Search

Abstract The Greenhouse gases Observing SATellite 2 (GOSAT-2) was launched in October 2018 as a successor to GOSAT (launched in 2009), the first satellite to specialize in greenhouse gas observations. Compared to the GOSAT sensors, the sensors of GOSAT-2 offer higher performance in most respects. The quality and quantity of data from observations are expected to be improved accordingly. The signal-to-noise ratio (SNR) is better in both the SWIR and TIR bands of TANSO-FTS-2, which is the main sensor of GOSAT-2. This improvement ultimately enhances the accuracy of greenhouse gas concentration analysis. Furthermore, because of the improved SNR in the SWIR band, the northern limit at which data are obtainable in high-latitude regions of the Northern Hemisphere in winter, where observation data have remained unavailable because of weak signal strength, has moved to higher latitudes. As better data are obtained in greater quantities, progress in carbon cycle research for high-latitude regions is anticipated. Moreover, the improvement of SNR in the TIR band is expected to be considerable. Particularly, the resolutions of the vertical concentration distributions of CO2 and CH4 have been improved drastically. The first function introduced for GOSAT-2 that is not in GOSAT is an intelligent pointing mechanism: a cloud area avoidance function using the in-field camera of TANSO-FTS-2. This function can increase the amounts of observation data globally and can improve the accuracy of CO2 emissions estimation and measurements of uptake intensity. The effects are expected to be strong, especially for the tropics because cumulus clouds are the most common cloud type. The intelligent pointing system can avoid the clouds effectively. Another important benefit of TANSO-FTS-2 is that the wavelength range of Band 3 of SWIR has been expanded for measuring carbon monoxide (CO). Because CO originates from combustion, it is used to evaluate some effects of human activities in urban areas and biomass burning in fields. Particularly, black carbon-type aerosols can be measured by the sub-sensor, TANSO-CAI-2, to assess biomass burning along with CO2 and CO by TANSO-FTS-2.

Abstract The dramatic increase of natural gas use in China, as a substitute for coal, helps to reduce CO2 emissions and air pollution, but the climate mitigation benefit can be offset by methane leakage into the atmosphere. We estimate methane emissions from 2010 to 2018 in four regions of China using the GOSAT satellite data and in-situ observations with a high-resolution (0.1° × 0.1°) inverse model and analyze interannual changes of emissions by source sectors. We find that estimated methane emission over the north-eastern China region contributes the largest part (0.77 Tg CH4 yr−1) of the methane emission growth rate of China (0.87 Tg CH4 yr−1) and is largely attributable to the growth in natural gas use. The results provide evidence of a detectable impact on atmospheric methane observations by the increasing natural gas use in China and call for methane emission reductions throughout the gas supply chain and promotion of low emission end-use facilities.

Enhancement ratios among trace gases co-emitted by combustion of fossil fuels vary with emission sources and their combustion efficiency. We used column-averaged dry-air mole fractions of carbon dioxide (XCO _2 ), methane (XCH _4 ), and carbon monoxide (XCO) from the Greenhouse gases Observing SATellite-2 (GOSAT-2), a unique satellite that observes them simultaneously with the same field of view, to derive the enhancement ratios (ΔXCO/ΔXCO _2 , ΔXCO/ΔXCH _4 , and ΔXCH _4 /ΔXCO _2 ) for the 40 most populous cities in the world. These enhancement ratios were used to evaluate the Emissions Database for Global Atmospheric Research (EDGAR). For cities where the difference in the 2015 EDGAR CO emissions between the latest versions (v6.1 and v5.0) was 30% or less, the GOSAT-2 ΔXCO/ΔXCO _2 ratios and EDGAR (v6.1/v7.0) CO/CO _2 emission ratios were in good agreement (correlation coefficient of 0.65). For ∼70% of the cities where the difference of CO emissions exceeded 30%, the EDGAR CO/CO _2 emission ratios using v6.1 CO emissions were in better agreement with the GOSAT-2 ΔXCO/ΔXCO _2 ratios than those using v5.0 CO emissions, indicating that v6.1 CO emissions were improved over v5.0 emissions. However, for the remaining cities, the version upgrade may have reduced the CO emissions too much. The CH _4 and CO emissions for each city were then estimated from the ΔXCH _4 /ΔXCO _2 and ΔXCO/ΔXCO _2 ratios, respectively, by reference to the CO _2 emissions from the Open-data Inventory for Anthropogenic CO _2 (ODIAC) or EDGAR. Compared to our estimates using the ODIAC (EDGAR) CO _2 emissions, the EDGAR v7.0 CH _4 and v6.1 CO emissions were underestimated for 74% (57%) and 76% (53%), respectively, of all cities where results were available. For several cities where emissions were estimated using in situ observations and ground-based remote sensing observations, our results were in reasonable agreement with these results. The implication is that satellite-derived enhancement ratios can provide informative constraints on anthropogenic emissions in megacities.

