Abstract
Abstract. As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper.
Highlights
With the development of remote sensing technology, spaceborne LiDAR has becoming one of the fastest and most accurate remote sensing methods for obtaining surface elevation information (Winker et al, 2003; Zhang et al, 2017)
It can be seen from the table that the maximum values of Mean Absolute Error (MAE) obtained by Gradient plus Inverse Distance Squared (GIDS), Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) are 1.2305, 2.0780 and 2.0567 respectively, and the mean values are 0.8167, 1.8785 and 1.8802
Facing the demand of spatial interpolation of temperature needed during the correction of LiDAR ranging, this paper firstly compares and analyzes three interpolation methods of IDW, OK and GIDS
Summary
With the development of remote sensing technology, spaceborne LiDAR has becoming one of the fastest and most accurate remote sensing methods for obtaining surface elevation information (Winker et al, 2003; Zhang et al, 2017). To perform atmospheric correction on the LiDAR ranging value, it is necessary to obtain the atmospheric parameters such as temperature, humidity, and pressure at the laser foot point (Niell et al, 1996; Xin et al, 2011). Sometimes we cannot directly obtain the atmospheric parameters in the region of LiDAR observation, so the method of spatial interpolation needs to be used. This paper makes a detailed comparison and analysis of the above three method effects using the monthly surface observation values in individual years (1981-2010) of Sichuan province observation stations. Among these three methods, GIDS performed better than the other two. In order to solve the problem of relatively poor interpolation effect in greatly undulate terrain, we proposed an improved
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