellite observations and model and reanalysis calculations over tropical Africa.

Satellite observations indicate that chia uses less water than other crops in warm climates

Many parts of the world face severe and prolonged drought conditions, stressing the sustainability of water resources and agriculture. Transitioning to water-efficient crops is one strategy that can help adapt to water scarcity. An emerging drought-tolerant crop of interest is chia (Salvia hispanica). Yet, no study has compared its large-scale water use dynamics to those of widely established crops across the globe. Here, we use satellite data over multiple years to assess the water use efficiency of chia, alfalfa, corn, and soybean globally. Results show that chia consumed 13-38% less water than alfalfa, corn, and soy and assimilated 14-20% more carbon per amount of water used. Substituting 10% of Southwest United States alfalfa cultivation with chia would save 184 million liters of water per growing season, equivalent to the annual water consumption of 1,300 households. Future research shall explore the economic, societal, and environmental ramifications of substituting alfalfa with chia in dry areas worldwide. These insights can guide decision-makers in promoting sustainable agriculture and water resource management.

Partitioning the drivers of Antarctic glacier mass balance (2003–2020) using satellite observations and a regional climate model

We investigate the mass changes of Antarctic glaciers from 2003 to 2020, partitioning them into the contributions of surface mass balance (SMB) and ice discharge, using high-resolution ice mass change estimates derived from the combination of two different types of satellite observations (gravimetry and altimetry) and outputs from a regional climate model. Our analysis indicates that changes in ice discharge have played a dominant role in ongoing ice mass trends and their accelerations, especially in glaciers near the Amundsen and Bellingshausen Seas in West Antarctica. In particular, mass losses of the Thwaites and Pine Island Glaciers have been mostly (>90%) controlled by ice discharge, while the contribution of SMB has been relatively minor. In East Antarctica, SMB accounts for significant portions (>50%) of ice mass imbalances of glaciers in e.g., Dronning Maud Land and Wilkes Land. Ice discharge has also played a notable role in overall mass gain in the region. While our ice discharge estimates agree well with previous estimates from satellite imagery in West Antarctica, notable differences are found in glaciers of East Antarctica and the Antarctic Peninsula. This highlights the need for more observations and improved numerical models to refine these estimates.

Post-process correction improves the accuracy of satellite PM2.5 retrievals

Abstract. Estimates of PM2.5 levels are crucial for monitoring air quality and studying the epidemiological impact of air quality on the population. Currently, the most precise measurements of PM2.5 are obtained from ground stations, resulting in limited spatial coverage. In this study, we consider satellite-based PM2.5 retrieval, which involves conversion of high-resolution satellite retrieval of aerosol optical depth (AOD) into high-resolution PM2.5 retrieval. To improve the accuracy of the AOD-to-PM2.5 conversion, we employ the machine-learning-based post-process correction to correct the AOD-to-PM conversion ratio derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis model data. The post-process-correction approach utilizes a fusion and downscaling of satellite observation and retrieval data, MERRA-2 reanalysis data, various high-resolution geographical indicators, meteorological data, and ground station observations for learning a predictor for the approximation error in the AOD-to-PM2.5 conversion ratio. The corrected conversion ratio is then applied to estimate PM2.5 levels given the high-resolution satellite AOD retrieval data derived from Sentinel-3 observations. The region of study is central Europe during the year 2019. Our model produces PM2.5 estimates with a spatial resolution of 100 m at satellite overpass times with R2 = 0.55 and RMSE = 6.2 µg m−3. The corresponding metrics for monthly averages are R2 = 0.72 and RMSE = 3.7 µg m−3. Additionally, we have incorporated an ensemble of neural networks to provide error envelopes for machine-learning-related uncertainty in the PM2.5 estimates. The proposed approach can produce accurate high-resolution PM2.5 data that can be very useful for air quality monitoring, emission regulation, and epidemiological studies.

