Vegetation NPP Distribution Based on MODIS Data and CASA Model——A Case Study of Northern Hebei Province

Net Primary Productivity （NPP） is one of the important biophysical variables of vegetation activity, and it plays an important role in studying global carbon cycle, carbon source and sink of ecosystem, and spatial and temporal distribution of CO2. Remote sensing can provide broad view quickly, timely and multi-temporally, which makes it an attractive and powerful tool for studying ecosystem primary productivity, at scales ranging from local to global. This paper aims to use Moderate Resolution Imaging Spectroradiometer （MODIS） data to estimate and analyze spatial and temporal distribution of NPP of the northern Hebei Province in 2001 based on Carnegie-Ames-Stanford Approach （CASA） model. The spatial distribution of Absorbed Photosynthetically Active Radiation （APAR） of vegetation and light use efficiency in three geographical subregions, that is, Bashang Plateau Region, Basin Region in the northwestern Hebei Province and Yanshan Mountainous Region in the Northern Hebei Province were analyzed, and total NPP spatial distribution of the study area in 2001 was discussed. Based on 16-day MODIS Fraction of Photosynthetically Active Radiation absorbed by vegetation （FPAR） product, 16-day composite NPP dynamics were calculated using CASA model; the seasonal dynamics of vegetation NPP in three subreglons were also analyzed. Result reveals that the total NPP of the study area in 2001 was 25.1877 × 10^6gC/（m^2.a）, and NPP in 2001 ranged from 2 to 608gC/（m^2-a）, with an average of 337.516gC/（m^2.a）. NPP of the study area in 2001 accumulated mainly from May to September （DOY 129-272）, high NIP values appeared from June to August （DOY 177-204）, and the maximum NPP appeared from late July to mid-August （DOY 209-224）.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Review of large scale crop remote sensing monitoring based on MODIS data

China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.

Extraction of Desertification Information in Hulun Buir Based on MODIS Image Data

[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.

Construction of lake bathymetry from MODIS satellite data and GIS from 2003 to 2011

In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang(Yangtze) River following the construction of Three Gorges Dam. The topography of the lake bottom has changed rapidly because of the intense exchange of water and sediment between the lake and the Changjiang River. However, time series information on lake-bottom topographic change is lacking. In this study, we introduced a method that combines remote sensing data and in situ water level data to extract a record of Dongting Lake bottom topography from 2003 to 2011. Multi-temporal lake land/water boundaries were extracted from MODIS images using the linear spectral mixture model method. The elevation of water/land boundary points were calculated using water level data and spatial interpolation techniques. Digital elevation models of Dongting Lake bottom topography in different periods were then constructed with the multiple heighted waterlines. The mean root-mean-square error of the linear spectral mixture model was 0.036, and the mean predicted error for elevation interpolation was-0.19 m. Compared with fi eld measurement data and sediment load data, the method has proven to be most applicable. The results show that the topography of the bottom of Dongting Lake has exhibited uneven erosion and deposition in terms of time and space over the last nine years. Moreover, lake-bottom topography has undergone a slight erosion trend within this period, with 58.2% and 41.8% of the lake-bottom area being eroded and deposited, respectively.

MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS: METHODS AND IDEAL TESTS

Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var （Global and Regional Assimilation and Prediction Enhanced System）, cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.

MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS Ⅱ: IMPACTS ON RAINSTORM FORECASTING

Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels. It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of the Global and Regional Assimilation and Prediction Enhanced System 3-dimensional variance(GRAPES-3DVar),cloud liquid water content, ice-water content and cloud cover are employed as governing variables in the assimilation system.This scheme can improve the simulation of infrared transmittance by a fast radiative transfer model for TOVS (RTTOV)and adjust the atmospheric and cloud parameters based on infrared radiance observations.In this paper,we investigate a heavy rainfall over Guangdong province on May 26,2007,which is right after the onset of a South China Sea monsoon.In this case,channels of the Moderate Resolution Imaging Spectroradiometer(MODIS)for observing water vapor(Channel 27)and cloud top altitude(Channel 36)are selected for the assimilation.The process of heavy rainfall is simulated by the Weather Research and Forecasting(WRF)model.Our results show that the assimilated MODIS data can improve the distribution of water vapor and temperature in the first guess field and indirectly adjust the upper-level wind field.The tendency of adjustment agrees well with the satellite observations.The assimilation scheme has positive impacts on the short-range forecasting of rainstorm.

