Soil organic matter(SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression(PLSR) mod...Soil organic matter(SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression(PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples(n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared(VNIR) spectra and six environmental factors(elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression(MLR), PLSR and geographically weighted regression(GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM; 2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively; 3) the spatial model(GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties.展开更多
The visible infrared radiometer(VIRR)is the first instrument with longest measurements equipped on the Fengyun(FY)polar-orbiting satellites.Through re-processing of the historic VIRR measurements,long-term(over 20 yr)...The visible infrared radiometer(VIRR)is the first instrument with longest measurements equipped on the Fengyun(FY)polar-orbiting satellites.Through re-processing of the historic VIRR measurements,long-term(over 20 yr)global data can be integrated from multiple participating VIRRs on a consistent radiometric scale,which are valuable to climate and climate change studies.Due to lack of an onboard calibration system for VIRR,the reflective solar bands must be vicariously calibrated.This study applied the multi-site vicarious approach for consistent calibration of the VIRR visible(VIS)and near-infrared(NIR)data,and produced calibration coefficients for five VIRRs on FY-1 C/D and FY-3 A/B/C.The data quality was then evaluated with observations.The reflectance predicted by using the radiative transfer model over multiple invariant desert and ocean targets was used to derive the calibration slope via a weighted fitting scheme,in which the weights are the inverse of the variance from a radiative transfer simulation evaluated with reference to Aqua moderate resolution imaging spectroradiometer(MODIS).The sensor-specific calibration coefficients were derived on a daily basis by using piecewise polynomials.The calibration reference of the VIRR solar band record was further traced to the Aqua MODIS Collection 6.1 reference calibration with a systematic correction derived from the Libya4 desert.The VIRR record was compared with the Aqua MODIS C6.1 calibration over the polar region based on simultaneous nadir overpass observations.The lifetime relative difference for each sensor are within 3.3%and 4.5%for channels 1 and 2.Invariant deserts were also employed to evaluate the stability and consistency of the VIRR record.In general,the means of the directional and spectral corrected reflectance for each sensor are within 1%of the 20-yr average,implying that the VIRR reflectance of the invariant targets is consistent to within 1%among the sensors for channels 1 and 2.The VIRR data thus derived are reliable.展开更多
基金Under the auspices of the Natural Science Foundation of Hubei(No.2018CFB372)the Fundamental Research Funds for the Central Universities(No.2662016QD032)+2 种基金the Key Laboratory of Aquatic Plants and Watershed Ecology of Chinese Academy of Sciences(No.Y852721s04)the Chinese National Natural Science Foundation(No.41371227)the National Undergraduate Innovation and Entrepreneurship Training Program(No.201810504023,201810504030)
文摘Soil organic matter(SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression(PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples(n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared(VNIR) spectra and six environmental factors(elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression(MLR), PLSR and geographically weighted regression(GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM; 2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively; 3) the spatial model(GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties.
基金Supported by the National Key Research and Development Program of China(2018YFB0504905 and 2018YFB0504900)。
文摘The visible infrared radiometer(VIRR)is the first instrument with longest measurements equipped on the Fengyun(FY)polar-orbiting satellites.Through re-processing of the historic VIRR measurements,long-term(over 20 yr)global data can be integrated from multiple participating VIRRs on a consistent radiometric scale,which are valuable to climate and climate change studies.Due to lack of an onboard calibration system for VIRR,the reflective solar bands must be vicariously calibrated.This study applied the multi-site vicarious approach for consistent calibration of the VIRR visible(VIS)and near-infrared(NIR)data,and produced calibration coefficients for five VIRRs on FY-1 C/D and FY-3 A/B/C.The data quality was then evaluated with observations.The reflectance predicted by using the radiative transfer model over multiple invariant desert and ocean targets was used to derive the calibration slope via a weighted fitting scheme,in which the weights are the inverse of the variance from a radiative transfer simulation evaluated with reference to Aqua moderate resolution imaging spectroradiometer(MODIS).The sensor-specific calibration coefficients were derived on a daily basis by using piecewise polynomials.The calibration reference of the VIRR solar band record was further traced to the Aqua MODIS Collection 6.1 reference calibration with a systematic correction derived from the Libya4 desert.The VIRR record was compared with the Aqua MODIS C6.1 calibration over the polar region based on simultaneous nadir overpass observations.The lifetime relative difference for each sensor are within 3.3%and 4.5%for channels 1 and 2.Invariant deserts were also employed to evaluate the stability and consistency of the VIRR record.In general,the means of the directional and spectral corrected reflectance for each sensor are within 1%of the 20-yr average,implying that the VIRR reflectance of the invariant targets is consistent to within 1%among the sensors for channels 1 and 2.The VIRR data thus derived are reliable.
