Moderate Resolution Imaging Camera (MoRIC) of China’s First Mars Mission Tianwen-1

China's first Mars exploration mission will carry out comprehensive global surveys of the planet from data collected by instruments carried in orbit and roving on the planet itself.Goals of the mission include detailed inspections and surveys of key areas on the surface of Mars.One of the main scientific payloads installed on the orbiter is the moderate resolution camera.Its mission is to image the surface of Mars sufficiently to produce a global remote sensing image map of the planet,and to explore and record changes to the topography of Mars,including major geological structures,and to advance research on topography and geomorphology in general.The moderate resolution camera uses a lightweight and compact integrated design;its primary components are an optical module,a focal plane module,a camera control module,a power and interface module,a camera support module,a thermal control module,and a reference module.Radiometric calibration,color calibration,and geometric calibration have been carried out to ensure that the camera can acquire sufficient accurate data to complete mission goals.This paper introduces the camera's detection mission,its system composition,and its working principle;it also describes the camera's ground calibration tests and their results,and provides a reference for processing the camera's scientific data and for future applications.

Distribution patterns of fire regime in the Pendjari Biosphere Reserve, West Africa

Pendjari Biosphere Reserve(PBR),a primary component of the W-Arly-Pendjari transboundary biosphere reserve,represents the largest intact wild ecosystem and pristine biodiversity spot in West Africa.This savannah ecosystem has long been affected by fire,which is the main ecological driver for the annual rhythm of life in the reserve.Understanding the fire distribution patterns will help to improve its management plan in the region.This study explores the fire regime in the PRB during 2001–2021 in terms of burned area,seasonality,fire frequency,and mean fire return interval(MFRI)by analysing moderate resolution imaging spectroradiometer(MODIS)burned area product.Results indicated that the fire season in the PBR extends from October to May with a peak in early dry season(November–December).The last two fire seasons(2019–2020 and 2020–2021)recorded the highest areas burned in the PBR out of the twenty fire seasons studied.During the twenty years period,8.2%of the reserve burned every 10–11 months and 11.5%burned annually.The largest part of the reserve burned every one to two years(63.1%),while 8.3%burned every two to four years,5.8%burned every four to ten years,and 1.9%burned every ten to twenty years.Only 1.3%of the entire area did not fire during the whole study period.Fire returned to a particular site every 1.39 a and the annual percentage of area burned in the PBR was 71.9%.The MFRI(MFRI<2.00 a)was low in grasslands,shrub savannah,tree savannah,woodland savannah,and rock vegetation.Fire regime must be maintained to preserve the integrity of the PBR.In this context,we suggest applying early fire in tree and woodland savannahs to lower grass height,and late dry season fires every two to three years in shrub savannah to limit the expansion of shrubs and bushes.We propose a laissez-faire system in areas in woodland savannah where the fire frequency is sufficient to allow tree growth.Our findings highlight the utility of remote sensing in defining the geographical and temporal patterns of fire in the PBR and could help to manage this important fire prone area.

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.

Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

Analysis and Evaluation of the Global Aerosol Optical Properties Simulated by an Online Aerosol-coupled Non-hydrostatic Icosahedral Atmospheric Model

Aerosol optical properties are simulated using the Spectral Radiation Transport Model I~）r Aerosol Species （SPRINTARS） coupled with the Non-hydrostatic ICosahedral Atmospheric Model （NICAM）. The 3-year global mean all-sky aerosol optical thickness （AOT） at 550 nm, theAngstr/Sm Exponent （AE） based on AOTs at 440 and 870 nm, and the single scattering albedo （SSA） at 550 nm are estimated at 0.123, 0.657 and 0.944, respectively. For each aerosol species, the mean AOT is within the range of the AeroCom models. Both the modeled all-sky and clear-sky results are compared with observations from the Moderate Resolution Imaging Spectroradiometer （MODIS） and the Aerosol Robotic Network （AERONET）. The simulated spatiotemporal distributions of all-sky AOTs can generally reproduce the MODIS retrievals, and the correlation and model skill can be slightly improved using the clear-sky results over most land regions. The differences between clear-sky and all-sky AOTs are larger over polluted regions. Compared with observations from AERONET, the modeled and observed all-sky AOTs and AEs are generally in reasonable agreement, whereas the SSA variation is not well captured. Although the spatiotemporal distributions of all-sky and clear-sky results are similar, the clear-sky results are generally better correlated with the observations. The clear-sky AOT and SSA are generally lower than the all-sky results, especially in those regions where the aerosol chemical composition is contributed to mostly by sulfate aerosol. The modeled clear-sky AE is larger than the all-sky AE over those regions dominated by hydrophilic aerosol, while the＇opposite is found over regions dominated by hydrophobic aerosol.

