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.

Multiple Cropping Intensity in China Derived from Agro-meteorological Observations and MODIS Data

Double-and triple-cropping in a year have played a very important role in meeting the rising need for food in China.However,the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality.Understanding and mapping cropping intensity in China′s agricultural systems are therefore necessary to better estimate carbon,nitrogen and water fluxes within agro-ecosystems on the national scale.In this study,we investigated the spatial pattern of crop calendar and multiple cropping rotations in China using phenological records from 394 agro-meteorological stations(AMSs)across China.The results from the analysis of in situ field observations were used to develop a new algorithm that identifies the spatial distribution of multiple cropping in China from moderate resolution imaging spectroradiometer(MODIS)time series data with a 500 m spatial resolution and an 8-day temporal resolution.According to the MODIS-derived multiple cropping distribution in 2002,the proportion of cropland cultivated with multiple crops reached 34%in China.Double-cropping accounted for approximately 94.6%and triple-cropping for 5.4%.The results demonstrat that MODIS EVI(Enhanced Vegetation Index)time series data have the capability and potential to delineate the dynamics of double-and triple-cropping practices.The resultant multiple cropping map could be used to evaluate the impacts of agricultural intensification on biogeochemical cycles.

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.

Crop Classification Using MODIS NDVI Data Denoised by Wavelet: A Case Study in Hebei Plain, China

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.

Evapotranspiration Estimation Based on MODIS Products and Surface Energy Balance Algorithms for Land(SEBAL) Model in Sanjiang Plain,Northeast China

In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.

Snow Cover Variation and Streamflow Simulation in a Snow-fed River Basin of the Northwest Himalaya

Snowmelt is an important component of any snow-fed river system.The Jhelum River is one such transnational mountain river flowing through India and Pakistan.The basin is minimally glacierized and its discharge is largely governed by seasonal snow cover and snowmelt.Therefore,accurate estimation of seasonal snow cover dynamics and snowmeltinduced runoff is important for sustainable water resource management in the region.The present study looks into spatio-temporal variations of snow cover for past decade and stream flow simulation in the Jhelum River basin.Snow cover extent(SCE) was estimated using MODIS(Moderate Resolution Imaging Spectrometer) sensor imageries.Normalized Difference Snow Index(NDSI) algorithm was used to generate multi-temporal time series snow cover maps.The results indicate large variation in snow cover distribution pattern and decreasing trend in different sub-basins of the Jhelum River.The relationship between SCE-temperature,SCE-discharge and discharge-precipitation was analyzed for different seasons and shows strong correlation.For streamflow simulation of the entire Jhelum basin Snow melt Runoff Model(SRM) used.A good correlation was observed between simulated stream flow and in-situ discharge.The monthly discharge contribution from different sub-basins to the total discharge of the Jhelum River was estimated using a modified version of runoff model based on temperature-index approach developed for small watersheds.Stream power - an indicator of the erosive capability of streams was also calculated for different sub-basins.

A Thin Cloud Removal Method from Remote Sensing Image for Water Body Identification

In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.

The Seasonal Variations of Aerosols over East Asia as Jointly Inferred from MODIS and OMI

Data on aerosol optical thickness(AOT) and single scattering albedo(SSA) derived from Moderate Resolution Imaging Spectrometer(MODIS) and Ozone Monitoring Instrument(OMI) measurements,respectively,are used jointly to examine the seasonal variations of aerosols over East Asia.The seasonal signals of the total AOT are well defined and nearly similar over the land and over the ocean.These findings indicate a natural cycle of aerosols that originate primarily from natural emissions. In contrast,the small-sized aerosols represented by the fine-mode AOT,which are primarily generated over the land by human activities,do not have evident seasonalscale fluctuations.A persistent maximum of aerosol loadings centered over the Sichuan basin is associated with considerable amounts of fine-mode aerosols throughout the year.Most regions exhibit a general spring maximum. During the summer,however,the aerosol loadings are the most marked over north central China.This occurrence may result from anthropogenic fine particles,such as sulfate and nitrate.Four typical regions were selected to perform a covariation analysis of the monthly gridded AOT and SSA.Over southwestern and southeastern China,if the aerosol loadings are small to moderate they are composed primarily of the highly absorptive aerosols. However,more substantial aerosol loadings probably represent less-absorptive aerosols.The opposite covariation pattern occurring over the coastal-adjacent oceans suggests that the polluted oceanic atmosphere is closely correlated with the windward terrestrial aerosols.North central China is strongly affected by dust aerosols that show moderate absorption.This finding may explain the lower variability in the SSA that accompanies increasing aerosol loadings in this region.

A MODIS Time Series Data Based Algorithm for Mapping Forest Fire Burned Area

Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and cli- mate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter （MODIS） time series data in Heilongjiang Province, China. The algorithm is divided into two steps： Firstly, the ＇core＇ pixels were extracted to represent the most possible burned pixels based on the comparison of the tem- poral change of Global Environmental Monitoring Index （GEMI）, Burned Area Index （BAI） and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ＇core＇ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the hiehest accuracy of 96.61%.

A Simple Method to Extract Tropical Monsoon Forests Using NDVI Based on MODIS Data:A Case Study in South Asia and Peninsula Southeast Asia

Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation lndex （NDVI） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps （MODMF） from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products （MCD 12Q 1）. The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMv which can be identified for 7 to 9 times between 200l and 2009 account for 53.1%, while only 7.9% ofMCD12QI pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 （GLC2000）, MCDI2Q1 and University of Maryland （UMD） products are compared with the statistical dataset at national level, which reveals that MODMv has the highest R^2 of 0.95 and the lowest RMSE of 14 014 km^2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.

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.

Validation of Global Evapotranspiration Product(MOD16) Using Flux Tower Data from Panjin Coastal Wetland,Northeast China

Recent advances in remote sensing technology and methods have resulted in the development of an evapotranspiration(ET) product from the Moderate Resolution Imaging Spectrometer(MOD16). The accuracy of this product however has not been tested for coastal wetland ecosystems. The objective of this study therefore is to validate the MOD16 ET product using data from one eddy covariance flux tower situated in the Panjin coastal wetland ecosystem within the Liaohe River Delta, Northeast China. Cumulative ET data over an eight-day period in 2005 from the flux tower was calculated to coincide with the MOD16 products across the same period. Results showed that data from the flux tower were inconsistent with that gained form the MOD16 ET. In general, results from Panjin showed that there was an underestimation of MOD16 ET in the spring and fall, with Biases of -2.27 and -3.53 mm/8 d, respectively(–40.58% and -49.13% of the observed mean). Results for Bias during the summer had a range of 1.77 mm/8 d(7.82% of the observed mean), indicating an overestimation of MOD16 ET. According to the RMSE, summer(6.14 mm/8 d) achieved the lowest value, indicating low accuracy of the MOD16 ET product. However, RMSE(2.09 mm/8 d) in spring was the same as that in the fall. Relationship between ET and its relevant meteorological parameters were analyzed. Results indicated a very good relationship between surface air temperature and ET. Meanwhile a significant relationship between wind speed and ET also existed. The inconsistent comparison of MOD16 and flux tower-based ET are mainly attributed to the parameterization of the Penman-Monteith model, flux tower measurement errors, and flux tower footprint vs. MODIS pixels.

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.

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.