Ultraviolet (UV) sensors on a geostationary orbit (GEO) have important potential value in atmospheric remote sensing, but
the satellites orbit mode of it is quit different from sun-synchronous orbit satellites, which result in the significant diurnal
and seasonal variations in radiation environment of earth observation and radiation signal of sensors, therefore, the effect to
sensor radiometric performance, such as signal to noise ratio for atmospheric ultraviolet remote sensing caused by
variations of solar angle is significant in the performance design of sensors. The synthetic ultraviolet sensor is set at the
geostationary orbit, 36000 km away from the sea level of the Equator with 8.75 degree field of view, and the subsatellite
track point of which is located at 90 degrees east longitude and Equator. The Satellite scanning angles (SA) from 0 to 8.648
degree that cover the earth surface are selected corresponding to the 10 degrees equal interval view zenith angle, and the
SA from 8.648 to 8.785 degree cover the earth lamb 100 km far away from earth tangent point. Based on the MODTRAN4
model, on normal atmospheric conditions, the distributions of the UV upwelling radiance from surface or limb viewing
path of the earth could be simulated with the change of sun's right ascension. Moreover, the average signal to noise ratio to
the atmospheric sounding is obtained in different UV spectra using the Sensor signal to noise ratio model. The results show
that the thresholds range, tendency and shape of signal to noise ratio have a variety of features affected by variation of Sun
hour angles and declinations. These result and conclusions could contribute to performance design of UV sensors on the
geostationary orbit.
The existing hyperspectral vegetation indices used for estimating the canopy leaf area index (LAI) of winter wheat (Triticum aestivum L.) performed well, but the use of such indices at late growth stages can lead to inaccurate results. To improve the performance of LAI models for wheat in late growth stages, the continuous wavelet transform (CWT) method was applied in this study and used to decompose the canopy reflectance and its first derivative into wavelet coefficients. The correlation scalograms of wavelet coefficients and the LAI were then constructed and used to extract the top 1% correlated region as the wavelet feature. The canopy LAI estimation model for late growth wheat was established at last and compared with models based on 12 different types of hyperspectral vegetation indices. The results showed that, compared with the estimation models using the hyperspectral vegetation indices (for which the R2 values were all less than 0.15 and the root-mean-square errors (RMSEs) were greater than 1), the CWT-based canopy LAI estimation model for late growth wheat had obvious improvements in accuracy (maximum R2 of 0.53 and minimum of RMSE of 0.78). Hence, this new method shows promise for use in agricultural and ecological applications.
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