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Correlation analysis has been widely used to validate the accuracy of synthetic medium-resolution data generated by spatial and temporal fusion methods. However, as the temporal resolution of Landsat data is 16 days, this method only can be used for the validation of multi-temporal Landsat data. This means that the fusion accuracy of most images in a synthetic daily time series have not been validated. Furthermore, the fusion accuracy of each image in a synthetic Landsat time series is different, because there is a negative correlation with the time interval length between the fusion date and the base image date. Therefore, there is a need for temporal validation of synthetic daily Landsat time series data. We propose a suitable validation method in this paper. The improved spatial and temporal data fusion approach (ISTDFA) was applied to generate synthetic daily Landsat Normalized Difference Vegetation Index (NDVI) time series, which were then validated for both spatial and temporal dimensions using actual MODIS NDVI time series. For temporal validation, the correlation coefficients ( R ) between the actual and synthetic 500 m NDVI time series were calculated by pixel-by-pixel to generate imagery with an R value for each pixel. For spatial validation, R between MODIS NDVI imagery and synthetic Landsat NDVI imagery was calculated day by day to generate an R time series. This method was tested and validated in two locations (Bole and Luntai) in Xinjiang Province, China. The results show that, for temporal validation, the R values of 86.08% pixels in Bole and 94.71% pixels in Luntai are higher than 0.9, and in spatial validation, R values are higher than 0.8 on most days. Synthetic daily Landsat NDVI data was used to monitor the phenology of vegetation at a spatial resolution of 30 m successfully, while the MODIS product is limited to 500 m.

In complex heterogeneous areas, it is difficult to map crops with high accuracy using only high spatial resolution or only high spectral resolution remote sensing data. Because the spectral resolution of high spatial resolution data is too low, the spectral differentiations of different vegetation types are very small in high spatial resolution data. It is hard to distinguish between different vegetation types using high spatial resolution data. For high spectral resolution remote sensing data, it is hard to exclude linear objects like roads, bridges and drains from crops due to the low spatial resolution of these data. To address this problem, a novel object-based fine crop mapping method by combining high spatial and high spectral resolution remote sensing data for heterogeneous areas was proposed and validated in Suzhou city, Jiangsu province, China. First, pure crop polygons were derived from a 0.5 m aerial data. Due to the high spatial resolution, non-cultivated land could be easily isolated from arable land. Then, a Hyperion data was used to classify crops for each of the pure crop polygons. The results show that this method can map crops in complex heterogeneous areas with an overall accuracy higher than 95%, which is much higher than the accuracy of maps classified using only high spatial resolution data or only high spectral resolution data, which have an overall accuracy of 58.78% and 77.54%, respectively.

Two key weaknesses of STDFA including sensor difference and spatial variability were adjusted.Three wildly used spatial and temporal fusion methods were compared.The correlation coefficient r had a negative exponential relationship with ratio of land cover change pixels.The accuracy of ISTDFA method had a logarithmic relationship with the size of applied area. Because of low temporal resolution and cloud influence, many remote-sensing applications lack high spatial resolution remote-sensing data. To address this problem, this study introduced an improved spatial and temporal data fusion approach (ISTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the weaknesses of the spatial and temporal data fusion approach (STDFA) method, including the sensor difference and spatial variability. A weighted linear mixed model was used to adjust the spatial variability of surface reflectance. A linear-regression method was used to remove the influence of differences in sensor systems. This method was tested and validated in three study areas located in Xinjiang and Anhui province, China. The other two methods, the STDFA and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), were also applied and compared in those three study areas. The results showed that the ISTDFA algorithm can generate daily synthetic Landsat imagery accurately, with correlation coefficient r equal to 0.9857 and root mean square error (RMSE) equal to 0.0195, which is superior to the STDFA method. The ISTDFA method had higher accuracy than ESTARFM in areas greater than 200?× 200 MODIS pixels while the ESTARFM method had higher accuracy than the ISTDFA method in small areas. The correlation coefficient r had a negative power relation with ratio of land-cover change pixels. A land-cover change of 20.25% pixels can lead to a reduced correlation coefficient r of 0.295 in the blue band. The accuracy of the ISTDFA method indicated a logarithmic relationship with the size of the applied area, so it is recommended for use in large-scale areas.

