Search

Remote sensing (RS) is a convenient technology to estimate the regional cultivation areas of crops. However, the accurate estimation of maize areas using RS over a broad region is a significant challenge due to the large phenology differences and insufficient prior knowledge in space. To address this issue, a new method was developed in this work. In this method, the correlation (r ) and root mean standard error (RMSE) between the time-series moderate resolution imaging spectroradiometer enhanced vegetation index (MODIS EVI) and the standard EVI curve of maize from a reference area are computed. Pixels with a high value of r and a low value of RMSE were identified as maize areas. The phenology information observed at agro-meteorological stations was also used to recognize maize pixels from the pixel-level phenology derived from time-series MODIS EVI. The proposed method provides an accurate characterization of the phenology differences over the study area by making use of the planting and maturity dates only. In addition, the few location-dependent parameters make the recognition of maize planting areas over large regions easier than previous studies. The proposed method was implemented over the Northeast China Plain (NECP) and North China Plain (NCP). The derived results were compared with official statistical results, and a close agreement was observed. At the city level, the satellite-derived estimates agreed well with the statistics with the R2(RMSE) of 0.86(110.97 k hm2) in the NECP and 0.76(68.74 k hm2) in the NCP. At the county level, the R2(RMSE) is 0.82(25.47 k hm2) in the NECP and 0.75(5.93 k hm2) in the NCP. At both temporal levels, the R2(RMSE) results obtained in this work are higher(lower) than those published in other studies. The obtained results indicate that the proposed method is effective in maize area estimation over broad regions.

Nitrogen dioxide (NO2) is one of the major atmospheric pollutants, and the concentration of NO2 is regarded as one of the indicators of air quality. In the past decades, China has experienced rapid economic growth and severe NO2 pollution to match. We evaluate the trends and spatiotemporal patterns of tropospheric NO2 over mainland China from 2005 to 2014 using vertical column density (VCD) datasets retrieved from the Ozone Monitoring Instrument (OMI). Results show that from 2005 to 2014, NO2 pollution regions have enlarged at the national scale, and high NO2 VCDs are mainly concentrated over highly populated regions in eastern China. The year 2011 is the turning point. Tropospheric NO2 VCDs first significantly increase by 0.19 × 1015 molec cm−2 year−1 (R 2 = 0.94, P = 0.002) from 2005 to 2011, and then decrease by 0.21 × 1015 molec cm−2 year−1 (R 2 = 0.97, P = 0.016) from 2011 to 2014. Since 2011, tropospheric NO2 VCDs over central-east China decrease remarkably. Tropospheric NO2 VCDs is higher in November (3.630 × 1015 molec/cm2), December (4.758 × 1015 molec/cm2), and January (4.863 × 1015 molec/cm2), while lower in July (1.684 × 1015 molec/cm2), August (1.627 × 1015 molec/cm2), and September (1.703 × 1015 molec/cm2), indicating that winter and spring are the most polluted seasons. Due to the huge gap in population density and industry development between western and eastern China, the spatial pattern of tropospheric NO2 VCDs shows large west-east difference.

Leaf optical model PROSPECT and canopy reflectance model SAIL are used to simulate vegetation canopy reflectance. The relationships between vegetation chlorophyll content and vegetation indices, view zenith angle and LAI are analyzed, in order to provide scientific basis for the estimation of chlorophyll content. We make field measurement of winter wheat in Luancheng County in Hebei Province, China, and the location, altitude, LAI, height, mean leaf angle of winter wheat at sampling points are obtained. In model simulation, LAI 4 is used, and view zenith angles 0°, 36°and 55°are selected according to five imaging angles of CHRIS (Compact High Resolution Imaging Spectrometer) sensor onboard PROBE satellite. Other view zenith angles include 40°, 60° and 90°. Many vegetation indices are selected to make this study. Results show that when LAI is 4, MCARI 705 and CI red edge are the most effective indices to estimate chlorophyll content. Under different view zenith angles, MCARI 705 and CI red edge are related linearly to chlorophyll content, and the correlation coefficients are all close to 1. The correlation coefficients of chlorophyll content with GRVI and MCARI 670 are greater than 0.91. At a certain view zenith angle, with the increase of chlorophyll content, RVI, PRI, GRVI, CI red edge and MCARI 705 tend to increase, while EVI and MCARI 670 tend to decrease. DVI values are close to saturation when chlorophyll content is greater than 40µg/cm2. When view zenith angles are less than 90°, the influence of view zenith angle on PRI is very small. Using GRVI and RVI calculated from Landsat-8 OLI image, chlorophyll content mapping of winter wheat is made.

