An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was di...An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was dividedinto three types according to its normalized difference vegetation index (NDVI)values: bare soil, vegetated area, and transition zone. For each type, BBE at813.5 mm was formulated as a nonlinear function of AVHRR reflectance forChannels 1 and 2. Given difficulties in validating coarse emissivity products withground measurements, the algorithm was cross-validated by comparing retrievedBBE with BBE derived through different methods. Retrieved BBE was initiallycompared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square errorwere less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 fortransition zones, and 0.002 and 0.005 for vegetated areas. Retrieved BBE wasalso compared with BBE obtained through the NDVI threshold method. Theproposed algorithm was better than the NDVI threshold method, particularly forbare soil. Finally, retrieved BBE and BBE derived from MODIS data wereconsistent, as were the two BBE values.展开更多
In order to provide a long time-series,high spatial resolution,and high accuracy dataset of land surface temperature（LST） for climatic change research,a modified Becker and Li＇s split-window approach is proposed in...In order to provide a long time-series,high spatial resolution,and high accuracy dataset of land surface temperature（LST） for climatic change research,a modified Becker and Li＇s split-window approach is proposed in this paper to retrieve LST from the measurements of Advanced Very High Resolution Radiometer（AVHRR） onboard National Oceanic and Atmospheric Administration（NOAA）-7 to-18 and the Visible and InfraRed Radiometer（VIRR） onboard FY-3A.For this purpose,the Moderate Resolution Transmittance Model（MODTRAN） 4.1 was first employed to compute the spectral radiance at the top of atmosphere（TOA） under a variety of surface and atmosphere conditions.Then,a temperature dataset consists of boundary temperature T s（which is one of the input parameters to MODTRAN）,and channels 4 and 5 brightness temperatures（T 4 and T 5） were constructed.Note that channels 4 and 5 brightness temperatures were simulated from the MODTRAN output spectral radiance by convolving them with the spectral response functions（SRFs） of channels 4 and 5 of AVHRRs and VIRR.The coefficients of modified Becker and Li＇s split-window approach for various AVHRRs and VIRR were subsequently regressed based on this temperature dataset using the least square method.As an example of validation,one AVHRR satellite image over Beijing acquired at 0312 UTC 27 April 2008 by AVHRR onboard NOAA-17 was selected to retrieve the LST image using the modified Becker and Li＇s approach.The comparison between this LST image and that from the MODIS level-2 LST product provided by the University of Tokyo in Japan indicates that the correlation coefficient is 0.88,the bias is 0.6 K,and the root mean square deviation（RMSD） is 2.1 K.Furthermore,about 70% and 37% pixels in the LST difference image,which is the result of retrieved LST image from AVHRR minus the corresponding MODIS LST image,have the values within ± 2 and ± 1 K,respectively.展开更多
Remote sensing techniques have the potential to provide information on agricultural crops quantitatively , instantaneously and above all nondestructively over large areas . Crop simulation models describe the relation...Remote sensing techniques have the potential to provide information on agricultural crops quantitatively , instantaneously and above all nondestructively over large areas . Crop simulation models describe the relationship between physiological processes in plants and environmental growing conditions. The integration between remote sensing data and crop growth simulation model is an important trend for yield estimation and prediction, since remote sensing can provide information on the actual status of the agricultural crop. In this study, a new model(Rice-SRS) was developed based mainly on ORYZA1 model and modified to accept remote sensing data as input from different sources. The model can accept three kinds of NDVI data: NOAA AVHRR(LAC)-NDVI,NOAA AVHRR(GAC)-NDVI and radiometric measurements-NDVI. The integration between NOAA AVHRR (LAC) data and simulation model as applied to Rice-SRS resulted in accurate estimates for rice yield in the Shaoxing area, reduced the estimating error to 1.027%,0.794% and (-0.787%) for early, single, and late season respectively. Utilizing NDVI data derived from NOAA AVHRR (GAC) as input in Rice-SRS can yield good estimation for rice yield with the average error (-7.43%). Testing the new model for radiometric measurements showed that the average estimation error for 10 varieties under early rice conditions was less than 1%.展开更多
基金the National High Technology Research and Development Program of China via Grant 2009AA122100the National Natural Science Foundation of China via Grant 40901167 and 41201331 and the Fundamental Research Funds for the Central Universities.