Identification of Flood-Prone Areas Using Geo-Informatics: A Case Study of Erbil City, Kurdistan Region, Iraq

Global flood events, intensified by an intricate interplay of natural dynamics and human activities, pose severe environmental and safety threats, driving the demand for advanced vulnerability assessments. This research innovates with a holistic geo-informatics methodology, pinpointing flood-prone regions in Erbil City, Kurdistan Region, Iraq. It harnesses the Analytic Hierarchy Process and Geographic Information System synergies, enriched with insights from multispectral satellite observations, to conduct an exhaustive analysis of key flood susceptibility indicators. These encompass geological, hydrological, meteorological, and anthropogenic parameters, integrated into a robust Geographic Information System analytical matrix. Through strategic factor analysis and prioritization, the study distills a detailed flood susceptibility cartogram, classifying regional vulnerabilities into distinct risk echelons: high (encompassing 25%), moderate (38.1%), low (28.4%), and very low (8.5%). The final flood map highlights an acute susceptibility epicenter in the northeast, contrasting with the minimal threat southwest. This demarcation reaffirms the Analytic Hierarchy Process-Geographic Information System fusion's precision in translating multifaceted data into strategic intelligence, proving instrumental for scholars, administrative frameworks, and developmental strategists. The insights garnered are pivotal in sculpting informed urban development and flood counteractive blueprints, instrumental in mitigating flood repercussions and preserving ecological and human integrities.

First satellite observation of total column water vapor from the Chinese Environmental Trace Gases Monitoring Instrument

First satellite observation of total column water vapor from the Chinese Environmental Trace Gases Monitoring Instrument

Investigating azimuthal propagation of Pc5 geomagnetic pulsations and their equivalent current vortices from ground-based and satellite data

Using phase delays at spaced stations and satellite observations in the magnetosphere during two events, we have studied azimuthal propagation of resonant bursts of geomagnetic pulsations in the Pc5 range. We have also examined propagation of equivalent current vortices during these events. It has been found that the pulsations, observed in the magnetosphere and ionosphere, and the equivalent current vortices in the ionosphere propagate in the azimuthal direction from the dayside to the nightside. Propagation velocities according to ground-based observations are 5–25 km/s; according to satellite observations, 114–236 km/s. Propagation velocities according to satellite observations do not exceed the Alfvén velocity in the magnetosphere, which is 620–1006 km/s. According to data from various instruments, there are signatures of fast magnetosonic and Alfvén waves at a time in one of the events on the satellite. This clearly reflects the transformation of these waves. The geomagnetic latitude of registration of vortex centers coincides with the latitude of the maximum amplitude of geomagnetic pulsations (field line resonances) and decreases by ~15° toward the early hours of MLT. The observed dynamics of Pc5 pulsations and vortices is assumed to reflect MHD wave propagation in the magnetosphere.

Исследование особенностей азимутального распространения геомагнитных Pс5-пульсаций и их эквивалентных токовых вихрей по данным наземных и спутниковых наблюдений

Using phase delays at spaced stations and satellite observations in the magnetosphere during two events, we have studied azimuthal propagation of resonant bursts of geomagnetic pulsations in the Pc5 range. We have also examined propagation of equivalent current vortices during these events. It has been found that the pulsations, observed in the magnetosphere and ionosphere, and the equivalent current vortices in the ionosphere propagate in the azimuthal direction from the dayside to the nightside. Propagation velocities according to ground-based observations are 5–25 km/s; according to satellite observations, 114–236 km/s. Propagation velocities according to satellite observations do not exceed the Alfvén velocity in the magnetosphere, which is 620–1006 km/s. According to data from various instruments, there are signatures of fast magnetosonic and Alfvén waves at a time in one of the events on the satellite. This clearly reflects the transformation of these waves. The geomagnetic latitude of registration of vortex centers coincides with the latitude of the maximum amplitude of geomagnetic pulsations (field line resonances) and decreases by ~15° toward the early hours of MLT. The observed dynamics of Pc5 pulsations and vortices is assumed to reflect MHD wave propagation in the magnetosphere.