SPOT-VGT NDVI datasets were used to analyze spatial and temporal changes of NDVI in vegetation growing seasons from 1998 to 2012 in Hebei Province, China. Vegetation types were come from MODIS product MCD12Q1 in 2004. The slope of liner regression equation was used to analyze the change trend of NDVI. The results showed that NDVIs in vegetation growing seasons showed a trend of fluctuating increase from 1998 to 2012. The lowest annual average NDVI in growing seasons was 0.49 in 1999, and the highest NDVI was 0.59 in 2012. The Pearson correlation coefficient (r) between annual average NDVI and mean precipitation was 0.893, the average NDVIs were related to precipitation more closely than to air temperature. The spatial distribution of NDVI in Hebei Province presented a trend of decrease from middle area to both sides. There were two areas with high NDVI values and two areas with low NDVI values. The highest average NDVI value 0.77 appeared in the southeast of Yanshan Mountains, especially near Wuling Mountain. The lowest NDVI value 0.01 appeared in the eastern coast of Hebei Province. Broadleaf forest had the highest average NDVI value 0.68 in growing seasons, followed by shrubland with NDVI value 0.64. The lowest NDVI 0.13 was in sparsely vegetated land or wetlands. The area of NDVI decrease in vegetation growing season accounted for 9.16%, while that of NDVI increase accounted for 90.84%, indicating that NDVI showed an increasing trend from 1998 to 2012. The areas with rapid NDVI increase were distributed in farming land of North China Plain, Taihang and Yanshan Mountainous regions. The areas with rapid NDVI decrease were mainly distributed in Bashang Plateau region, Beijing-Tianjin-Tangshan cities belt, Shijiazhuang, Xingtai and Handan cities, indicating that vegetation in these regions presented a trend of deterioration. The reasons were also analyzed.

This paper used 10-day SPOT-VEGATION NDVI time-series data from 1998 to 2008 in Hebei province, to analyze vegetation change of Hebei province during ten years. Vegetation, as one indicator of natural environment, can reflect situation of natural environment. From the analysis of NDVI, it was clear that vegetation grew best in July and August every year. In 1998–2008, the lowest value of NDVI appeared in 2000, the highest value of NDVI appeared in 2004. From the analysis of NDVI values in different times, it is found that there was some fluctuatation in the vegetation of Hebei province in these ten years, but it had the decreasing trend of fluctuatation. Vegetation growth had the trend of increasingly improved. Of course, it did not exclude the desertification of some area. From the analysis between air temperature, precipitation and NDVI during 1998–2004, the correlation between NDVI and precipitation was higher (0.70767) than that of temperature (0.53628). The spatial distribution of NDVI in 2004 was also mapped.

This paper evaluated the capability of FLAASH in ENVI software to make atmospheric correction for Hyperion hyperspectral image and ALI image. Hyperion and ALI sensors are two of the three instruments onboard NASA EO-1 satellite, New Millennium Program (NMP) launched on November 21, 2000, and Hyperion is the first spaceborne hyperspectral imaging spectrometer. The study area is Zhangye city (37deg28'N-39deg57'N, 97deg20'E-102deg12'E) in Heihe River valley of Gansu province, China. Using TM data with UTM projection, Hyperion hyperspectral data acquired on September 10, 2007 and ALI data on September 20, 2007 were geometrically and radiometrically corrected, and then atmospherically corrected using FLAASH. Surface reflectance spectra of corn, water body, desert and buildings were extracted from these two images and made comparison with apparent reflectance. Canopy reflectance spectra of corn were recorded using ASD Fieldspec spectroradiometer in near-real time to coincide with EO-1 satellite sensor overpass. According to filter function of ALI and central wavelength and Gaussian filter function based on full width at half maximum (FWHM) of Hyperion, the ASD reflectance spectra were resampled to corresponding Hyperion and ALI bands. Results showed that resampled ASD spectra of corn were consistent with spectra on Hyperion and ALI images after FLAASH. This demonstrated the effectiveness of atmospheric correction using FLAASH.