Using MAN and Coastal AERONET Measurements to Assess the Suitability of MODIS C6.1 Aerosol Optical Depth for Monitoring Changes from Increased Arctic Shipping

Collocated data of the moderate resolution imaging spectroradiometer (MO-DIS) Collection 6.1 aerosol optical depths (AOD) at 3 km × 3 km north of 59.9°N over ocean were assessed at 550 nm by aerosol robotic network (AERONET) data from coastal sites and marine aerosol network (MAN) data from vessels during June to October 2006 to 2018. Typically, MODIS AOD was higher at low and lower at high values than the AERONET AOD. Discrepancies were largest for sites where the Earth’s surface around the site is very heterogeneous (Canadian Archipelago, coast of Greenland). Due to the higher likelihood for sea-ice, MAN and MODIS AOD differed stronger west of Greenland and over the Beaufort Sea than at location in the Greenland and Norwegian Seas and Atlantic. MODIS AOD well captured the inter-seasonal variability found in the AERONET AOD data (R = 0.933). At all sites, MO-DIS and AERONET AOD agreement improved as time progressed in the shipping season, hinting at errors in sea-ice vs. open water classification. Overall 75.3% of the MODIS AOD data fell within the limits of the error envelops of the AERONET/MAN AOD data with MAN ranging between 87.5% and 100%. Changes in both MODIS and AERONET mean AOD between two periods of same length (2006-2011, 2013-2018) were explainable by changes in emissions for all sites.

Changes of net primary productivity in China during recent 11 years detected using an ecological model driven by MODIS data

Net primary productivity （NPP） is an important component of the terrestrial carbon cycle. Accurately mapping the spatial-temporal variations of NPP in China is crucial for global carbon cycling study. In this study the process-based Boreal Ecosystem Productivity Simulator （BEPS） was employed to study the changes of NPP in China＇s ecosystems for the period from 2000 to 2010. The BEPS model was first validated using gross primary productivity （GPP） measured at typical flux sites and forest NPP measured at different regions. Then it was driven with leaf area index （LAI） inversed from the Moderate Resolution Imaging Spectroradiometer （MODIS） reflec- tance and land cover products and meteorological data interpolated from observations at753 national basic meteorological stations to simulate NPP at daily time steps and a spatial resolution of 500m from January 1, 2000 to December 31, 2010. Validations show that BEPS is able to capture the seasonal variations of tower-based GPP and the spatial variability of forest NPP in different regions of China. Estimated national total of annual NPP varied from 2.63 to 2.84Pg C.yr-1, averaging 2.74Pg C.yr-1 during the study period. Simulated terrestrial NPP shows spatial patterns decreasing from the east to the west and from the south to the north, in association with land cover types and climate. South-west China makes the largest contribution to the national total of NPP while NPP in the North-west account for only 3.97% of the national total. During the recent 11 years, the temporal changes of NPP were heterogamous. NPP increased in 63.8% of China＇s landmass, mainly in areas north of the Yangtze River and decreased in most areas of southern China, owing to the low temperature freezing in early 2008 and the severe drought in late 2009.

Observed Impacts of Climate Variability on LULC in the Mesopotamia Region

Remote sensing analysis techniques have been investigated extensively,represented by a critical vision,and are used to advance our understanding of the impacts of climate change and variability on the environment.This study aims to find a means of analysis that relies on remote sensing techniques to demonstrate the effects of observed climate variability on Land Use and Land Cover(LULC)of the Mesopotamia region,defined as a historical region located in the Middle East.This study employed the combined analysis of the Normalized Difference Vegetation Index(NDVI),Land Surface Temperature(LST),and two statistical analysis methods(Pearson Correlation Analysis,r;Coefficient of Determination,R^(2)),which were applied using the Moderate Resolution Imaging Spectroradiometer data and observed surface meteorological data from 2000 to 2018.The resulting NDVI images show five LULC classes with NDVI values varying between−0.3 and 0.9.Furthermore,changes in the classified LULC area were compared statistically to those in NDVI values,where a positive relationship was found.Also,when the LST values and temperature are more extreme,the NDVI values were found to be smaller,suggesting a decrease in the density of vegetation cover.A negative correlation was found through Pearson correlation analysis(r=∼−0.64),indicating a direct effect of increased temperatures on LULC.Indeed,this negative relationship between NDVI and LST was proven using R^(2) values,where a two-dimensional scatter plot analysis showed that R^(2) ranges from 0.54 to 0.9.Ultimately,the results obtained from this study reveal changes that may have many prominent effects in the field of LULC classification,accelerating the implications of climate change and variability factors.