Optical Characterization of Coastal Waters with Atmospheric Correction Errors: Insights from SGLI and AERONET-OC

This study identifies the characteristics of water regions with negative normalized water-leaving radiance (nLw(λ)) values in the satellite observations of the Second-generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission–Climate (GCOM-C) satellite. SGLI Level-2 data, along with atmospheric and in-water optical properties measured by the sun photometers in the AErosol RObotic NETwork-Ocean Color (AERONET-OC) from 26 sites globally, are utilized in this study. The focus is particularly on Tokyo Bay and the Ariake Sea, semi-enclosed water regions in Japan where previous research has pointed out the occurrence of negative nLw(λ) values due to atmospheric correction with SGLI. The study examines the temporal changes in atmospheric and in-water optical properties in these two regions, and identifies the characteristics of regions prone to negative nLw(λ) values due to atmospheric correction by comparing the optical properties of these regions with those of 24 other AERONET-OC sites. The time series results of nLw(λ) and the single-scattering albedo (ω(λ)) obtained by the sun photometers at the two sites in Tokyo Bay and Ariake Sea, along with SGLI nLw(λ), indicate the occurrence of negative values in SGLI nLw(λ) in blue band regions, which are mainly attributed to the inflow of absorptive aerosols. However, these negative values are not entirely explained by ω(λ) at 443 nm alone. Additionally, a comparison of in situ nLw(λ) measurements in Tokyo Bay and the Ariake Sea with nLw(λ) values obtained from 24 other AERONET-OC sites, as well as the inherent optical properties (IOPs) estimated through the Quasi-Analytical Algorithm version 5 (QAA_v5), identified five sites—Gulf of Riga, Long Island Sound, Lake Vanern, the Tokyo Bay, and Ariake Sea—as regions where negative nLw(λ) values are more likely to occur. These regions also tend to have lower nLw(λ) values at shorter wavelengths. Furthermore, relatively high light absorption by phytoplankton and colored dissolved organic matter, plus non-algal particles, was confirmed in these regions. This occurs because atmospheric correction processing excessively subtracts aerosol light scattering due to the influence of aerosol absorption, increasing the probability of the occurrence of negative nLw(λ) values. Based on the analysis of atmospheric and in-water optical measurements derived from AERONET-OC in this study, it was found that negative nLw(λ) values due to atmospheric correction are more likely to occur in water regions characterized by both the presence of absorptive aerosols in the atmosphere and high light absorption by in-water substances.

Assessment of forest disturbance and soil erosion in wind farm project using satellite observations

The construction of wind farms, involving road construction and wind turbine installation, severely disrupts natural landscapes. Wind energy expansion in global forested areas has unclear impacts on local forests and ecosystem services. Due to a lack of information on internal road distribution and deployment dates, few studies have assessed forest disturbances caused by wind farms. Environmental issues like vegetation destruction and soil erosion may be overlooked. To address this, we integrated multi-source spaceborne observations to identify deployment dates and road distributions of forest wind farms and mapped related forest disturbances and soil erosion changes. Six global locations were tested, showing over 80 % accuracy. Disturbance intensity ranged from 1.5 to 6.5 ha/MW, with NDVI decreasing by 0.03 to 0.33 in disturbed forest regions. The average soil erosion increase per unit area due to road construction ranged from 24.74 to 274.33 t/hm−1a−1, while wind turbine construction caused an average soil erosion increase ranging from 26.52 to 26.52 to 263.46 t/hm−1a−1. Road construction is the primary cause of forest disturbance, with greater soil erosion increases in mountainous than in plain forests. This method enhances monitoring and understanding of wind farms' environmental impacts.