Considering the significant role of global methane emissions in the Earth’s radiative budget, global or regionally persistent increasing trends in its emission are of great concern. Understanding the regional contributions of various emissions sectors to the growth rate thus has policy relevance. We used a high-resolution global methane inverse model to independently optimize sectoral emissions using GOSAT and ground-based observations for 2009–2020. Annual emission trends were calculated for top-emitting countries, and the sectoral contributions to the total anthropogenic trend were studied. Global total posterior emissions show a growth rate of 2.6 Tg yr ^−2 ( p < 0.05), with significant contributions from waste (1.1 Tg yr ^−2 ) and agriculture (0.9 Tg yr ^−2 ). Country-level aggregated sectoral emissions showed statistically significant ( p < 0.1) trends in total posterior emissions for China (0.56 Tg yr ^−2 ), India (0.22 Tg yr ^−2 ), United States (0.65 Tg yr ^−2 ), Pakistan (0.22 Tg yr ^−2 ) and Indonesia (0.28 Tg yr ^−2 ) among the top methane emitters. Emission sectors contributing to the above country-level emission trend are, China (waste 0.35; oil and gas 0.07 Tg yr ^−2 ), India (agriculture 0.09; waste 0.11 Tg yr ^−2 ), United States (oil and gas 1.0; agriculture 0.07; coal −0.15 Tg yr ^−2 ), Brazil (waste 0.09; agriculture 0.08 Tg yr ^−2 ), Russia (waste 0.04; biomass burning 0.15; coal 0.11; oil and gas −0.42 Tg yr ^−2 ), Indonesia (coal 0.28 Tg yr ^−2 ), Canada (oil and gas 0.08 Tg yr ^−2 ), Pakistan (agriculture 0.15; waste 0.03 Tg yr ^−2 ) and Mexico (waste 0.04 Tg yr ^−2 ). Additionally, our analysis showed that methane emissions from wetlands in Russia (0.24 Tg yr ^−2 ) and central African countries such as Congo (0.09 Tg yr ^−2 ), etc. have a positive trend with a considerably large increase after 2017, whereas Bolivia (−0.09 Tg yr ^−2 ) have a declining trend. Our results reveal some key emission sectors to be targeted on a national level for designing methane emission mitigation efforts.

The global ocean is a major sink of anthropogenic carbon dioxide (CO2) emitted into the atmosphere. Machine learning has been actively used in the past decades to estimate the oceanic sink, but it is still a challenge to obtain an accurate estimate due to scarcely available CO2 measurements. One of the methods to deal with data scarcity was normalizing multiple years’ CO2 values to a reference year to increase the spatial coverage. The practice assumed a constant CO2 trend for the normalization. Here, we used three machine learning models to extract variable ocean CO2 trends on a decadal scale and proposed a method to use the extracted ocean CO2 trends to correct the decadal atmospheric CO2 trends for data normalization. The method minimizes assumptions of using the extracted ocean CO2 trends directly. Comparisons of our CO2 flux estimate with machine learning products included in Global Carbon Budget 2021 indicates that using the variable trends improved the bias resulted from using a constant trend and that the trends are a critical factor for machine learning methods. Our dataset includes monthly distributions of surface ocean CO2 concentration and air-sea flux in 1980-2020 with a spatial resolution of 1×1 degree.