STUDY ON CLOUD CLASSIFICATIONS BY USING AVHRR,GMS-5 AND TERRA/MODIS SATELLITE DATA

This paper presents the automated pixel-scale neural network classification methods being developed at National Satellite Meteorological Center(NSMC)of China to classify clouds by using NOAA/AVHRR and GMS-5 satellite imageries.By using Terra satellite MODIS imageries,an automated pixel-scale threshold technique has been developed to detect and classify clouds.The study focuses on applications of these cloud classification techniques to the Huaihe River and the Changjiang(Yangtze)River drainage basin.The different types of clouds show more clearly on this cloud classification image than single band image.The results of the cloud classifications are the basis of studying cloud amount,cloud top height and cloud top pressure.Cloud mask methods are widely used in SST,LST,and TPW retrieval schemes.Some case studies about cloud mask and cloud classification in satellite imageries,which are related with the study of Global Energy and Water Cycle Experiment(GEWEX)in the Huaihe River and the Changjiang River drainage basin are illustrated.

SOC storage and potential of grasslands from 2000 to 2012 in central and eastern Inner Mongolia, China

Grassland ecosystem is an important component of the terrestrial carbon cycle system. Clear comprehension of soil organic carbon（SOC） storage and potential of grasslands is very important for the effective management of grassland ecosystems. Grasslands in Inner Mongolia have undergone evident impacts from human activities and natural factors in recent decades. To explore the changes of carbon sequestration capacity of grasslands from 2000 to 2012, we carried out studies on the estimation of SOC storage and potential of grasslands in central and eastern Inner Mongolia, China based on field investigations and MODIS image data. First, we calculated vegetation cover using the dimidiate pixel model based on MODIS-EVI images. Following field investigations of aboveground biomass and plant height, we used a grassland quality evaluation model to get the grassland evaluation index, which is typically used to represent grassland quality. Second, a correlation regression model was established between grassland evaluation index and SOC density. Finally, by this regression model, we calculated the SOC storage and potential of the studied grasslands. Results indicated that SOC storage increased with fluctuations in the study area, and the annual changes varied among different sub-regions. The SOC storage of grasslands in 2012 increased by 0.51×1012 kg C compared to that in 2000. The average carbon sequestration rate was 0.04×1012 kg C/a. The slope of the values of SOC storage showed that SOC storage exhibited an overall increase since 2000, particularly for the grasslands of Hulun Buir city and Xilin Gol League, where the typical grassland type was mainly distributed. Taking the SOC storage under the best grassland quality between 2000 and 2012 as a reference, this study predicted that the SOC potential of grasslands in central and eastern Inner Mongolia in 2012 is 1.38×1012 kg C. This study will contribute to researches on related methods and fundamental database, as well as provide a reference for the protection of grassland ecosystems and the formulation of local policies on sustainable grassland development.

A global terrestrial ecosystem respiration dataset(2001-2010)estimated with MODIS land surface temperature and vegetation indices

This paper describes how a validated semi-empirical,but physiologically based,remote sensing model-Ensemble_all-was upscaled using MODIS land surface temperature data(MOD11C2),enhanced vegetation indices(MOD13C1)and land-cover data(MCD12C1)to produce a global terrestrial ecosystem respiration data set(Reco)for January 2001-December 2010.The temporal resolution of this data set is 1 month,the spatial resolution is 0.05°,and the range is from 55°S to 65°N and 180°W to 180°E(crop and natural vegetation mosaic is not included).After crossvalidating our data set using in-situ observations as well as Reco outputs from an empirical variable_Q10 model,a LPJ_S1 process model and a machine learning method model,we found that our data set performed well in detecting both temporal and spatial patterns in Reco’s simulation in most ecosystems across the world.This data set can be found at http://www.dx.doi.org/10.11922/sciencedb.934.

Monitoring Heat Waves and Their Impacts on Summer Crop Development in Southern Brazil

Periods in the soybean summer cycle that are sensitive to the occurrence of high temperatures were studied. An analysis was performed on the variability of soybean yields associated with crop canopy temperatures during key development periods. A land surface temperature (LST) data series from MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua satellite was processed between 2003 and 2012 that covered the entire state of Rio Grande do Sul, in Brazil. Enhanced vegetation index (EVI) data from MODIS on the Terra satellite were used to monitor the LST during different phenological stages. Spatially interpolated maps of soybean yield distributions were generated using data obtained from Instituto Brasileiro de Geografia e Estatística (IBGE) at state and municipality levels. The results indicate that canopy-LST occurrence in mid-February, during the grain filling, is most correlated to yield reduction (R2 = 0.82 and RMSD = 14.4%). At the state level, the average yield is 2003 kg·ha-1 with a standard deviation of 308 kg·ha-1. The overall average of the canopy-LST is 305.0 K (31.8°C) with a standard deviation of 1.9 K. The slope of the downward linear relationship between canopy-LST and yield was -28.7%. These results indicate that monitoring heat wave events can provide important information for characterising agriculture vulnerability.

