The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderate-resolution imaging spectroradiometer(MODIS) data in China.Paddy rice fields were extracted by identify...The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderate-resolution imaging spectroradiometer(MODIS) data in China.Paddy rice fields were extracted by identifying the unique char-acteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization.The characteristic could be reflected by the enhanced vegetation index(EVI) and the land surface water index(LSWI) derived from MODIS sensor data.Algorithms for single,early,and late rice identification were obtained from selected typical test sites.The algorithms could not only separate early rice and late rice planted in the same fields,but also reduce the uncertainties.The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics,and the spatial matching was examined by ETM+(enhanced thematic mapper plus) images in a test region.Major factors that might cause errors,such as the coarse spatial resolution and noises in the MODIS data,were discussed.Although not suitable for monitoring the inter-annual variations due to some inevitable factors,the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale,and they might provide reference for further studies.展开更多
We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts ...We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.展开更多
The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data colle...The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity.To solve the critical problems of estimating air temperature(T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days(GDDs) calculation from remotely sensed data,a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer(MODIS) data was proposed.This is a preliminary study to calculate heat accumulation,expressed in accumulative growing degree days(AGDDs) above 10 ℃,from reconstructed T a based on MODIS land surface temperature(LST) data.The verification results of maximum T a,minimum T a,GDD,and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels.Overall,MODIS-derived AGDD was slightly underestimated with almost 10% relative error.However,the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper.Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring,agricultural climatic regionalization,and agro-meteorological disaster detection at the regional scale.展开更多
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflec...Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.展开更多
The objectives of the study were to select suitable wavebands for rice leaf area index （LAI） estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by co...The objectives of the study were to select suitable wavebands for rice leaf area index （LAI） estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance （350-2500 nm） and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation： correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350-2500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1633 rim. They were followed by 444, 524, 576, 594, 804, 849, 974, 1074, 1219, 1510, and 2194 rim. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.展开更多
Ecological compensation is becoming one of key and multidiscipline issues in the field of resources and environmental management. Considering the change relation between gross domestic product (GDP) and ecological cap...Ecological compensation is becoming one of key and multidiscipline issues in the field of resources and environmental management. Considering the change relation between gross domestic product (GDP) and ecological capital (EC) based on remote sensing estimation, we construct a new quantitative estimate model for ecological compensation, using county as study unit, and determine standard value so as to evaluate ecological compensation from 2001 to 2004 in Zhejiang Province, China. Spatial differences of the ecological compensation were significant among all the counties or districts. This model fills up the gap in the field of quantitative evaluation of regional ecological compensation and provides a feasible way to reconcile the conflicts among benefits in the economic, social, and ecological sectors.展开更多
The winter oilseed rape（Brassica napus L.） accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/...The winter oilseed rape（Brassica napus L.） accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010–2011 growing season. The normalized difference vegetation index（NDVI） was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury： terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree（P0.01, two-tailed）. The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south- and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature（LST） of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.展开更多
Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of...Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of H J-1 CCD and Landsat-8 operational land imager （OLI） by using the ordinary least-squares （OLS） and construct higher temporal resolution vegetation indices （VIs） time-series data to extract the phenological param- eters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index （NDVI） and 2-band enhanced vegetation index （EVI2）, were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay （S-G） filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error （RMSE）, and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available.展开更多