Top-down approaches, such as atmospheric inversions, are a promising tool for evaluating emission estimates based on activity-data. In particular, there is a need to examine carbon budgets at subnational scales (e.g. state/province), since this is where the climate mitigation policies occur. In this study, the subnational scale anthropogenic CO _2 emissions are estimated using a high-resolution global CO _2 inverse model. The approach is distinctive with the use of continuous atmospheric measurements from regional/urban networks along with background monitoring data for the period 2015–2019 in global inversion. The measurements from several urban areas of the U.S., Europe and Japan, together with recent high-resolution emission inventories and data-driven flux datasets were utilized to estimate the fossil emissions across the urban areas of the world. By jointly optimizing fossil fuel and natural fluxes, the model is able to contribute additional information to the evaluation of province–scale emissions, provided that sufficient regional network observations are available. The fossil CO _2 emission estimates over the U.S. states such as Indiana, Massachusetts, Connecticut, New York, Virginia and Maryland were found to have a reasonable agreement with the Environmental Protection Agency (EPA) inventory, and the model corrects the emissions substantially towards the EPA estimates for California and Indiana. The emission estimates over the United Kingdom, France and Germany are comparable with the regional inventory TNO–CAMS. We evaluated model estimates using independent aircraft observations, while comparison with the CarbonTracker model fluxes confirms ability to represent the biospheric fluxes. This study highlights the potential of the newly developed inverse modeling system to utilize the atmospheric data collected from the regional networks and other observation platforms for further enhancing the ability to perform top-down carbon budget assessment at subnational scales and support the monitoring and mitigation of greenhouse gas emissions.

Quantification of spatial and temporal variations in premature mortality attributable to PM2.5 has important implications for air quality control in South and Southeast Asia (SSEA). The number of PM2.5-induced premature deaths during 1999–2014 in SSEA was estimated using an integrated exposure-response model based on 0.01° × 0.01° satellite-retrieved PM2.5 data, population density, and spatially and temporally variable baseline mortality data. The results showed extremely high premature death rates in North India and Bangladesh. PM2.5-induced premature deaths in SSEA increased with small interannual variations from 1999 to 2014 owing to the interannual variations in PM2.5 concentrations. Moreover, four scenarios on the effects of premature deaths by PM2.5 mitigation efforts based on World Health Organization (WHO) air quality guidelines (AQG) and interim targets (ITs) were investigated for each disease and each country during 1999–2014. Four scenarios based on WHO AQG (10 μg/m3), IT-3 (15 μg/m3), IT-2 (25 μg/m3), and IT-1 (35 μg/m3) resulted in 69.3%, 49.1%, 25.4%, and 12.8% reductions compared to the total reference premature deaths (1256,300), which was calculated using the original PM2.5 datasets. Overall, stroke was the most serious disease associated with air pollution, causing 40% of total premature deaths. Ischemic heart disease was the largest contributor (58%) to the deaths in relatively cleaner air (Scenario 1). The annual rate of change in premature deaths in South Asian countries (India, Bangladesh, and Pakistan) was higher than that in Southeast Asian countries under all scenarios. The results for different scenarios provide insight into the largest health benefits of PM2.5 reduction efforts. Keywords: PM2.5 concentration, Premature mortality, Scenario analysis, Interannual variations, Annual rate of change, South and Southeast Asia

In Asia, much effort is put into reducing methane (CH _4 ) emissions due to the region’s contribution to the recent rapid global atmospheric CH _4 concentration growth. Accurate quantification of Asia’s CH _4 budgets is critical for conducting global stocktake and achieving the long-term temperature goal of the Paris Agreement. In this study, we present top-down estimates of CH _4 emissions from 2009 to 2018 deduced from atmospheric observations from surface network and GOSAT satellite with the high-resolution global inverse model NIES-TM-FLEXPART-VAR. The optimized average CH _4 budgets are 63.40 ± 10.52 Tg y ^−1 from East Asia (EA), 45.20 ± 6.22 Tg y ^−1 from Southeast Asia (SEA), and 64.35 ± 9.28 Tg y ^−1 from South Asia (SA) within the 10 years. We analyzed two 5 years CH _4 emission budgets for three subregions and 13 top-emitting countries with an emission budget larger than 1 Tg y ^−1 , and interannual variabilities for these subregions. Statistically significant increasing trends in emissions are found in EA with a lower emission growth rate during 2014–2018 compared to that during 2009–2013, while trends in SEA are not significant. In contrast to the prior emission, the posterior emission shows a significant decreasing trend in SA. The flux decrease is associated with the transition from strong La Ninña (2010–2011) to strong El Ninño (2015–2016) events, which modulate the surface air temperature and rainfall patterns. The interannual variability in CH _4 flux anomalies was larger in SA compared to EA and SEA. The Southern Oscillation Index correlates strongly with interannual CH _4 flux anomalies for SA. Our findings suggest that the interannual variability in the total CH _4 flux is dominated by climate variability in SA. The contribution of climate variability driving interannual variability in natural and anthropogenic CH _4 emissions should be further quantified, especially for tropical countries. Accounting for climate variability may be necessary to improve anthropogenic emission inventories.