A simple data assimilation method for improving the MODIS LAI time-series data products based on the object analysis and gradient inverse weighted filter

A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.

Snow Information Abstraction Based on Remote Sensing Data： Taking the North of Xinjiang for Example

This paper proposes an applicable approach for snow information abstraction in northern Xinjiang Basin using MODIS data. Linear spectral mixture analysis （LSMA） was used to calculate snow cover fractions （SF） within a pixel, which was used to establish a regression function with NDSI. In addition, 80 snow depths samples were collected in the study region. The correlation between image spectra reflectance and snow depth as well as the comparison between measured snow spectra and image spectra was analyzed. An algorithm was developed for snow depth inversion on the basis of the correlation between snow depth and snow spectra in the region. The results indicated that the model of SF had a high accuracy with the mean absolute error 0.06 tested by 26 true measured values and the validation for snow depth model using another dataset with 50 sampling sites showed an RMSE of 1.63. Our study showed that MODIS data provide an alternative method for snow information abstraction through development of algorithms suitable for local application.

Jet-like features of Jiulongjiang River plume discharging into the west Taiwan Strait

In-situ data from the summer cruise of 2010 in the west Taiwan Strait are used to study the spatial distribution of the Jiulongjiang River plume （JRP）. The results show that in the 2 m layer, the JRP debouches into the west Taiwan Strait in the form of jets, with one branch through the Xiamen Bay （Xiamen JR_P） and another through the channel between Jinmen and Weitou （JinWei JRP）. Driven by the summer southwesterly monsoon, the upwelling-related Dongshan low temperature and high salinity water flows northeastward in the form of a jet as well. To a certain degree, the Dongshan low temperature and high salinity jet restricts the Xiamen JRP from spreading further offshore and drags the JinWei JRP northeastward at the same time. Meanwhile, a terrestrial dissolved organic matter （DOM） distribution model on the basis of molecular collision theory in thermodynamics and statistical physics is applied to analyze the Moderate Resolution Imaging Spectroradiometer （MODIS） turbidity data. The correlation coefficient of the theoretical model to the MODIS turbidity data reaches 0.96 （significant at a 95% level of confidence）. The result clarifies the dynamic mechanism for the turbidity distribution characteristics. It is the salinity in macro-scale that plays a decisive role in the turbidity variability in the coastal water. This suggests that the satellite-derived turbidity data can be used as an indicator to show the spreading patterns of the JRP. Based on the turbidity data from 2003 to 2011, we conclude that there are four main spreading patterns of the JRP.

Numerical Study of the Impacts of Urban Expansion on Meiyu Precipitation over Eastern China

The Yangtze River Delta（YRD） has experienced significant urban expansion in recent years, while the Meiyu belt of China has demonstrated a decadal northward shifting trend. Thus, it is of interest to assess how urban expansion affects Meiyu precipitation and hopefully to reveal the underlying physical mechanisms involved. In this study, the urban extents over the YRD in 2001 and 2010 are derived based on land use/land cover（LULC） category data and nighttime light image data. Two parallel groups of10-summer（2001-2010） numerical simulations are carried out with the urban extents over the YRD in2001 and 2010, respectively. The results show that the urban expansion in the YRD tends to result in increased（decreased） Meiyu precipitation over the Huaihe River（Yangtze River） basin with intensities of0.2-1.2 mm day-1. Further analysis indicates that the spatiotemporal pattern of the Meiyu precipitation change induced by the urban expansion resembles the third empirical orthogonal function（EOF） mode of the observed Meiyu precipitation. Analyses of the possible underlying physical mechanisms reveal that urban expansion in the YRD leads to changes in the surface energy balance and warming（cooling） of tropospheric（stratospheric） air temperature over eastern China. Anomalous upward（downward） motion and moisture convergence（divergence） over the Huaihe River（Yangtze River） basin occur, corresponding to the increases（decreases） of the Meiyu precipitation over the Huaihe River（Yangtze River） basin.