GPS data of August in 2004 obtained from 4 sites in Hong Kong GPS/MET network has been used to inverse the precipitable water vapor. The inversed GPS integrated water vapor has 1.44 mm RMSE and 0.97 mm BIAS compared with that from radiosonde data in Hong Kong Observatory, which shows good agreement between them. In this paper, we analyze the temporal and spatial change of Hong Kong summer water vapor using inversed GPS PWV, and have studied the relations of integrated water vapor with the average temperature, precipitation and ground vapor pressure. The results show that: there exists obvious temporal and spatial change of PWV, and for stations with close basic geographic location, it is obvious of the impact of altitude that the PWV in mountain station is lower than that in plain point under normal circumstances; ground vapor pressure has a good correlation with PWV; there often comes forth a precipitation process when the PWV increases rapidly, but there is no obvious correlation between the amount of integrated water vapor and the size of precipitation, and so the average amount before, the increase range in short time, and the maximum value of PWV should be considered in the precipitation forecast.

Canopy nitrogen content has strong relationship with net primary productivity, litter nitrogen and nitrogen mineralization rate, so the estimation of nitrogen content can provide valuable understanding of large-scale terrestrial carbon and nitrogen cycle. Hyperspectral remote sensing technology demonstrates the capacity for accurate biochemical component estimation of vegetation. This paper employed Hyperion hyperspectral data acquired over Xishuangbanna tropical area in Yunnan province, China to estimate nitrogen content based on normalized band depth (BNC) method. Hyperion data geometric and radiometric corrections were first made, and then Hyperion reflectance of 56 samples in 35 plots was extracted. Continuum removal was applied to the selected absorption features related to nitrogen. The BNC of 56 samples were calculated. Relationships between BNC values in Hyperion image and in situ field measured nitrogen content were analyzed using stepwise multiple linear regression. Results showed that central wavelengths in the model predicting nitrogen were 650.67nm, 2213.93nm, 2173.53nm and 671.02nm, and coefficient of determination (R2) was 0.505. Bands 650.67nm and 671.02nm coincided with chlorophyll absorption features highly related to nitrogen; 2213nm and 2173nm corresponded to protein and nitrogen absorption features. Correlation analysis showed that the biggest correlation coefficient between nitrogen and BNC was -0.573, which was at 650.67nm.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Incident photosynthetically active radiation (PAR) is a key variable needed by almost all terrestrial ecosystem models. Unfortunately, the current incident PAR products estimated from remotely sensed data at spatial and temporal resolutions are not sufficient for carbon cycle modeling and various applications. In this study, the author improved Eck and Dye model which is a PAR simulating model using ultraviolet reflectance. The advantage of the ultraviolet over existing visible band approaches to estimating insolation is the improved ability to discriminate clouds from high-albedo background surfaces. Ultraviolet spectral reflectance data from the Total Ozone Mapping Spectrometer(TOMS) was used to test the approach for Qianyanzhou station, southeast of China. Estimates of 8-day composition total incident PAR from the satellite technique differed from measurement values by higher than 3.47%. The incident PAR over Jiangxi Province was mapped in 2003 and 2004. The results indicate that this approach can produce reasonable PAR product and is suitable for regional and global applications, although more quantitative validation activities are still needed.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Land degradation is one of the serious environmental problems that can lead to poverty. North Hebei province is one part of eco-fragile region in North China, and is a transitional zone from farming to forest and grassland. This paper evaluated land degradation of North Hebei province from 1987 to 2001. Remote sensing data were time-series MODIS 500m 8-day surface reflectance and 16-day EVI product in 2001 and Landsat TM data in 1987 and 2000. TM and MODIS data were processed in ERDAS and ModisTool software including projection transformation and subset. Degradation evaluation indices, including enhanced vegetation index (EVI), land surface water index (LSWI) and modified soil adjusted vegetation index (MSAVI) of three subregions were calculated. Results showed that the total land degradation area was 64439km2 in 2001, of which high and severe land degradation level area were 20734km2 and 3948km2, accounting for 32.18% and 6.13%, respectively. Low land degradation level appeared in Yanshan mountainous area; medium land degradation level mainly appeared in Bashang plateau region, where vegetation types were cultivated land and grassland, and high land degradation level mainly appeared in Bashang plateau region and the basin area, where grass land degradation and soil erosion were serious.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The sensitivity of hyperspectral indices to LAI, chlorophyll contents and leaf internal structure at canopy level are investigated using simulated canopy reflectance dataset, this method can avoid expensive and impractical surface reflectance measurement, providing a theoretical basis for satellite-borne remote sensing. The model employed is PROSAIL that couples leaf reflectance model PROSPECT and canopy radiative transfer model SAIL. Hyperspectral indices used are NDVI, EVI, GI, RI, TVI, SIPI, PRI, TCARI, OSAVI, TCARI/ OSAVI, mNDVI705 and NDWI. Using PROSAIL model, leaf and canopy reflectance under different chlorophyll contents, leaf internal structures, LAI and water contents are first simulated and compared. Then using PROSAIL simulated canopy reflectance data, different hyperspectral indices are calculated, the sensitivity of vegetation indices to LAI and chlorophyll contents is analyzed in detail. And the sensitivity of vegetation indices to leaf internal structure is also analyzed. Results show that relationships between hyperspectral indices and LAI are approximately logarithmic while the relationship between hyperspectral indices and leaf internal structure is linear. EVI and TVI are good indicators to estimate LAI. GI, RI, TCARI, MNDVI705 can be used to estimate chlorophyll content. N has great influence on hyperspectral indices.