Influence of Sea Surface Temperature on Outbreak of Ulva prolifera in the Southern Yellow Sea,China

In this study,using Moderate Resolution Imaging Spectroradiometer(MODIS)satellite images and environmental satellite CCD images,the spatio-temporal distribution of Ulva prolifera in the southern Yellow Sea during the period of 2011–2018 was extracted and combined with MODIS Level3 Photosynthetically Active Radiation(PAR)product data and Earth System Research Laboratory(ESRL)Sea Surface Temperature(SST)data to analyze their influences on the growth and outbreak of Ulva prolifera.The following conclusions were drawn:1)comprehensive analysis of Ulva prolifera distribution during the eight-year period revealed that the coverage area of Ulva prolifera typically exhibited a gradually increasing trend.The coverage area of Ulva prolifera reached a maximum of approximately 1714.21 km^2 during the eight-year period in late June 2015.The area affected by Ulva prolifera fluctuated.In mid-July 2014,the area affected by Ulva prolifera reached a maximum of approximately 39020.63 km^2.2)The average growth rate of Ulva prolifera was positive in May and June but negative in July.During the outbreak of Ulva prolifera,the SST in the southern Yellow Sea tended to increase each month.The SST anomaly and average growth rate of Ulva prolifera were positively correlated in May(R^2=0.62),but not significantly correlated in June or July.3)The variation trends of PAR and SST were approximately the same,and the PAR during this time period maintained a range of 40–50 mol/(m^2·d),providing sufficient illumination for the growth and outbreak of Ulva prolifera.In addition,the abundant nutrients and suitable temperature in the sea area near northern Jiangsu shoal resulted in a high growth rate of Ulva prolifera in May.In summary,the outbreak of Ulva prolifera was closely related to the environmental factors including SST,nutrients,and PAR.Sufficient nutrients and suitable temperatures resulted in a fast growth rate of Ulva prolifera.However,under poor nutrient conditions,even more suitable temperatures were not sufficient to trigger an outbreak of Ulva prolifera.

Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data

MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.

Steady increase in water clarity in Jiaozhou Bay in the Yellow Sea from 2000 to 2018:Observations from MODIS

The Moderate Resolution Imaging Spectroradiometer(MODIS)surface reflectance data were used to analyze the temporal and spatial distribution characteristics of water clarity(Z_(sd))in the Jiaozhou Bay,Qingdao,China,in the Yellow Sea from 2000 to 2018.Z_(sd)retrieval models were regionally optimized using in-situ data with coincident MODIS images,and then were used to retrieve the Z_(sd) products in Jiaozhou Bay from 2000-2018.The analysis of the Z_(sd) results suggests that the spatial distribution of relative Z_(sd) spatial characteristics in Jiaozhou Bay was stable,being higher Z_(sd) in the southeast and a lower Z_(sd) in the northwest.The annual mean Z_(sd) in Jiaozhou Bay showed a significant upward trend,with an annual increase of approximately 0.02 m.Water depth and wind speed were important factors affecting the spatial distribution and annual variation of Z_(sd) in Jiaozhou Bay,respectively.

