The CHRIS (Compact High Resolution Imaging Spectrometer) is a new imaging spectrometer, carried on board a new space platform called PROBA (Project for On Board Autonomy). The satellite was successfully launched in Oc...The CHRIS (Compact High Resolution Imaging Spectrometer) is a new imaging spectrometer, carried on board a new space platform called PROBA (Project for On Board Autonomy). The satellite was successfully launched in October 2001 by the European Space Agency (ESA). CHRIS operates over the visible/near infrared band (400-1050 nm). It has five work modes and can reach a maximum of 62 spectral bands. In this research, atmospheric correction based on hyperspectral images was performed on CHRIS images by using the popular radiance transfer code ACORN (Atmospheric Correction Now) and empirical algorithms. ACORN was also used to evaluate the calibration performance of CHRIS by the retrieved spectra of typical vegetation and soil. As a result,the maize reflectance spectrum corrected by ACORN could characterize vegetation reflectance in the range of 498-750 nm, but gave a fairly large deviation beyond 750 nm,showing the deficiency of spectral calibration beyond 750 nm. The ACORN-derived soil reflectance decreased beyond 800 nm, which was quite inconsistent with field-spectrummeasurement and showed that the calibration accuracy couldn't meet the requirements of ACORN for spectral and radiometric calibration within a certain spectral range. In addition,the stripes on the retrieved water-vapor content map indicated that the radiance-calibration performance of CHRIS is not perfect. As the first spaceborne hyperspectral imager of ESA, the calibration performance of CHRIS needs to be improved.展开更多
Sensitivity analyses were conducted for the retrieval of vegetation leaf area index （LAI） from multi- angular imageries in this study. Five spectral vegetation indices （VIs） were derived from Compact High Resoluti...Sensitivity analyses were conducted for the retrieval of vegetation leaf area index （LAI） from multi- angular imageries in this study. Five spectral vegetation indices （VIs） were derived from Compact High Resolution Imaging Spectrometer onboard the Project for On Board Autonomy （CHRIS/PROBA） images, and were related to LAI, acquired from in situ measurement in Jiangxi Province, China, for five vegetation communities. The sensitivity of LAI retrieval to the variation of VIs from different observation angles was evaluated using the ratio of the slope of the best-fit linear VI-LAI model to its root mean squared error. Results show that both the sensitivity and reliability of VI-LAI models are influenced by the heterogeneity of vegetation communities, and that perfor- mance of vegetation indices in LAI estimation varies along observation angles. The VI-LAI models are more reliable for tall trees than for low growing shrub-grasses and also for forests with broad leaf trees than for coniferous forest. The greater the tree height and leaf size, the higher the sensitivity. Forests with broad-leaf trees have higher sensitivities, especially at oblique angles, while relatively simple-structured coniferous forests, shrubs, and grasses show similar sensitivities at all angles. The multi-angular soil and/or atmospheric parameter adjustments will hope- fully improve the performance of VIs in LAI estimation, which will require further investigation.展开更多
基金supported by the National Natural Science Foundation of China(Grant No:40271085)the National"973"Key Basic Research Development Program(Grant No:2002CB412506).
文摘The CHRIS (Compact High Resolution Imaging Spectrometer) is a new imaging spectrometer, carried on board a new space platform called PROBA (Project for On Board Autonomy). The satellite was successfully launched in October 2001 by the European Space Agency (ESA). CHRIS operates over the visible/near infrared band (400-1050 nm). It has five work modes and can reach a maximum of 62 spectral bands. In this research, atmospheric correction based on hyperspectral images was performed on CHRIS images by using the popular radiance transfer code ACORN (Atmospheric Correction Now) and empirical algorithms. ACORN was also used to evaluate the calibration performance of CHRIS by the retrieved spectra of typical vegetation and soil. As a result,the maize reflectance spectrum corrected by ACORN could characterize vegetation reflectance in the range of 498-750 nm, but gave a fairly large deviation beyond 750 nm,showing the deficiency of spectral calibration beyond 750 nm. The ACORN-derived soil reflectance decreased beyond 800 nm, which was quite inconsistent with field-spectrummeasurement and showed that the calibration accuracy couldn't meet the requirements of ACORN for spectral and radiometric calibration within a certain spectral range. In addition,the stripes on the retrieved water-vapor content map indicated that the radiance-calibration performance of CHRIS is not perfect. As the first spaceborne hyperspectral imager of ESA, the calibration performance of CHRIS needs to be improved.
文摘Sensitivity analyses were conducted for the retrieval of vegetation leaf area index （LAI） from multi- angular imageries in this study. Five spectral vegetation indices （VIs） were derived from Compact High Resolution Imaging Spectrometer onboard the Project for On Board Autonomy （CHRIS/PROBA） images, and were related to LAI, acquired from in situ measurement in Jiangxi Province, China, for five vegetation communities. The sensitivity of LAI retrieval to the variation of VIs from different observation angles was evaluated using the ratio of the slope of the best-fit linear VI-LAI model to its root mean squared error. Results show that both the sensitivity and reliability of VI-LAI models are influenced by the heterogeneity of vegetation communities, and that perfor- mance of vegetation indices in LAI estimation varies along observation angles. The VI-LAI models are more reliable for tall trees than for low growing shrub-grasses and also for forests with broad leaf trees than for coniferous forest. The greater the tree height and leaf size, the higher the sensitivity. Forests with broad-leaf trees have higher sensitivities, especially at oblique angles, while relatively simple-structured coniferous forests, shrubs, and grasses show similar sensitivities at all angles. The multi-angular soil and/or atmospheric parameter adjustments will hope- fully improve the performance of VIs in LAI estimation, which will require further investigation.