Field trial of a seismoacoustic method for ice cover parameters monitoring on the Franz Josef Land archipelago

Among the physical parameters of the freezing seas ice cover, ice thickness is of key importance, and its measurement is one of the most important tasks. The increased interest in the state of the sea ice cover as an indicator of global climatic changes, as well as the growth of comprehensive development of the Arctic shelf has caused intensive development of technical and methodological bases for ice observations. Despite the great variety of approaches to ice thickness estimation, all of them are not without weaknesses. Thus, most contact methods imply direct human presence, which significantly complicates the procedure, taking into account, among other factors, the rough weather conditions of the Arctic. Remote methods depend on weather conditions and cannot always provide high spatial resolution. In this connection, it is promising to use satellite observations coupled with the results of autonomous “ground” measurements, which can be seismoacoustic data containing information on the characteristics of elastic waves propagating in the ice-covered sea, is promising. The purpose of this work is to experimentally test a new passive method for monitoring ice cover parameters along long profiles based on the analysis of natural seismoacoustic fields. The article analyzes the data of a full-scale seismoacoustic experiment with a multichannel group of geophones placed on the floating ice of Alexandra Island in the Franz Josef Land archipelago within the framework of a complex expedition of the Russian Geographical Society. The demonstrates that it is in principle possible to use flexural-gravity waves propagating in the floating ice to estimate its characteristics, both in the active mode and by analyzing the ambient noise, is demonstrated. The results of ice parameter reconstruction obtained in a nondestructive manner using seismoacoustic waves and averaged over long profiles are compared with the data of direct contact measurements. This can be further used for monitoring seasonal and multiyear variability of sea ice thickness of freezing seas, including shelf zones.

Dense Water Production in Storfjorden, Svalbard, From a 1‐Year Time Series of Observations and a Simple Model: Are Polynyas in a Warming Arctic Exporting Heat to the Deep Ocean?

AbstractThe formation of dense Brine‐enriched Shelf Water (BSW) in Storfjorden is analyzed during Winter 2016–2017 from mooring observations, a polynya model nudged to satellite observations, and an original BSW production model. The ice season was two months shorter than average, yet 44.2 of sea ice were formed, in line with estimates for the period preceding the atlantification of the Barents Sea in the mid‐2000s: A thinner, more fragile ice may favor polynya openings and frazil ice production. A saline specimen of BSW was produced in large volumes, corresponding to an annual mean transport of 0.042 Sv, larger than previous estimates. The important production is due to the preconditioning of the polynya with a more saline source water, exceeding the pre‐2005 values by 0.37. The BSW overflow was observed on the West Spitsbergen shelf slope from hydrographic sections down to 750 m, thus entering the Norwegian Sea Deep Water layer. Its core temperature was about C warmer than the pre‐2005 values owing to the entrainment of a warmer water in Storfjordrenna, suggesting that a part of the excess surface heat of the Barents Sea could be exported into the deep ocean. Overall our results suggest that dense water formation in the Storfjorden polynya may not, at least for now, be hampered by the atlantification of the Barents Sea, and perhaps even temporarily favored by the more saline source water. Anomalous atmospheric warming during the Winter‐Spring may however disrupt the production, as was observed 1 year before.

MAFNet: Multimodal Asymmetric Fusion Network for Radar Echo Extrapolation

Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset.

The source, transport, deposition and direct radiative effect of mineral dust over western China: A modeling study of July 2022 with focus on the Tibetan Plateau