Hyperspectral remote sensing technology demonstrates the capacity for accurate vegetation identification. Hyperion image acquired simultaneously in 356-2577nm narrow spectral bands with Wnm sampling interval allows the capture of vegetation types information. This paper employed hyperion hyperspectral data and ETM+ data acquired over Xishuangbanna tropical vegetation area in Yunnan province, China to make agriculture land classification. Remote sensing data geometric correction and radiometric correction preprocessing were first made. Atmospheric correction of the Hyperion image was performed using second simulation of the satellite signal in the solar spectrum (6S) radiation transfer model. The first three principle components of hyperion accounted for 99.3% of total information, which were selected to make unsupervised classification using ISODATA algorithm. Overall classification accuracy of hyperion using spectral angle mapper (SAM) and unsupervised classification after principle component analysis were 52.3% and 65.7% respectively.

To meet the demand of large-scale agricultural monitoring system with remote sensing, extracting crop area planted must be rapid, precise and reliable. In this paper, winter wheat identification with MODIS data in 2004 is taken as example in North China. Applying spectral analysis and integrating genetic algorithm with neural network (GA-BP) is proposed, which gives attention to two optimization algorithm, genetic algorithm and back propagation algorithm. According to the spectral and biological characteristics of winter wheat, Red, Blue, NIR, ESWIR, LSWI, EVI are selected as characteristic parameters. Then GA-BP algorithm is used for winter wheat identification. Results show that compared with maximum likelihood and back propagation neural network classification algorithm, the GA-BP algorithm can not only run with better efficiency, but also achieve best accuracy of identification. Therefore, it is the operational method for agricultural condition monitoring with remote sensing and information service system at national level.

The evaluation index system of urban land intensive use potential is built with IKONOS remote sensing data as the main data source. Based on this, quantitative evaluation model of land intensive use potential is constructed through BP neural network method. This model is applied to evaluate the potential of land intensive use in Shijiazhuang. It has been proved that this kind of evaluation system, i.e. remote sensing image as the main data resource and BP model as the evaluation method, to evaluate urban land intensive use potential, is more effective, more practicable, more convenient and the evaluation result is more objective.