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.

The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop height for the identification of crop types (mainly corn, cotton, and sorghum). The crop height was extracted by subtracting the DSM derived during the crop growing season from that derived after the crops were harvested. Then, the crops were identified from four-band aerial imagery (blue, green, red, and near-infrared) and the crop height, using an object-based classification method and a maximum likelihood method. The results showed that the extracted crop height had a very high linear correlation with the field measured crop height, with an R-squared value of 0.98. For the object-based method, crops could be identified from the four-band airborne imagery and crop height, with an overall accuracy of 97.50% and a kappa coefficient of 0.95, which were 2.52% and 0.04 higher than those without crop height, respectively. When considering the maximum likelihood, crops could be mapped from the four-band airborne imagery and crop height with an overall accuracy of 78.52% and a kappa coefficient of 0.67, which were 2.63% and 0.04 higher than those without crop height, respectively.

This study presents a comprehensive deformation retrieval method based on the multi-baseline synthetic aperture radar interferometry InSAR technique for ground movement monitoring. Both the displacement pattern in at the large scale and detailed deformation for local areas are investigated by exploiting two sets of data generated at low and full spatial resolution. In particular, we focus on the description and estimation of non-linear deformation in full-resolution signals. A polynomial parameter model is built and the total least squares-singular value decomposition algorithm is proposed to control the overall errors of the system. The ascending Advanced Land Observing Satellite ALOS Phased Array type L-band SAR PALSAR data set on Tianjin China is used for the algorithm experiment and detailed displacement information between July 2008 and August 2010 is obtained. The experimental results are validated by levelling data and field surveys, and the good agreement proves the effectiveness of the algorithm.

As an advanced generation instrument of earth observation, small footprint full waveform light detection and ranging (LiDAR) technology has been widely used in the past few years. Decomposition and radiative correction is an important step in waveform data processing, it influences the accuracy of both information extraction and further applications. Based on a stepwise strategy, this study adopts Gaussian mixture model to approximate the LiDAR waveform. In addition to waveform decomposition, a relative correction model is proposed in this paper, the model considers the transmit pulses as well as the different of the travel path for implementing LiDAR waveform relative correction. Validation of the stepwise decomposition and relative correction model are carried out on LiDAR waveform acquired over Zhangye, China. The results indicate that stepwise decomposition identified the number of peaks in LiDAR waveforms, center position and width of each peak well. The relative radiometric correction also improves the similarity of waveforms which acquired at the same target.

There is a shortage of daily high spatial land surface temperature (LST) data for use in high spatial and temporal resolution environmental process monitoring. To address this shortage, this work used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Spatial and Temporal Data Fusion Approach (STDFA) to estimate high spatial and temporal resolution LST by combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST and Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The actual ASTER LST products were used to evaluate the precision of the combined LST images using the correlation analysis method. This method was tested and validated in study areas located in Gansu Province, China. The results show that all the models can generate daily synthetic LST image with a high correlation coefficient (r) of 0.92 between the synthetic image and the actual ASTER LST observations. The ESTARFM has the best performance, followed by the STDFA and the STARFM. Those models had better performance in desert areas than in cropland. The STDFA had better noise immunity than the other two models.