A Regional Air Quality Model System (RAQMS) driven by WRF was applied to explore the emission, transport, deposition, and direct radiative effect of mineral dust over western China in July 2022, with focus on the Tibetan Plateau (TP). Model validation against ground and satellite observations demonstrated the model reproduced meteorological variables, PM10 concentration, aerosol optical depth (AOD), and extinction coefficients in the vertical reasonably well. There was a dust event during 3–7 July 2022, which was originated from the Taklimakan Desert (TKD) and affected eastern and central TP under anticyclonic flows, resulting in the maximum hourly PM10 concentration exceeding 50 μg m−3 in Lhasa. Shortwave radiation was reduced considerably by dust aerosols over eastern TP, with the maximum decrease in daytime mean shortwave radiation reaching 30 W m−2 around Nyingchi on 5 July. Anthropogenic aerosols dominated PM10 mass in the capital cities of western China (54–67 %), while dust aerosols were dominant in the cities near the deserts. During this dust event, dust aerosols from TKD and Qaidam Desert (QDD) significantly influenced eastern TP, with dust contributions to PM10 mass concentration of 52 %, 76 % and 69 %, respectively, in Chamdo, Lhasa and Nyingzhi, respectively. The total dust emission in western China was about 10.6 Tg in July 2022, with the largest contribution from TKD (63.5 %), followed by Gobi Desert (GB) (26 %). The total deposition of dust was estimated to be 6.2 Tg, in which TKD and GB contributed 66 % and 22 %, respectively. During the study period, about 418 Gg dust aerosols were deposited on TP, 49 % of which was from TKD and 25 % from QDD. Foreign dust sources contributed approximately 7 % and 9 % to dust concentration and total deposition over TP, respectively. Over southern TP, the source contribution to dust deposition was estimated to be 42 %, 24 % and 21 % from TKD, foreign sources and QDD, respectively, suggesting potentially important impact of long-range transboundary dust transport on deposition, surface albedo and climate over TP.

Correlation between Peak Height of Polar Mesospheric Clouds and Mesopause Temperature

Polar mesospheric clouds (PMCs) are ice crystal clouds formed in the mesosphere of high-latitude regions in both the northern (NH) and southern hemispheres (SH). Peak height is an important physical characteristic of PMCs. Satellite observation data from solar occultation for ice experiments (SOFIE) during seven PMC seasons from 2007 to 2014 show that the difference between the height of the mesopause and the peak height of the PMCs (Zmes-Zmax) were inversely correlated with the atmospheric mesopause temperature. The Zmes-Zmax averages for all seasons for the NH and SH were 3.54 km and 2.66 km, respectively. They were smaller at the starting and ending stages of each PMC season and larger in the middle stages. Analysis of the individual cases and statistical results simulated by the PMCs 0-D model also revealed the inverse correlations between the Zmes-Zmax and mesopause temperature, with correlation coefficients of −0.71 and −0.62 for the NH and SH, respectively. The corresponding rates of change of Zmes-Zmax with respect to mesopause temperature were found to be −0.21 km/K and −0.14 km/K, respectively. The formation mechanism of PMCs suggests that a lower temperature around the mesopause can lead to a greater distance and longer time for ice crystals to condense and grow in clouds. Thus, ice crystals sediment to a lower height, making the peak height of the PMCs further away from the mesopause. In addition, disturbances in small-scale dynamic processes tend to weaken the impact of temperature on the peak height of PMCs.

Forest fire size amplifies postfire land surface warming

Climate warming has caused a widespread increase in extreme fire weather, making forest fires longer-lived and larger1–3. The average forest fire size in Canada, the USA and Australia has doubled or even tripled in recent decades4,5. In return, forest fires feed back to climate by modulating land–atmospheric carbon, nitrogen, aerosol, energy and water fluxes6–8. However, the surface climate impacts of increasingly large fires and their implications for land management remain to be established. Here we use satellite observations to show that in temperate and boreal forests in the Northern Hemisphere, fire size persistently amplified decade-long postfire land surface warming in summer per unit burnt area. Both warming and its amplification with fire size were found to diminish with an increasing abundance of broadleaf trees, consistent with their lower fire vulnerability compared with coniferous species9,10. Fire-size-enhanced warming may affect the success and composition of postfire stand regeneration11,12 as well as permafrost degradation13, presenting previously overlooked, additional feedback effects to future climate and fire dynamics. Given the projected increase in fire size in northern forests14,15, climate-smart forestry should aim to mitigate the climate risks of large fires, possibly by increasing the share of broadleaf trees, where appropriate, and avoiding active pyrophytes.

Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.