Vegetation Phenology in Permafrost Regions of Northeastern China Based on MODIS and Solar-induced Chlorophyll Fluorescence

Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),extracted from the Moderate Resolution Imaging Spectrometer(MODIS),are widely used to monitor phenology by calculating land surface reflectance.However,the applicability of the vegetation index based on‘greenness'to monitor photosynthetic activity is hindered by poor observation conditions(e.g.,ground shadows,snow,and clouds).Recently,satellite measurements of solar-induced chlorophyll fluorescence(SIF)from OCO-2 sensors have shown great potential for studying vegetation phenology.Here,we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China,exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI.The results show that NDVI has obvious SOS advance and EOS lag,and EVI is closer to SIF.The growing season length based on SIF is often the shortest,while it can represent the true phenology of vegetation because it is closely related to photosynthesis.SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices.We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions.The results show that the length of growing season of vegetation in predominantly continuous permafrost(zone I)is longer than in permafrost with isolated taliks(zone II).Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.

Classification of Vegetation in North Tibet Plateau Based on MODIS Time-Series Data

Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.

Relationship Between Land Use Changes and the Production of Dust Sources in Kermanshah Province,Iran

Recognizing land use changes(LUC)and evaluating their relationship with producing dust sources are considered effective to manage the environment.Taking Kermanshah Province,Iran as study area,dusty days from 2008 to 2015 were selected and dust sources were identified applying thermal-infrared dust index(TDI),hybrid single-particle lagrangian integrated trajectory(HYSPLIT),false color composite(FCC)and true color composite(TCC)of MODerate resolution Imaging Spectroradiometer(MODIS)images.Afterwards,the land use change map was produced using Landsat images in 2000 and 2015.Then,the distribution and frequency of the sources in each land-use change class and important dust production areas were specified.Eventually,two non-parametric tests including Chisquare and Kruskal-Wallis were applied to examine the relationship between LUC and dust sources.Results indicated that the distribution of dust sources was not identical in the study area,and the sources were mainly generated in the areas where land-use change had occurred.In fact,different classes of LUC have different contributions to dust production,and the highest contribution refers to the deflation in gentle slope areas and lowlands where the rangeland has been converted into agriculture land.The findings from this study are useful to manage and control dust in the identified sources.

Spatio-temporal Variation of Water Heat Flux Using MODIS Land Surface Temperature Product over Hulun Lake,China During 2001–2018

Heat flux is important for studying interactions between atmosphere and lake.The heat exchange between air-water interfaces is one of the important ways to govern the temperature of the water surface.Heat exchange between the air-water interfaces and the surrounding environment is completed by solar radiation,conduction,and evaporation,and all these processes mainly occur at the air-water interface.Hulun Lake was the biggest lake which is also an important link and an indispensable part of the water cycle in Northeast China.This study mapped surface energy budget to better understand spatial and temporal variations in Hulun Lake in China from 2001 to 2018.Descriptive statistics were computed to build a historical time series of mean monthly heat flux at daytime and nighttime from June to September during 2001–2018.Remote sensing estimation methods we used was suitable for Hulun Lake(R2=0.81).At month scale,shortwave radiation and latent heat flux were decrease from June to September.However,the maximum sensible heat flux appeared in September.Net longwave radiation was the largest in August.The effective heat budget showed that Hulun Lake gained heat in the frost-free season with highest value in June(686.31 W/m2),and then steadily decreased to September(439.76 W/m2).At annual scale,net longwave radiation,sensible heat flux and latent heat flux all show significant growth trend from 2001 to 2018(P<0.01).Wind speed had the well correlation on sensible heat flux and latent heat flux.Water surface temperature showed the highest coefficient in sensitivity analysis.