文摘An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was dividedinto three types according to its normalized difference vegetation index (NDVI)values: bare soil, vegetated area, and transition zone. For each type, BBE at813.5 mm was formulated as a nonlinear function of AVHRR reflectance forChannels 1 and 2. Given difficulties in validating coarse emissivity products withground measurements, the algorithm was cross-validated by comparing retrievedBBE with BBE derived through different methods. Retrieved BBE was initiallycompared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square errorwere less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 fortransition zones, and 0.002 and 0.005 for vegetated areas. Retrieved BBE wasalso compared with BBE obtained through the NDVI threshold method. Theproposed algorithm was better than the NDVI threshold method, particularly forbare soil. Finally, retrieved BBE and BBE derived from MODIS data wereconsistent, as were the two BBE values.
基金Supported by the National Science and Technology Special Funds for Infrastructure Work Projects of China (2006DAK31700)the GF Verification Program of the National Satellite Meteorological Center of China (220043001011003-1)
文摘In order to provide a long time-series,high spatial resolution,and high accuracy dataset of land surface temperature（LST） for climatic change research,a modified Becker and Li＇s split-window approach is proposed in this paper to retrieve LST from the measurements of Advanced Very High Resolution Radiometer（AVHRR） onboard National Oceanic and Atmospheric Administration（NOAA）-7 to-18 and the Visible and InfraRed Radiometer（VIRR） onboard FY-3A.For this purpose,the Moderate Resolution Transmittance Model（MODTRAN） 4.1 was first employed to compute the spectral radiance at the top of atmosphere（TOA） under a variety of surface and atmosphere conditions.Then,a temperature dataset consists of boundary temperature T s（which is one of the input parameters to MODTRAN）,and channels 4 and 5 brightness temperatures（T 4 and T 5） were constructed.Note that channels 4 and 5 brightness temperatures were simulated from the MODTRAN output spectral radiance by convolving them with the spectral response functions（SRFs） of channels 4 and 5 of AVHRRs and VIRR.The coefficients of modified Becker and Li＇s split-window approach for various AVHRRs and VIRR were subsequently regressed based on this temperature dataset using the least square method.As an example of validation,one AVHRR satellite image over Beijing acquired at 0312 UTC 27 April 2008 by AVHRR onboard NOAA-17 was selected to retrieve the LST image using the modified Becker and Li＇s approach.The comparison between this LST image and that from the MODIS level-2 LST product provided by the University of Tokyo in Japan indicates that the correlation coefficient is 0.88,the bias is 0.6 K,and the root mean square deviation（RMSD） is 2.1 K.Furthermore,about 70% and 37% pixels in the LST difference image,which is the result of retrieved LST image from AVHRR minus the corresponding MODIS LST image,have the values within ± 2 and ± 1 K,respectively.
文摘Remote sensing techniques have the potential to provide information on agricultural crops quantitatively , instantaneously and above all nondestructively over large areas . Crop simulation models describe the relationship between physiological processes in plants and environmental growing conditions. The integration between remote sensing data and crop growth simulation model is an important trend for yield estimation and prediction, since remote sensing can provide information on the actual status of the agricultural crop. In this study, a new model(Rice-SRS) was developed based mainly on ORYZA1 model and modified to accept remote sensing data as input from different sources. The model can accept three kinds of NDVI data: NOAA AVHRR(LAC)-NDVI,NOAA AVHRR(GAC)-NDVI and radiometric measurements-NDVI. The integration between NOAA AVHRR (LAC) data and simulation model as applied to Rice-SRS resulted in accurate estimates for rice yield in the Shaoxing area, reduced the estimating error to 1.027%,0.794% and (-0.787%) for early, single, and late season respectively. Utilizing NDVI data derived from NOAA AVHRR (GAC) as input in Rice-SRS can yield good estimation for rice yield with the average error (-7.43%). Testing the new model for radiometric measurements showed that the average estimation error for 10 varieties under early rice conditions was less than 1%.