Wetland ecosystems are very important for ecological diversity and have a strong ability to sequester carbon. Through comparisons with field measured eddy covariance data, we evaluated the relationships between the light use efficiency (LUE) index and the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and land surface temperature (LST). Consequently, we have proposed a new model for the estimation of gross primary production (GPP) for wetland ecosystems using Moderate Resolution Imaging Spectroradiometer (MODIS) products, including these vegetation indices, LST and the fraction of photosynthetically active radiation (FAPAR) absorbed by the active vegetation. This model was developed and validated for a study site on Chongming Island, Shanghai, China. Our results show that photosynthetically active radiation (PAR) was highly correlated with the LST, with a coefficient of determination (R(2)) of 0.59 (p0.001). Vegetation indices, such as EVI, NDVI and LST, were highly correlated with LUE. We found that the product of vegetation indices (VIs) and a modified form of LST (Te) can be used to estimate LUE, with an R(2) of 0.82 (P0.0001) and an RMSE of 0.054 kg C per mol PAR. This new model can provide reliable estimates of GPP (R(2) of 0.87 and RMSE of 0.009 kg C m(-2) 8 d(-1) (P0.0001)).

Hyperspectral remote sensing data with bandwidth of nanometre (nm) level have tens or even several hundreds of channels and contain abundant spectral information. Different channels have their own properties and show the spectral characteristics of various objects in image. Rational feature selection from the varieties of channels is very important for effective analysis and information extraction of hyperspectral data. This paper, taking Shunyi region of Beijing as a study area, comprehensively analysed the spectral characteristics of hyperspectral data. On the basis of analysing the information quantity of bands, correlation between different bands, spectral absorption characteristics of objects and object separability in bands, a fundamental method of optimum band selection and feature extraction from hyperspectral remote sensing data was proposed.

To describe canopy emitting thermal radiance precisely and physically is one of the key researches in retrieving land surface temperature (LST) over vegetation-covered regions by remote sensing technology. This work is aimed at establishing gap probability models to describe the thermal emission characteristics in continuous plant, including the basic model and the sunlit model. They are suitable respectively in the nighttime and in the daytime. The sunlit model is the basic model plus a sunlit correcting item which takes the hot spot effect into account. The researches on the directional distribution of radiance and its relationship to canopy structural parameters, such as the leaf area index (LAI) and leaf angle distribution (LAD), were focused. The characteristics of directional radiance caused by temperature differences among components in canopy, such as those between leaf and soil, and between sunlit leaf or soil and shadowed leaf or soil, were analyzed. A well fitting between experimental data and the theoretical calculations shows that the models are able to illustrate the canopy thermal emission generally.

ZY-1 02C satellite is a new Chinese civil remote sensing satellite launched on December 22, 2011. It is the highest resolution civil remote sensing satellite in China which carried panchromatic/multispectral sensor and high resolution sensor. Compared with image of SPOT5 satellite, the radiation quality of ZY-1 02C sensors data was evaluated. The result showed: the SNR of ZY-1 02C is higher than SPOT5 panchromatic band; the power spectrum of ZY-1 02C is close to SPOT5 panchromatic band; however, the mean value and variance of ZY-1 02C data are smaller than SPOT5 panchromatic band, while the skewness and kurtosis of ZY-1 02C data are higher than SPOT5 panchromatic band; the definition of ZY-1 02C data is much smaller than SPOT5 panchromatic band and the entropy is a little smaller than SPOT5 panchromatic band.

Spatial and Temporal Fusion of Remote Sensing Data is a technology that can generate dense time series high spatial data whose spatial resolution is similar with high spatial resolution date, and temporal resolution is same with high temporal resolution data. Using Hai'an County of Jiangsu Province as the experimental area, we tested the ability of different wavelet functions and fusion methods in Spatial and Temporal Fusion. Through classification of fusion images and classification precision evaluation, the Spatial and Temporal Fusion Data which generated by appropriate wavelet functions and fusion methods can be used as current high spatial resolution image when it is missed.

