Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter...Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.展开更多
Exploration of TV white space(TVWS)is a promising solution to mitigate the spectrum shortage and provide opportunities for new applications.In this paper,we present a detailed analysis of spectrum utilisation over TVW...Exploration of TV white space(TVWS)is a promising solution to mitigate the spectrum shortage and provide opportunities for new applications.In this paper,we present a detailed analysis of spectrum utilisation over TVWS at different locations in London.Both short-term and long-term outdoor measurement campaigns are conducted over large scales to better understand the spectrum features and variations across multiple locations and time periods.Different from most fixed-location-only measurements,we also drive along the main streets of London with a portable moving node to measure the on-route spectrum density along with the corresponding geographical information,which allows us to study the features and variations of spectrum use through a continuous space.To better analyse the dynamic spectrum utilisation,a machine learning based analysis algorithm is developed over the real-world measurements.This approach allows us to characterise the similarity and variability in spectrum usage within and among different channels,locations,and time instances,which is critical for the secondary system deployment to efficiently exploit the white space.展开更多
基金financially supported by the National Basic Research Program of China (2009CB825105)the National Natural Science Foundation of China (41261090)
文摘Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.
基金supported in part by the Engineering and Physical Sciences Research Council,U.K.,under grant EP/R00711X/1,in part by Shenzhen Fundamental Research Fund under grants No.KQTD2015033114415450 and No.ZDSYS201707251409055by Guangdong Province grants No.2017ZT07X152 and No.2018B030338001+1 种基金in part by the Foundation for Distinguished Young Talents in Higher Education of Guangdong under grant 2018KQNCX222by the Natural Science Foundation of SZU under grant 2019115.
文摘Exploration of TV white space(TVWS)is a promising solution to mitigate the spectrum shortage and provide opportunities for new applications.In this paper,we present a detailed analysis of spectrum utilisation over TVWS at different locations in London.Both short-term and long-term outdoor measurement campaigns are conducted over large scales to better understand the spectrum features and variations across multiple locations and time periods.Different from most fixed-location-only measurements,we also drive along the main streets of London with a portable moving node to measure the on-route spectrum density along with the corresponding geographical information,which allows us to study the features and variations of spectrum use through a continuous space.To better analyse the dynamic spectrum utilisation,a machine learning based analysis algorithm is developed over the real-world measurements.This approach allows us to characterise the similarity and variability in spectrum usage within and among different channels,locations,and time instances,which is critical for the secondary system deployment to efficiently exploit the white space.