The proxy method, using the ratio of total column CH4 to CO2 to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH4 from satellite data. The present study characterizes the remaining scattering effects in the CH4/CO2 ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH4 for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH4 and CO2 bands. The ratio of partial column CH4 reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites.

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011−2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries.

We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH4 yr−1 for Russia, 12.8 to 14.3 Tg CH4 yr−1 for temperate South America, and 43.2 to 44.9 Tg CH4 yr−1 for contiguous USA, and the values decrease from 54 to 51.3 Tg CH4 yr−1 for China, 26.2 to 25.5 Tg CH4 yr−1 for Europe, and by 12.4 Tg CH4 yr−1 for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC.

Information about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it is difficult to apply such methods to large areas. To address this problem, we have developed several support systems for the automated detection of wild animals in remote sensing images. In this study, we applied one of the developed algorithms, the computer-aided detection of moving wild animals (DWA) algorithm, to thermal remote sensing images. We also performed several analyses to confirm that the DWA algorithm is useful for thermal images and to clarify the optimal conditions for obtaining thermal images (during predawn hours and on overcast days). We developed a method based on the algorithm to extract moving wild animals from thermal remote sensing images. Then, accuracy was evaluated by applying the method to airborne thermal images in a wide area. We found that the producer’s accuracy of the method was approximately 77.3% and the user’s accuracy of the method was approximately 29.3%. This means that the proposed method can reduce the person-hours required to survey moving wild animals from large numbers of thermal remote sensing images. Furthermore, we confirmed the extracted sika deer candidates in a pair of images and found 24 moving objects that were not identified by visual inspection by an expert. Therefore, the proposed method can also reduce oversight when identifying moving wild animals. The detection accuracy is expected to increase by setting suitable observation conditions for surveying moving wild animals. Accordingly, we also discuss the required observation conditions. The discussions about the required observation conditions would be extremely useful for people monitoring animal population changes using thermal remote sensing images.

Greenhouse Gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two Earth-observing instruments: the Thermal and Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer 2 (TANSO-FTS-2) and TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 can be used to perform cloud discrimination in each band. The cloud discrimination algorithm uses minimum reflectance (Rmin) for comparisons with observed top-of-atmosphere reflectance. The creation of cloud-free Rmin requires 10 CAI or CAI-2 data. Thus, Rmin is created from CAI L1B data for a 30-day period in GOSAT, with a revisit time of 3 days. It is necessary to change the way in which 10 observations are chosen for GOSAT-2, which has a revisit time of 6 days. Additionally, Rmin processing for GOSAT CAI data was updated to version 02.00 in December 2016. Along with this change, the resolution of Rmin changed from 1/30° to 500 m. We examined the impact of changes in Rmin on cloud discrimination results using GOSAT CAI data. In particular, we performed comparisons of: (1) Rmin calculated using different methods to choose the 10 observations and (2) Rmin calculated using different generation procedures and spatial resolutions. The results were as follows: (1) The impact of using different methods to choose the 10 observations on cloud discrimination results was small, except for a few cases, e.g., snow-covered regions and sun-glint regions; (2) Cloud discrimination results using Rmin in version 02.00 were better than results obtained using Rmin in the previous version, apart from some special situations. The main causes of this were as follows: (1) The change of used band from band 2 to band 1 for Rmin calculation; (2) The change of spatial resolution of Rmin from 1/30° to 500-m.

Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter). CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1), which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM), a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI) continue to be made. In this study, we examined the impact of various support vectors (SV) on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1) the impact of the choice of different time periods for the training data and (2) the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following. (1) For the period from November to May, it is more effective to use SV generated from training data from February while for the period from June to October it is more effective to use training data from August; (2) In the preparation of SV, features obtained using MODIS bands are more effective than those obtained using the corresponding GOSAT CAI bands to automatically extract cloud training samples.