An adaptive threshold discriminating Algorithm for remote sensing image corner detection was discussed in this paper. Firstly, this paper proposed a novel corner detection method, which discriminated the direction of corner by analyzing eight neighborhood direction gray gradients, then adopted the neighborhood gray gradient tracking method and two thresholds of gray gradient was adopted to detect corner point. By this corner detection method, corner of remote sensing image could be extracted correctly. Secondly, an adaptive threshold discriminating method for remote sensing image corner detection was introduced in this paper, the threshold of discriminant function could be determined adaptively by calculating probability density curvature extremum of gray gradient. By comparison with traditional empirical threshold methods, this adaptive threshold discriminating method was more intelligent and automated. The experiment illustrated that, the result of corner point detection was more objective and dependable, and it had more detection accuracy and efficiency. Meanwhile, the algorithm could detect suitable corner point, and it had more adaptability and applicable prospect.

An adaptive corner detection algorithm for remote sensing image was discussed in this paper. The main proposal of this paper was to detect corner of remote sensing image automatically and intelligently. This paper proposed a novel corner detection algorithm, which confirmed the direction of corner by analyzing eight neighborhood direction gray gradients, then adopted the neighborhood gray gradient tracking method and two thresholds of gray gradient was adopted to detect the correct corner. By this corner detection method, corner of remote sensing image could be extracted correctly. Meanwhile, the threshold of discriminant function could be determined adaptively by calculating probability density curvature extremum of gray gradient instead of traditional empirical threshold. The result of the experiment demonstrated that, the algorithm could detect valuable ground control point, it had more detection accuracy and efficiency, and it had more adaptability and applicable prospect. The result of ground control point detection was more objective and dependable.

In this paper, Moderate Resolution Image Spectroradiometer (MODIS) data with high spectral and temporal resolutions were used as input parameters for Chinese regional scale land cover classification. Firstly, Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Normalized Difference Soil Index (NDSI) were calculated as input spectral features relies on an annual time series of twelve MODIS 8-day composite reflectance images (MOD09) acquired during the year of 2007. The monthly EVI was produced by the maximum value composite; the three indices were added in the image to form a 10-spectral-bands image. In order to reduce the input feature space dimension, we resort to the mean Jeffries-Matusita distance as a statistical separability criterion to select the best spectral feature combination according to their ability of separating the land cover classes. Once we achieved, the monthly best combination spectral bands were dealt with Principal Component Analysis (PCA) method and their first three principal components were used as input parameters for decision tree classification. The result showed that the best combination of spectral bands added temporal information as input parameters can reach a certain high classification accuracy (81.16%) at moderate spatial scales without other accessorial data.© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

MODIS data has a high temporal and spectral resolution, and it can provide vegetation indices of high quality. By using MODIS NDVI time series with 250 m spatial resolution which were composite of 16 days in 2005, this work chose annual modulus of vector, maximum and minimum NDVI three indices to do classification. Training and validation samples were selected based on TM images and the 1:1,000,000 vegetation atlas of China. Then the land coverage map was generated using maximum likelihood classification (MLC) method. After post-classification process of the original classification result, the final land classification map of Keerqin sandy land was got in the end. The classification accuracy was assessed using validation samples and the result indicates that 250 m MODIS NDVI time series has advantage and potential in regional land coverage mapping. Also the classification method used in the paper could not only reduce the data amount and quicken the speed of classification, but also could reduce the disturbance of other invalidation information to classification and get better classification accuracy.

Remote sensing and GIS technologies were integrated to estimate runoff for Binjiang basin, China. Remotely sensed images from the Landsat satellites were used to develop land cover maps of the study area for the years 1990, 1995 and 2000. GIS analysis, based on land-cover and soil map data, was used to estimate SCS curve numbers on a 30-meter grid and to compute runoff depths for a 10 -year maximum rainfall event. Runoff coefficients were computed by the Rational method for each year of the study. Temporal changes in spatial distribution of land cover, runoff coefficients, and runoff volume were estimated. A runoff hydrograph, based on the above information and digital elevation model, was developed for the study area. The land cover for the study region showed an increase in urban area and a corresponding decrease in vegetation area. A increase in modeled runoff volume and peak flow is attributed to this change in land cover. The results of this study indicate a need for protecting vegetation to reduce the effect of floods in this basin.