Crop Calendar Mapping of Bangladesh Rice Paddy Field with ALOS-2 ScanSAR Data

Rice paddy mapping with optical remote sensing is challenging in Bangladesh due to the heterogeneous cropping pattern, fragmented field size and cloud cover during the growing period. The high-resolution Synthetic Aperture Radar (SAR) sensor is the potential alternate to mapping rice area in Bangladesh. The L-band SAR sensor onboard Advanced Land Observing Satellite (ALOS) acquires multi-polarization and multi-temporal images are a very useful tool for rice area mapping. In this study, we used ALOS-2 ScanSAR dual (HH + HV) polarized time series data in the study area. We used orthorectification and slope corrected backscatter (sigma-naught) images and median filtering (3 × 3) window for image processing. The unsupervised classification with the k-means++ algorithm is used for initial clustering (20 categories) of images over the study area. The GPS location of rice paddy field with cropping pattern over study area uses for classifying the different rice-growing season from the k-means clustering data. The result is compared with the moderate resolution imaging spectroradiometer (MODIS) based rice area and national statistical agricultural yearbook statistics. The results show that, based on the MODIS based rice map, the rice fields can be mapped with a conditional Kappa value of 0.68 and at user’s and producer’s accuracies of 86% and 90%, respectively. The large commission error primarily came from confusion between wet season Aus rice and others crop, Aus-Amon and Boro-Aus-Amon cropping pattern because of their similar backscatter amplitudes and temporal similarities in the rice growing season. The relatively high rice mapping accuracy in this study indicates that the ALOS/PALSAR-2 data could provide useful information in rice cropping management in subtropical regions such Bangladesh.

Statistical Estimation of High-Resolution Surface Air Temperature from MODIS over the Yangtze River Delta,China

High-resolution surface air temperature data are critical to regional climate modeling in terms of energy balance,urban climate change,and so on.This study demonstrates the feasibility of using Moderate Resolution Imaging Spectroradiometer（MODIS）land surface temperature（LST）to estimate air temperature at a high resolution over the Yangtze River Delta region,China.It is found that daytime LST is highly correlated with maximum air temperature,and the linear regression coefficients vary with the type of land surface.The air temperature at a resolution of 1 km is estimated from the MODIS LST with linear regression models.The estimated air temperature shows a clear spatial structure of urban heat islands.Spatial patterns of LST and air temperature differences are detected,indicating maximum differences over urban and forest regions during summer.Validations are performed with independent data samples,demonstrating that the mean absolute error of the estimated air temperature is approximately 2.5°C,and the uncertainty is about 3.1°C,if using all valid LST data.The error is reduced by 0.4°C（15%）if using best-quality LST with errors of less than 1 K.The estimated high-resolution air temperature data have great potential to be used in validating high-resolution climate models and other regional applications.

On the Nature of Caspian Clouds

Caspian clouds(CCs)are formed between the southern coast of the Caspian Sea and the Alborz Mountains.The purpose of this study is to identify characteristics of CCs using aerosol,cloud,and meteorological data from ModernEra Retrospective analysis for Research and Applications version 2(MERRA-2),Moderate Resolution Imaging Spectroradiometer(MODIS),and ECMWF Reanalysis version 5(ERA5)during 2000–2020.During this period,we identified and investigated 636 days with CCs.The results indicated that the frequency(%)of these clouds was higher in the summer than in other seasons because synoptic system activity varies between hot and cold periods.The hot season with the beginning of high-pressure subtropical Azores activity and the formation of a stable atmosphere in northern Iran leads to more frequent occurrence of CCs.These clouds are mainly the low-and middle-level clouds in the region,e.g.,stratus and altocumulus.CCs resulted in 13.9%of annual rainfall,and 55.9%and 18.7%of the summer and autumn rainfall,respectively,relative to total rainfall from all cloud types in the study region.In the multivariate regression analysis,CC precipitation exhibited a strong positive relationship with the cloud water path(CWP),cloud optical thickness(COT),and cloud effective radius(CER).A comparison of the mean and standard deviation of aerosol optical thickness(AOT)and aerosol index(AI)for CC and non-CC days did not show a significant difference.Examination of the synoptic patterns showed that the main factors in the formation of CCs are the specific environmental conditions of the region and the orographic lift of stable air masses.The Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model indicated that the source of moisture for the formation of CCs was largely the Caspian Sea.

Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data

The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer(MODIS) data.We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index(EVI) and land surface water index with a central wavelength at 2130 nm(LSWI2130).In two intensive sites in Northeast China,fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3×3 pixel window levels,respectively.The commission and omission errors in both of the intensive sites were approximately less than 20%.Based on the algorithm,annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed.The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination(R2) value of 0.847.It also revealed a sharp decline in 2003,especially in the Sanjiang Plain located in the northeast of Heilongjiang Province,due to the oversupply and price decline of rice in 2002.These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.

Regional fire monitoring and NASA MODIS fire products characterization using global in dry lands of Central Asia

Central Asian dry lands are grass- and desert shrub-dominated ecosystems stretching across Northern Eurasia. This region supports a population of more than 1 00 million which continues to grow at an average rate of 1.5% annually. Dry steppes are the primary grain and cattle growing zone within Central Asia. Degradation of this ecosystem through burning and overgrazing directly impacts economic growth and food supply in the region. Fire is a recurrent disturbance agent in dry lands contributing to soil erosion and air pollution. Here we provide an overview of inter-annual and seasonal fire dynamics in Central Asia obtained from remotely sensed data. We evaluate the accuracy of the Moderate Resolution Imaging Spectroradiometer （MODIS） global fire products within Central Asian dry lands and use these products to characterize fire occurrence between 2001 and 2009. The results show that on average -15 million ha of land burns annually across Central Asia with the majority of the area burned in August and September in grasslands. Fire is used as a common crop residue management practice across the region. Nearly 89% of all burning occurs in Kazakhstan, where 5% and 3% of croplands and grasslands, respec- tively, are burned annually.

Estimating rice paddy areas in China using multi-temporal cloud-free normalized difference vegetation index (NDVI) imagery based on change detection

The spatial pattern of rice paddies is an essential parameter used for studies of greenhouse gas emissions,agricultural resource management,and environmental monitoring.On large spatial scales,previous studies have usually mapped rice paddies using a single vegetation index product based on a traditional classification method,or a combined analysis of various vegetation and water indices derived from the moderate resolution imaging spectroradiometer(MODIS)satellite data.However,different indices increase the computational cost and constrain the satellite data sources,and traditional classification methods(e.g.,maximum likelihood classification)may be time-consuming and difficult to carry out over a large area like China.In this study,we designed an auto-thresholding and single vegetation index(normalized difference vegetation index(NDVI))-based procedure to estimate the spatial distribution of rice paddies in China.The MOD09Q1 product,which was available at MODIS’s highest spatial resolution(250 m),was taken as the input source.An auto-threshold function was also introduced into the change detection process to distinguish rice paddies from other croplands.Our MODIS-derived maps were validated with ground surveys and then compared with China national statistical data of rice paddy areas.The results indicated that the best classification result was achieved for plain regions,and that the accuracy declined for hilly regions,where the complex landscape could lead to an underestimation of the rice paddy area.A comparison between the modeled results and other analyses using 500-m MODIS data suggests that rice paddies may be identified routinely using a single vegetation index with finer resolution on large spatial scales.

Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products

With the objective of reducing the large uncertainties in the estimations of emissions from crop residue open burning, an improved method for establishing emission inventories of crop residue open burning at a high spatial resolution of 0.25°× 0.25° and a temporal resolution of1 month was established based on the moderate resolution imaging spectroradiometer（MODIS） Thermal Anomalies/Fire Daily Level3 Global Product（MOD/MYD14A1）. Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to improve the accuracy of related activity data. Locally observed emission factors were used to calculate the primary pollutant emissions. MODIS satellite data were modified by combining them with county-level agricultural statistical data, which reduced the influence of missing fire counts caused by their small size and cloud cover. The annual emissions of CO2, CO, CH4,nonmethane volatile organic compounds（NMVOCs）, N2O, NOx, NH3, SO2, fine particles（PM2.5）,organic carbon（OC）, and black carbon（BC） were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43,1.09, 0.34, and 0.06 Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June（37%）.Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ±126% for N2O to a high of within ± 169% for NH3.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.