e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detect...e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index （EVI） and land surface water index with a central wavelength at 2130 nm （LSW12130）. In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3x3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination （R^2） value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.展开更多
The main objective of this work was to compare the applicability of the single leaf(the uppermost leaf L1 and the third uppermost leaf L3) modified simple ratio(mSR705 index) and the leaf positional difference in the ...The main objective of this work was to compare the applicability of the single leaf(the uppermost leaf L1 and the third uppermost leaf L3) modified simple ratio(mSR705 index) and the leaf positional difference in the vegetation index between L1 and L3(mSR705L1-mSR705L3) in detecting nitrogen(N)-overfertilized rice plants.A field experiment consisting of three rice genotypes and five N fertilization levels(0,75,180,285,and 390 kg N/ha) was conducted at Xiaoshan,Hangzhou,Zhejiang Province,China in 2008.The hyperspectral reflectance(350-2500 nm) and the chlorophyll concentration(ChlC) of L1 and L3 were measured at different stages.The mSR705L1 and mSR705L3 indices appeared not to be highly sensitive to the N rates,especially when the N rate was high(above 180 kg N/ha).The mean mSR705L1-mSR705L3 across the genotypes increased significantly(P<0.05) or considerably from 180 to 285 kg N/ha treatment and from 285 to 390 kg N/ha treatment at all the stages.Also,use of the difference(mSR705L1-mSR705L3) greatly reduced the influence of the stages and genotypes in assessing the N status with re-flectance data.The results of this study show that the N-overfertilized rice plants can be effectively detected with the leaf positional difference in the mSR705 index.展开更多
The tasseled cap transformation of remote sensing data has been widely used in agriculture,forest,ecology,and landscape.In this paper,tasseled cap transformation coefficients appropriate for data from a new sensor(Chi...The tasseled cap transformation of remote sensing data has been widely used in agriculture,forest,ecology,and landscape.In this paper,tasseled cap transformation coefficients appropriate for data from a new sensor(China & Brazil Earth Resource Satellite(CBERS-02B)) are presented.The first three components after transformation captured 98% of the four-band variance,and represent the physical characteristics of brightness(coefficients:0.509,0.431,0.330,and 0.668),greenness(coefficients:0.494,0.318,0.324,and 0.741),and blueness(coefficients:0.581,0.070,0.811,and 0.003),respectively.We hope these results will enhance the application of CBERS-02B charge-coupled device(CCD) data in the areas of agriculture,forest,ecology,and landscape.展开更多
To further develop the methods to remotely sense the biochemical content of plant canopies,we report the results of an experiment to estimate the concentrations of three biochemical variables of corn,i.e.,nitrogen(N),...To further develop the methods to remotely sense the biochemical content of plant canopies,we report the results of an experiment to estimate the concentrations of three biochemical variables of corn,i.e.,nitrogen(N),crude fat(EE) and crude fiber(CF) concentrations,by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed,and a set of estimation models were established using curve-fitting analyses. Coefficient of determination(R2),root mean square error(RMSE) and relative error of prediction(REP) of estimation models were calculated for the model quality evaluations,and the possible opti-mum estimation models of three biochemical variables were proposed,with R2 being 0.891,0.698 and 0.480 for the estimation models of N,EE and CF concentrations,respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation,and that the first derivative reflectances at 759 nm,1954 nm and 2370 nm were most suitable to develop the estimation models of N,EE and CF concentrations,respectively. In addition,the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained,especially for nitrogen(r=0.948).展开更多
There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the c...There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.展开更多
The present study aims to identify the narrow spectral bands that are most suitable for characterizing rice biophysical parameters. The data used for this study come from ground-level hyperspectral reflectance measure...The present study aims to identify the narrow spectral bands that are most suitable for characterizing rice biophysical parameters. The data used for this study come from ground-level hyperspectral reflectance measurements for five rice species at three levels of nitrogen fertilization during the growing period. Reflectance was measured in discrete narrow bands between 350 and 2 500 nm. Observed rice biophysical parameters included leaf area index （LAI）, wet biomass and dry biomass. The stepwise regression method was applied to identify the optimal bands for rice biophysical parameter estimation. This research indicated that combinations of four narrow bands in stepwise regression models explained 69% to 83% variability for LAI, 56% to 73% for aboveground wet biomass and 70% to 83% for leaf wet biomass. An overwhelming proportion of rice information was in a particular portion of near infrared （NIR） （1 100-1 150 nm）, red-edge （700-750 nm）, and a longer portion of green （550-600 nm）. These were followed by the moisture-sensitive NIR （950-1 000 nm）, the intermediate portion of shortwave infrared （SWlR） （1 650-1 700 nm）, and another portion of NIR （1 000-1 050 nm）.展开更多