The determination of terrestrial ecosystem carbon source/sink spatial pattern is becoming one of the hottest problems and many environment politics focus on it. As a new tool for terrestrial ecosystem carbon modelling at large scale from field plot, to region, to global, remote sensing is applied to initialize, drive, and validate the model, combined with geophysics information system (GIS) and computer modelling. Carbon flux models with remote sensing data as input may be classified as light use efficiency model, process model, and eco-physiological model based on “big leaf” hypothesis. The model generally includes two parts: NPP and soil respiration model to estimate carbon flux based on the principle that the carbon flux of ecosystem equal NPP minus heterogeneity respiration (soil respiration). Remote sensing, however, is more applied in NPP modeling but little in soil respiration estimation. The latter mostly based on relationship between soil respiration and soil temperature and is highly developed. Since remote sensing is applied to retrieve land surface temperature (LST) with infrared waveband, a hypothesis was put forward, that is, land surface temperature retrieved from infrared waveband can substitute soil temperature to estimate soil respiration. The hypothesis was validated with a field experiment and result was given in this article. The experiment located in a winter wheat field at Quzhou experiment station, Hebei province, China, from Apr 19 to May 20, in 2002. The soil respiration rate was measured with CID photosynthesis system, and canopy infrared temperature, soil surface temperature were measured respectively at same time. The station provided us soil moisture content data of whole growth of winter wheat. The result shows that the soil CO 2 efflux from winter wheat field is -0.03~1.38μmolm -2 s -1 . Its diurnal variation is well fitted with univariate quartic curve. Its variation in winter wheat heading growth period well coincide with temperature and soil moisture content. The Pearson correlation analysis shows that, on the averaged sense, for a day, soil CO 2 efflux significantly correlated with the temperature of the air (T air ), the soil surface (T sur ), the averaged thermal (T inf ) temperature respectively at the p-level

The primary purpose of this study was to estimate the boundary between vegetated and non-vegetated areas and to assess the condition of desertification in central Asia and western China located in arid and semiarid regions. Remote sensing data used in this study are a time-series of 10-day maximum Normalized Difference Vegetation Index (NDVI) composites derived from Global Area Coverage of Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2000. Taking place and development of desertification in the arid and semiarid regions directly influence the density and growth status of vegetation, making surface vegetation a most important indicator to desertification assessment. Vegetation is very sparse in desert and therefore onset of green-up in the desert was undetectable with AVHRR NDVI data. The occurrence of onset of green-up, as determined with time series NDVI data was used to identify desert and non-desert areas. The coefficient of variation (CoV) of the monthly NDVI (maximum-value composite) is used as a parameter to characterize the changes of vegetation in this work. The CoV can be used to compare the amount of variation in different sets of samples data. Changes in the value of the pixel-level CoV over time can be interpreted as a measure of vegetative biomass change over that time. The method to detect and quantify changes in CoV values for each pixel over 20-year period for which data was available is based on linear regression. If the CoV values exhibit a statistically significant decrease over time, it is possible to conclude that the area imaged in that pixel is under desertification.

In arid and semi-arid area of North China, one serious problem is soil degradation and expanding desertification, and annual net primary productivity (NPP) of terrestrial ecosystem is low, and the converted soil organic matter is also very small. Moreover, for short of precipitation, it is mainly the aeolian erosion of soil that causes severe loss of soil organic matter, which plays a negative role on NPP. According to the field data of second national soil profile survey, this paper computed the soil organic matter pool of surface soil, and its result indicates that desert has small pool of soil organic matter and grass land has big pool of soil organic matter. Further, based on remote sensing investigation of soil erosion, this paper computed annual soil loss and its distribution map, and estimated the soil organic matter lost for aeolian erosion. And NPP was also calculated by using light energy efficiency model. So based on pool of soil organic matter, loss of soil organic matter and NPP, this paper analyzed the balance of soil organic matter in North China, and we found that the overall situation of soil organic matter is loss in aeolian erosion area.

