The objective of this work was to evaluate the sensitivity of three different satellite signals (interferometric coherence (γ), backscattering coefficient (σ<sup>0</sup>) and NDVI) to corn biophysical pa...The objective of this work was to evaluate the sensitivity of three different satellite signals (interferometric coherence (γ), backscattering coefficient (σ<sup>0</sup>) and NDVI) to corn biophysical parameters (leaf area index, height, biomass and water content) throughout its entire vegetation cycle. All of the satellite and in situ data were collected during the Multi-spectral Crop Monitoring (MCM’10) experiment conducted in 2010 by the CESBIO Laboratory over eight different agricultural sites located in southwestern France. The results demonstrated that the NDVI is well adapted for leaf area index monitoring, whereas γ<sub>27.3°</sub> is much more suited to the estimation of the three other Biophysical Parameters throughout the entire crop cycle, with a coefficient of determination ranging from 0.83 to 0.99, using non-linear relationships. Moreover, contrary to the use of the NDVI or backscattering coefficients, the use of coherence exhibited a low sensitivity to the changes in vegetation and soil moisture occurring during senescence, offering interesting perspectives in the domain of applied remote sensing展开更多
Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not pro...Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not provide any information about the coastal wind field, as the coarse spatial resolution hampers the radar backscattering. Synthetic aperture radar (SAR) with a high spatial resolution and all-weather observation abilities has become one of the most important tools for ocean wind retrieval, especially in the coastal area. Conventional methods of wind field retrieval from SAR, however, require wind direction as initial information, such as the wind direction from numerical weather prediction models (NWP), which may not match the time of SAR image acquiring. Fortunately, the polarimetric observations of SAR enable independent wind retrieval from SAR images alone. In order to accurately measure coastal wind fields, this paper proposes a new method of using co-polarization backscattering coefficients from polarimetric SAR observations up to polarimetric correlation backscattering coefficients, which are acquired from the conjugate product of co-polarization backscatter and cross-polarization backscatter. Co-polarization backscattering coefficients and polarimetric correlation backscattering coefficients are obtained form Radarsat-2 single-look complex (SLC) data.The maximum likelihood estimation is used to gain the initial results followed by the coarse spatial filtering and fine spatial filtering. Wind direction accuracy of the final inversion results is 10.67 with a wind speed accuracy of 0.32 m/s. Unlike previous methods, the methods described in this article utilize the SAR data itself to obtain the wind vectors and do not need external wind directional information. High spatial resolution and high accuracy are the most important features of the method described herein since the use of full polarimetric observations contains more information about the space measured.This article is a useful ad展开更多
文摘The objective of this work was to evaluate the sensitivity of three different satellite signals (interferometric coherence (γ), backscattering coefficient (σ<sup>0</sup>) and NDVI) to corn biophysical parameters (leaf area index, height, biomass and water content) throughout its entire vegetation cycle. All of the satellite and in situ data were collected during the Multi-spectral Crop Monitoring (MCM’10) experiment conducted in 2010 by the CESBIO Laboratory over eight different agricultural sites located in southwestern France. The results demonstrated that the NDVI is well adapted for leaf area index monitoring, whereas γ<sub>27.3°</sub> is much more suited to the estimation of the three other Biophysical Parameters throughout the entire crop cycle, with a coefficient of determination ranging from 0.83 to 0.99, using non-linear relationships. Moreover, contrary to the use of the NDVI or backscattering coefficients, the use of coherence exhibited a low sensitivity to the changes in vegetation and soil moisture occurring during senescence, offering interesting perspectives in the domain of applied remote sensing
基金The National Natural Science Foundation of China under contract Nos 41306186,41076012 and 41276019the Youth Science Fund Project of State Oceanic Administration of China
文摘Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not provide any information about the coastal wind field, as the coarse spatial resolution hampers the radar backscattering. Synthetic aperture radar (SAR) with a high spatial resolution and all-weather observation abilities has become one of the most important tools for ocean wind retrieval, especially in the coastal area. Conventional methods of wind field retrieval from SAR, however, require wind direction as initial information, such as the wind direction from numerical weather prediction models (NWP), which may not match the time of SAR image acquiring. Fortunately, the polarimetric observations of SAR enable independent wind retrieval from SAR images alone. In order to accurately measure coastal wind fields, this paper proposes a new method of using co-polarization backscattering coefficients from polarimetric SAR observations up to polarimetric correlation backscattering coefficients, which are acquired from the conjugate product of co-polarization backscatter and cross-polarization backscatter. Co-polarization backscattering coefficients and polarimetric correlation backscattering coefficients are obtained form Radarsat-2 single-look complex (SLC) data.The maximum likelihood estimation is used to gain the initial results followed by the coarse spatial filtering and fine spatial filtering. Wind direction accuracy of the final inversion results is 10.67 with a wind speed accuracy of 0.32 m/s. Unlike previous methods, the methods described in this article utilize the SAR data itself to obtain the wind vectors and do not need external wind directional information. High spatial resolution and high accuracy are the most important features of the method described herein since the use of full polarimetric observations contains more information about the space measured.This article is a useful ad