Net primary productivity （NPP） is a key component of energy and matter transformation in the terrestrial ecosystem, and the responses of NPP to global change locally and regionally have been one of the most importan...Net primary productivity （NPP） is a key component of energy and matter transformation in the terrestrial ecosystem, and the responses of NPP to global change locally and regionally have been one of the most important aspects in climatevegetation relationship studies. In order to isolate causal climatic factors, it is very important to assess the response of seasonal variation of NPP to climate. In this paper, NPP in Xinjiang was estimated by NOAA/AVHRR Normalized Difference Vegetation Index （NDVI） data and geographic information system （GIS） techniques. The impact of climatic factors （air temperature, precipitation and sunshine percentage） on seasonal variations of NPP was studied by time lag and serial correlation ageing analysis. The results showed that the NPP for different land cover types have a similar correlation with any one of the three climatic factors, and precipitation is the major climatic factor influencing the seasonal variation of NPP in Xinjiang. It was found that the positive correlation at 0lag appeared between NPP and precipitation and the serial correlation ageing was 0 d in most areas of Xinjiang, which indicated that the response of NPP to precipitation was immediate. However, NPP of different land cover types showed significant positive correlation at 2 month lag with air temperature, and the impact of which could persist 1 month as a whole. No correlation was found between NPP and sunshine percentage.展开更多
基金supported by the National High-Tech Research and Development Program (863) of China(No.2006AA120101)the National Natural Science Foundation of China(No.40871158/D0106)the Key Technologies Research and Development Program of China(No.2006BAD10A01)
文摘The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderate-resolution imaging spectroradiometer(MODIS) data in China.Paddy rice fields were extracted by identifying the unique char-acteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization.The characteristic could be reflected by the enhanced vegetation index(EVI) and the land surface water index(LSWI) derived from MODIS sensor data.Algorithms for single,early,and late rice identification were obtained from selected typical test sites.The algorithms could not only separate early rice and late rice planted in the same fields,but also reduce the uncertainties.The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics,and the spatial matching was examined by ETM+(enhanced thematic mapper plus) images in a test region.Major factors that might cause errors,such as the coarse spatial resolution and noises in the MODIS data,were discussed.Although not suitable for monitoring the inter-annual variations due to some inevitable factors,the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale,and they might provide reference for further studies.
基金Project supported by the National High-Tech Research and Development Program (863) of China (No. 2006AA120101)the National Natural Science Foundation of China (Nos. 40871158 and 40875070)the Key Technologies Research and Development Program of China (No. 2006BAD10A01)
文摘We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.
基金Project supported by the National Key Technology R&D Program of China (No. 2012BAH29B02)the PhD Programs Foundation of Ministry of Education of China (No. 200100101110035)
文摘The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity.To solve the critical problems of estimating air temperature(T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days(GDDs) calculation from remotely sensed data,a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer(MODIS) data was proposed.This is a preliminary study to calculate heat accumulation,expressed in accumulative growing degree days(AGDDs) above 10 ℃,from reconstructed T a based on MODIS land surface temperature(LST) data.The verification results of maximum T a,minimum T a,GDD,and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels.Overall,MODIS-derived AGDD was slightly underestimated with almost 10% relative error.However,the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper.Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring,agricultural climatic regionalization,and agro-meteorological disaster detection at the regional scale.
基金supported by the National Basic Research Program (973) of China (No.2010CB126200)China Postdoctoral Science Foundation Project (No.20090451437)
文摘Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.
基金supported by the National Natural Science Foundation of China (No. 40571115)the Hi-Tech Research and Development Program (863) of China (No. 2006AA120101)the National Basic Research Program (973) of China (No. 2006BAD10A09)
文摘The objectives of the study were to select suitable wavebands for rice leaf area index （LAI） estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance （350-2500 nm） and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation： correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350-2500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1633 rim. They were followed by 444, 524, 576, 594, 804, 849, 974, 1074, 1219, 1510, and 2194 rim. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.