Integration of remotely-sensed and non-remotely-sensed information becomes an effective approach for vegetation classification. In this paper, vegetation in China is comprehensively classified by integration of NOAA-AVHRR data and geographical factors, such as temperature, precipitation and DEM. The procedure of comprehensive vegetation classification is chiefly composed of four steps: feature selection of NOAA data, creation of geographic information image, data integration and image comprehensive classification. Precision test and error analysis indicate a higher precision of the classification result.

Mixed pixels constitute AVHRR image. Evapotranspiration (ET) calculated from AVHRR image certainly includes some mistake information of ground objects which have no meaning as far as ET is concerned. Registration of AVHRR image and high spatial resolution image is carried out. A meteorology-RS combination model is used to calculate the ET of the AVHRR image and of the cropping area of the corresponding high resolution image respectively and the difference of these two ETs is given. The error is analyzed and a corresponding statistical calibration method adaptable to test region is given.

High spectral remote sensing is a hopeful technology in diagnosing crop nutrition background. With surface spectral measurement and laboratory biochemical analysis, the relationship between crop properties and spectral remote sensing data has been established. Seven chemical components - total chlorophyll, water crude protein, soluble sugar, N, P, K - were analyzed by laboratory chemical analyzing instrument. Foliar spectral property was detected outdoors by surface spectrometer. Chemical concentrations have been related to foliar spectral properties through stepwise multiple regression. The statistical equations between the chemical concentrations and reflectance as well as its several transformations were established. They underscored good estimation performance for chlorophyll, water crude protein, N and K with high squared multiple correlation coefficients (R2) values and high believable level. Especially R2 value of the equation between crude protein concentration and the first derivative of reflectance is 0.9564, which is the best result in the study of the fresh leave biochemistry up to now. On the basis of field experiment, an airborne remote sensing for crop nutrition monitoring was conducted in Shunyi County, Beijing, PR China. The sensor, made by Chinese Academy of Sciences, is in visible and near IR band. By image processing, the crop biochemistry map is obtained.

Land cover classification has long been primarily focused on automated image analysis applications and there is ongoing search for new classifiers that can yield improvements in results. This study shows the method of combining unsupervised classification and Artificial Neural Network (ANN) to the land cover classification of whole China and the time series National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) 1-kilometer (km) data is used. Some factors related to the effect on accuracy of land cover classification are discussed. The research involves the following steps: (1) Production of monthly maximum normalized difference vegetation index (NDVI). (2) Land cover classification system of China is proposed. (3) Unsupervised clustering of monthly NDVI data using ISOCLASS algorithm. (4) The preliminary identifying with the addition of digital elevation, ecoregions data and other land cover/vegetation reference data and extraction of the training data. (5) Land cover classification of China using Neural network. The results indicate that the accuracy of classification is much improved comparing with the common classification method.

Due to cloud coverage and obstruction, it is difficult to obtain useful images during the critical periods of monitoring vegetation using medium-resolution spatial satellites such as Landsat and Satellite Pour l'Observation de la Terre (SPOT), especially in pluvial regions. Although high temporal resolution sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), can provide high-frequency data, the coarse ground resolutions of these sensors make them unsuitable to quantify the vegetation growth processes at fine scales. This paper introduces a new data fusion model for blending observations of high temporal resolution sensors (e.g., MODIS) and moderate spatial resolution satellites (e.g., Landsat) to produce synthetic imagery with both high-spatial and temporal resolutions. By detecting temporal change information from MODIS daily surface reflectance images, our algorithm produced high-resolution temporal synthetic Landsat data based on a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image at the beginning time (T1). The algorithm was then tested over a 185×185 km2 area located in East China. The results showed that the algorithm can produce high-resolution temporal synthetic Landsat data that were similar to the actual observations with a high correlation coefficient (r) of 0.98 between synthetic imageries and the actual observations.