Abstract: Methane is an important greenhouse gas due to its high warming potential. While quantifying anthropogenic methane emissions is important for evaluation measures applied for climate change mitigation, large emission uncertainties still exist for many source categories. To evaluate anthropogenic methane emission inventory in various regions over the globe, we extract emission signatures from column-average methane observations (XCH4) by GOSAT (Greenhouse gases Observing SATellite) satellite using high-resolution atmospheric transport model simulations. XCH4 abundance due to anthropogenic emissions is estimated as the difference between polluted observations from surrounding cleaner observations. Here, reduction of observation error, which is large compared to local abundance, is achieved by binning the observations over large region according to model-simulated enhancements. We found that the local enhancements observed by GOSAT scale linearly with inventory based simulations of XCH4 for the globe, East Asia and North America. Weighted linear regression of observation derived and inventory-based XCH4 anomalies was carried out to find a scale factor by which the inventory agrees with the observations. Over East Asia, the observed enhancements are 30% lower than suggested by emission inventory, implying a potential overestimation in the inventory. On the contrary, in North America, the observations are approximately 28% higher than model predictions, indicating an underestimation in emission inventory. Our results concur with several recent studies using other analysis methodologies, and thus confirm that satellite observations provide an additional tool for bottom-up emission inventory verification.

n/a

Understanding the atmospheric distribution of water (H 2 O) is crucial for global warming studies and climate change mitigation. In this context, reliable satellite data are extremely valuable for their global and continuous coverage, once their quality has been assessed. Short-wavelength infrared spectra are acquired by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) aboard the Greenhouse gases Observing Satellite (GOSAT). From these, column-averaged dry-air mole fractions of carbon dioxide, methane and water vapor (XH 2 O) have been retrieved at the National Institute for Environmental Studies (NIES, Japan) and are available as a Level 2 research product. We compare the NIES XH 2 O data, Version 02.21, with retrievals from the ground-based Total Carbon Column Observing Network (TCCON, Version GGG2014). The datasets are in good overall agreement, with GOSAT data showing a slight global low bias of −3.1% ± 24.0%, good consistency over different locations (station bias of −1.53% ± 10.35%) and reasonable correlation with TCCON (R = 0.89). We identified two potential sources of discrepancy between the NIES and TCCON retrievals over land. While the TCCON XH 2 O amounts can reach 6000–7000 ppm when the atmospheric water content is high, the correlated NIES values do not exceed 5500 ppm. This could be due to a dry bias of TANSO-FTS in situations of high humidity and aerosol content. We also determined that the GOSAT-TCCON differences directly depend on the altitude difference between the TANSO-FTS footprint and the TCCON site. Further analysis will account for these biases, but the NIES V02.21 XH 2 O product, after public release, can already be useful for water cycle studies.

Remotely sensed solar-induced chlorophyll fluorescence "SIF" is applicable as an indicator of changing photosynthetic activity in terrestrial ecosystems. The vegetation of Borneo has previously been affected by drought and fire during El Niño events. Changes in satellite-based SIF data during an El Niño event in Borneo in 2015 were examined using three satellites-GOSAT, GOME-2, and OCO-2-covering the whole island and its southern and northern areas, respectively. Relationships between environmental factors and vegetation damage, precipitation, fire incidence, vegetation indices, and gross primary production "GPP", which were determined using machine-learning methods, were also examined for the period 2007-2018. SIF tended to be low in dry seasons, even in normal years, possibly because of increased drought stress and / or a higher incidence of fires with less precipitation. During the dry season of 2015, there were significant reductions in SIF in southern Borneo where fires were frequent. Other vegetation indices and GPP were also lower. Serious drought conditions with frequent fires during the El Niño event might have caused ecological degradation throughout Borneo, with a significant decrease in SIF. [ABSTRACT FROM AUTHOR]

This article discusses the rapid economic development in several Asian countries, which has led to significant land use changes, including deforestation, urbanization, and agricultural conversion. These changes pose a threat to essential ecosystem functions and services. The article highlights the use of remote sensing technology to monitor and quantify these changes, providing valuable insights for land management and policy decisions. It also presents various studies that examine land degradation and its environmental implications, as well as the relationships between land systems, ecosystem services, and sustainable management. Additionally, the article addresses agricultural land use, soil management, and resource management, offering strategies for sustainable crop growth and soil improvement. Overall, the article provides a comprehensive understanding of the issues surrounding land use, degradation, ecosystem services, and agricultural soil management in Asia. [Extracted from the article]