Sub-pixel analysis to enhance the accuracy of evapotranspiration determined using MODIS images

A study was carried out to estimate the actual evapotranspiration(ET)over a 1074 km2 of the humid area of Perak State(Malaysia),where water and evaporation cycle deeply influences the climate,natural resources and human living aspects.Images from both Terra and Aqua platforms of the Moderate Resolution Imaging Spectroradiometer(MODIS)sensor were used for ET estimation by employing the Surface Energy Balance Algorithm for Land(SEBAL)model.As a part of the accuracy assessment process,in-situ measurements on soil temperature and reference ET(ET0)were recorded at the time of satellite overpass.In order to enhance the accuracy of the generated ET maps,MODIS images were subjected to sub-pixel analysis by assigning weights for different land surface cover(urban,agriculture and multi-surface areas)reflections.The weighting process was achieved by integrating ET from pure pixels with the respective site-specific ET0 of each land cover.The enhanced SEBAL model estimated ET exhibited a good correlation with the in-situ measured Penman-Montieth ET0,with R2 values for the Aqua and the Terra platforms of 0.67 and 0.73,respectively.However,the correlation of the non-enhanced ET maps resulted in R2 values of 0.61 and 0.68 for the Aqua and the Terra platforms,respectively.Hence,the results of this study revealed the feasibility of employing the sub-pixel analysis method for an accurate estimation of ET over large areas.

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.

Characteristics and generations of internal wave in the Sulu Sea inferred from optical satellite images

Characteristics and generation of internal waves(IWs)in the Sulu Sea are studied using Moderate-Resolution Imaging Spectroradiometer(MODIS)and Visible Infrared Imaging Radiometer Suite(VIIRS)images taken from October 2016 to September 2019.Satellite observations show that IWs in the Sulu Sea mainly located in the shallower western areas with occasional observations in the deeper eastern regions.The dominant length of wave crest(LWC)of IWs is between 50 and 150 km with the largest LWC reaching over 300 km.The analysis of temporal distributions of IWs shows that March has the most IWs and July has the least.Further analysis shows that the seasonal variation is mainly due to the cloud contamination of optical satellite images.New generation sites of IWs are analyzed using satellite images.Six possible generation sites for IWs in the western Sulu Sea and one generation site for IWs in the eastern Sulu Sea are found using the ray-tracing method.Multi IW sources in the same strait are found,which may be due to the seawater fl ow over the strait in diff erent directions.The analysis shows that IWs with long wave crest in the Sulu Sea is a combined eff ort of all straits between small islands in the Sulu Archipelago.Remote generated IWs with long wave crest in the eastern Sulu Sea are studied,which are generated at the straits around(121.5°E,6°N)by the nonlinear evolution of internal tide originated from the Sulu Archipelago.

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.

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.

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.

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.

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.

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.

Bathymetrical influences on spatial and temporal characteristics of chlorophyll-a concentrations in the Southern Ocean from 2002 to 2012(October to March)using MODIS

Phytoplankton blooms,particularly in the Southern Ocean,can have significant impact on global biogeochemistry cycling.To investigate the accuracy of chlorophyll-a distribution,and to better understand the spatial and temporal dynamics of phytoplankton biomass,we examine chlorophyll-a estimates(October-March from 2002 to 2012)derived from Moderate Resolution Imaging Spectrometer(MODIS)data following the ocean chlorophyll-a 3 model(OC3M)algorithm.Noticeable seasonality occurs in the temporal distribution of chlorophyll-a concentrations,which shows the highest value in December and January and an increasing tendency during the 2002-2012 period.The spatial distribution of chlorophyll-a varies greatly with latitude,as higher latitudes experience more phytoplankton blooms(chlorophyll-a concentration larger than 1 mg/m3)and marginal seas(Ross Sea and Amundsen Sea)show different bloom anomalies caused by two dominant algae species.Areas at higher latitudes and shallow water(<500 m)experience the shorter icefree periods with greater seasonality.A noticeable bathymetry gradient exists at 2500-m isobaths,while water at the 500-2500-m depth experiences quite long ice-free periods with a stable water environment.Blooms generally occur near topographic features where currents have strong interactions when the water depth is more than 2500 m.Based on these findings,we can classify the Southern Ocean into two bloom subregions,0-500 m as an enhanced bloom zone(EBZ),and 500-2500 m as a moderate bloom zone(MBZ).The EBZ has a quite high-bloom probability of about 30%,while the MBZ has only 10%.

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.

Development of a new index for automated mapping of ratoon rice areas using time-series normalized difference vegetation index imagery

Over recent decades, the global demand for food has continued to grow, owing to population growth and the loss of arable land. Rice ratooning offers new opportunities for increasing rice production and has received renewed interest because of the minimal additional labor input required for its adoption. Regular, regional-scale monitoring of the spatial patterns of both traditional and ratoon rice cropping systems provides essential information for agricultural resource management and food security studies. However, the similar phenological characteristics of traditional double rice and ratoon rice cropping systems make it challenging to accurately classify these cropping practices based on satellite observations alone. In this study, we first proposed an improved phenology-based rice cropping area detection algorithm using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) imagery. A new index, ratoon rice index, was then developed to automatically delineate ratoon rice cropping areas with the aid of a base map of rice in Hubei Province, China. The accuracy assessment using ground truth data showed that our approach could map both traditional and ratoon rice cropping areas with high user accuracy (91.25% and 91.43%, respectively). The MODIS-retrieved rice cropping areas were validated using annual agricultural census data, and coefficient of determination (R2) values of 0.60 and 0.41 were recorded for traditional and ratoon rice cropping systems, respectively. The total area of ratoon rice was estimated to be 1 283.6 km2, 5.0% of the total rice cropping area, in Hubei Province in 2016. These demonstrated the feasibility of extracting the spatial patterns of both traditional and ratoon rice cropping systems solely from time-series NDVI and field survey data and strides made in facilitating the timely and routine monitoring of traditional and ratoon rice distribution at subnational level. Given sufficient historical satellite and phenology records, the proposed algorithm had the potential to enhance rice cropping area mapping efforts across a broad temporal scale (e.g., from the 1980s to the present).

The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

This paper investigates the contribution of multi-temporal enhanced vegetation index(EVI)data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer(MODIS)imagery.In object-oriented classification,similar pixels are firstly grouped together and then classified;the produced result does not suffer the speckled appearance and closer to human vision.EVI data are from the MODIS sensor aboard Terra spacecraft.69 EVI data(scenes)were collected during the period of three years(2001
2003)in a mountainous vegetated area.These data sets were used to study the phenology of the land cover types.Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification.Two experiments were carried out:one was only with single date MODIS multispectral data,and the other one including also the 69 EVI images.Eight classes were distinguished:temperate forest,tropical dry forest,grassland,irrigated agriculture,rain-fed agriculture,orchards,lava flows and human settlement.The two classifications were evaluated with independent verification data,and the results showed that with multitemporal EVI data,the classification accuracy was improved 5.2%.Evaluated by McNemar’s test,this improved was significant,with significance level p=0.01.

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.

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.