基金Project (No. 2006AA120101) supported by the Hi-Tech Research and Development Program (863) of China
文摘Ecological compensation is becoming one of key and multidiscipline issues in the field of resources and environmental management. Considering the change relation between gross domestic product (GDP) and ecological capital (EC) based on remote sensing estimation, we construct a new quantitative estimate model for ecological compensation, using county as study unit, and determine standard value so as to evaluate ecological compensation from 2001 to 2004 in Zhejiang Province, China. Spatial differences of the ecological compensation were significant among all the counties or districts. This model fills up the gap in the field of quantitative evaluation of regional ecological compensation and provides a feasible way to reconcile the conflicts among benefits in the economic, social, and ecological sectors.
基金Project supported by the National Natural Science Foundation of China(No.41171276)
文摘The winter oilseed rape（Brassica napus L.） accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010–2011 growing season. The normalized difference vegetation index（NDVI） was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury： terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree（P0.01, two-tailed）. The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south- and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature（LST） of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.
基金supported by the National High-Tech R&D Program(863)of China(No.2012AA12A30703)the Fundamental Research Funds for the Central Universities,China
文摘Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of H J-1 CCD and Landsat-8 operational land imager （OLI） by using the ordinary least-squares （OLS） and construct higher temporal resolution vegetation indices （VIs） time-series data to extract the phenological param- eters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index （NDVI） and 2-band enhanced vegetation index （EVI2）, were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay （S-G） filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error （RMSE）, and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available.
基金Project supported by the National High-Tech R&D Program (863) of China(No.2012AA12A30703)the Meteorology Industry Special Project of China Meteorological Administration(CMA)(No.GYHY 201306036)the Ph.D Programs Foundation of the Ministry of Education of China(No.20100101110035)
文摘e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index （EVI） and land surface water index with a central wavelength at 2130 nm （LSW12130）. In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3x3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination （R^2） value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.
基金Project (Nos.2007AA10Z102 and 2006AA10Z201) supported by the National High-Tech R & D Program (863) of China
文摘The main objective of this work was to compare the applicability of the single leaf(the uppermost leaf L1 and the third uppermost leaf L3) modified simple ratio(mSR705 index) and the leaf positional difference in the vegetation index between L1 and L3(mSR705L1-mSR705L3) in detecting nitrogen(N)-overfertilized rice plants.A field experiment consisting of three rice genotypes and five N fertilization levels(0,75,180,285,and 390 kg N/ha) was conducted at Xiaoshan,Hangzhou,Zhejiang Province,China in 2008.The hyperspectral reflectance(350-2500 nm) and the chlorophyll concentration(ChlC) of L1 and L3 were measured at different stages.The mSR705L1 and mSR705L3 indices appeared not to be highly sensitive to the N rates,especially when the N rate was high(above 180 kg N/ha).The mean mSR705L1-mSR705L3 across the genotypes increased significantly(P<0.05) or considerably from 180 to 285 kg N/ha treatment and from 285 to 390 kg N/ha treatment at all the stages.Also,use of the difference(mSR705L1-mSR705L3) greatly reduced the influence of the stages and genotypes in assessing the N status with re-flectance data.The results of this study show that the N-overfertilized rice plants can be effectively detected with the leaf positional difference in the mSR705 index.
基金Project (No. 40871158/D0106) supported by the National Natural Science Foundation of China
文摘The tasseled cap transformation of remote sensing data has been widely used in agriculture,forest,ecology,and landscape.In this paper,tasseled cap transformation coefficients appropriate for data from a new sensor(China & Brazil Earth Resource Satellite(CBERS-02B)) are presented.The first three components after transformation captured 98% of the four-band variance,and represent the physical characteristics of brightness(coefficients:0.509,0.431,0.330,and 0.668),greenness(coefficients:0.494,0.318,0.324,and 0.741),and blueness(coefficients:0.581,0.070,0.811,and 0.003),respectively.We hope these results will enhance the application of CBERS-02B charge-coupled device(CCD) data in the areas of agriculture,forest,ecology,and landscape.