Disentangling the Effects of Atmospheric and Soil Dryness on Autumn Phenology across the Northern Hemisphere

In recent decades, drought has intensified along with continuous global warming, significantly impacting terrestrial vegetation. High atmospheric water demand, indicated by vapor pressure deficit (VPD), and insufficient soil moisture (SM) are considered the primary factors causing drought stress in vegetation. However, the influences of VPD and SM on the autumn phenology are still unknown. Using satellite observations and meteorological data, we examined the impacts of VPD and SM on the end of the growing season (EOS) across the Northern Hemisphere (>30°N) from 1982 to 2022. We found that VPD and SM were as important as temperature, precipitation, and radiation in controlling the variations in the EOS. Moreover, the EOS was predominantly influenced by VPD or SM in more than one-third (33.8%) of the study area. In particular, a ridge regression analysis indicated that the EOS was more sensitive to VPD than to SM and the other climatic factors, with 25% of the pixels showing the highest sensitivity to VPD. In addition, the effects of VPD and SM on the EOS varied among biome types and climate zones. VPD significantly advanced the EOS in 25.8% of temperate grasslands, while SM had the greatest impact on advancing the EOS in 17.7% of temperate coniferous forests. Additionally, 27.7% of the midlatitude steppe (BSk) exhibited a significant negative correlation between VPD and the EOS, while 19.4% of the marine west coast climate (Cfb) showed a positive correlation between SM and the EOS. We also demonstrated that the correlation between VPD and the EOS was linearly affected by VPD and the leaf area index, while the correlation between SM and the EOS was affected by SM, precipitation, and the leaf area index. Our study highlights the importance of VPD and SM in regulating autumn phenology and enhances our understanding of terrestrial ecosystem responses to climate change.

In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations

Various yield forecasting methods have been reported in literature, but the benefits of assimilating seasonal climate forecasts and satellite observations for in-season yield forecasting during different growth stages have rarely been examined using machine learning. By synthesizing census yields, seasonal climate forecasts (SCF) and satellite-based gross primary production (GPP), this study develops a machine learning (i.e., Random Forest)–based in-season maize yield forecasting model for Northeast China, which produces over 40 % of China's total maize production. Based on the dynamically trained model, 11 numerical experiments are conducted to investigate the benefits of assimilating SCF and GPP relative to the experiments without using SCF and GPP for yield prediction in four forecast phases (planting–three leaves, three leaves–jointing, jointing–tasseling, tasseling–milk). Generally, our yield forecasting model exhibits a promising skill with a low bias of 4.11 %−4.97 %, when the observed climate and GPP are used as inputs. When climate forecasts are assimilated into the model by sampling historical climate, satisfactory yield forecasting can be achieved one month before harvest with a bias of 5.50 %−5.63 %. Bias-correcting climate forecast data from dynamical weather forecast models has a larger benefit for yield prediction with a lower bias of 4.77 %−5.06 %. Furthermore, we found a better benefit of assimilating SCF when compared with GPP during the first three forecast phases, although its relative importance decreases substantially towards harvest. Finally, phase-dependent maps indicating the best model are produced for each county, with historical resampling methods performing best in 40 %, 32 %, 27 %, and 21 % of counties from Phase 1 to Phase 4 and dynamical weather forecast models showing greater predictability in 54 %, 58 %, 58 %, and 52 % of counties during Phases 1–4, respectively. This study provides a useful yield forecasting framework for the breadbasket of China, which can be extended to other crops.

Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge

Atmospheric methane concentrations rose rapidly over the past decade and surged in 2020-2022 but the causes have been unclear. We find from inverse analysis of GOSAT satellite observations that emissions from the wet tropics drove the 2010-2019 increase and the subsequent 2020-2022 surge, while emissions from northern mid-latitudes decreased. The 2020-2022 surge is principally contributed by emissions in Equatorial Asia (43%) and Africa (30%). Wetlands are the major drivers of the 2020-2022 emission increases in Africa and Equatorial Asia because of tropical inundation associated with La Niña conditions, consistent with trends in the GRACE terrestrial water storage data. In contrast, emissions from major anthropogenic emitters such as the United States, Russia, and China are relatively flat over 2010-2022. Concentrations of tropospheric OH (the main methane sink) show no long-term trend over 2010-2022 but a decrease over 2020-2022 that contributed to the methane surge.