基金Project supported by the National Natural Science Foundation of China (No. 40271078)the Basic Research Program of Science and Technology Department of China (No. 2003DEA2C010-13)
文摘To further develop the methods to remotely sense the biochemical content of plant canopies,we report the results of an experiment to estimate the concentrations of three biochemical variables of corn,i.e.,nitrogen(N),crude fat(EE) and crude fiber(CF) concentrations,by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed,and a set of estimation models were established using curve-fitting analyses. Coefficient of determination(R2),root mean square error(RMSE) and relative error of prediction(REP) of estimation models were calculated for the model quality evaluations,and the possible opti-mum estimation models of three biochemical variables were proposed,with R2 being 0.891,0.698 and 0.480 for the estimation models of N,EE and CF concentrations,respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation,and that the first derivative reflectances at 759 nm,1954 nm and 2370 nm were most suitable to develop the estimation models of N,EE and CF concentrations,respectively. In addition,the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained,especially for nitrogen(r=0.948).
基金Supported by the National Natural Science Foundation of China （30371018） and the State Key Basic Research and Development Plan of Chfna （2003DEA2C010-13）.
文摘There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.
基金the National Natural Science Foundation of China (40571115)the Hi-Tech Research and Development Program (863) of China(2006AA10Z203).
文摘The present study aims to identify the narrow spectral bands that are most suitable for characterizing rice biophysical parameters. The data used for this study come from ground-level hyperspectral reflectance measurements for five rice species at three levels of nitrogen fertilization during the growing period. Reflectance was measured in discrete narrow bands between 350 and 2 500 nm. Observed rice biophysical parameters included leaf area index （LAI）, wet biomass and dry biomass. The stepwise regression method was applied to identify the optimal bands for rice biophysical parameter estimation. This research indicated that combinations of four narrow bands in stepwise regression models explained 69% to 83% variability for LAI, 56% to 73% for aboveground wet biomass and 70% to 83% for leaf wet biomass. An overwhelming proportion of rice information was in a particular portion of near infrared （NIR） （1 100-1 150 nm）, red-edge （700-750 nm）, and a longer portion of green （550-600 nm）. These were followed by the moisture-sensitive NIR （950-1 000 nm）, the intermediate portion of shortwave infrared （SWlR） （1 650-1 700 nm）, and another portion of NIR （1 000-1 050 nm）.
基金Supported by the Hi-Tech Research and Development (863) Program of China (2006AA12010103)the China Meteorological Administration (CCSF2006-37)Xinjiang Meteorological Bureau (QSR2003010006).
文摘Net primary productivity （NPP） is a key component of energy and matter transformation in the terrestrial ecosystem, and the responses of NPP to global change locally and regionally have been one of the most important aspects in climatevegetation relationship studies. In order to isolate causal climatic factors, it is very important to assess the response of seasonal variation of NPP to climate. In this paper, NPP in Xinjiang was estimated by NOAA/AVHRR Normalized Difference Vegetation Index （NDVI） data and geographic information system （GIS） techniques. The impact of climatic factors （air temperature, precipitation and sunshine percentage） on seasonal variations of NPP was studied by time lag and serial correlation ageing analysis. The results showed that the NPP for different land cover types have a similar correlation with any one of the three climatic factors, and precipitation is the major climatic factor influencing the seasonal variation of NPP in Xinjiang. It was found that the positive correlation at 0lag appeared between NPP and precipitation and the serial correlation ageing was 0 d in most areas of Xinjiang, which indicated that the response of NPP to precipitation was immediate. However, NPP of different land cover types showed significant positive correlation at 2 month lag with air temperature, and the impact of which could persist 1 month as a whole. No correlation was found between NPP and sunshine percentage.
