高分遥感共性产品生成和真实性检验技术体系

Technique system of remote sensing product generation and validation of GF common products

随着高分辨率对地观测系统重大专项的成功实施,GF-1—GF-7七型卫星共19种主要载荷发射升空,形成了中国遥感卫星多谱段、多模式的观测能力,可为各种科研和行业遥感应用提供源源不断的高空间、高时间和高光谱分辨率的高质量遥感数据。如何打通高分卫星遥感数据到信息的转换链,降低高分卫星数据应用门槛、提升高分卫星应用服务成效已成为急需破解的迫切问题。遥感定量产品的误差来源包括传感器成像、几何与辐射定标、数据预处理、定量反演与产品检验等各个环节,提高定量遥感产品精度是一个复杂的系统工程,各行业应用部门和多领域用户难以独立完成全流程数据处理、产品生产和检验。本文在分析高分卫星遥感产品体系的基础上,针对多用户共同需求,梳理了7大类共45种共性定量遥感产品;从全链条误差溯源和质量检验需要出发,提出了高分遥感共性产品生成和检验的技术体系,分析了算法测评—算法优化—产品生产—真实性检验等环节面临的关键技术;进而提出了高分遥感共性产品真实性检验平台与产品定型分系统的初步设计方案,并介绍了系统研发的最新进展;最后对高分共性产品应用前景进行了展望,构建高分遥感共性产品生成与真实性检验技术体系,对于保障高分卫星遥感共性产品精度和质量、提升高分卫星应用服务效益具有重要的意义。

GF-1—GF-7 satellite series with 19 major payloads has been launched with the continuous implementation of the high-resolution Earth Observation System(referred to as GF)in the past decade.This progress is vital in forming the multispectral and multimode observation capability of China’s Earth Observation System.Remote sensing data with high spatial,temporal,and spectral resolution have been obtained and widely used in scientific research and remote sensing applications.However,obtaining high-quality remote sensing information products from the original satellite data is a complicated scientific issue and faces huge challenges.Hence,the conversion chain from GF data to information must be urgently set up to reduce the remote sensing application threshold and improve the effectiveness of application services.The errors of remote sensing quantitative products are determined by accumulating a series of errors,such as sensor imaging error,calibration error,remote sensing data processing error,and quantitative inversion error.Thus,improving the accuracy of quantitative remote sensing products is a complex system engineering.Completing the whole process,including data processing,retrieval algorithm development,product generation,and validation independently,is challenging.Remote sensing algorithm test and product validation are the two crucial ways for the quality improvement of remote sensing products.Hence,this study proposes the technique system of GF common product generation and validation to improve the quality of GF remote sensing products further,thereby guaranteeing the improvement of the application quality and the extensive application area of GF remote sensing products.Lastly,the current progress of the GF common product validation and algorithm determination system platform is introduced and discussed.GF common products are required by more than two thematic remote sensing products.They can be validated using in situ observations.According to the GF common product system,the number of 39+6 products in seven categories are sorted out for the common requirements of multiple users,including geometric products,basic radiation products,land cover and land type products,energy balance products,vegetation products,water products,and atmosphere products.This study presents the technique flowchart of GF common product algorithm determination and product generation.The key technologies of algorithm testing,algorithm optimization,product generation,and validation are developed.Eleven national standards for remote sensing product validation are issued and implemented.Other group standards,such as GF common product generation,ground in situ observation,and validation of GF common remote sensing products,are being designed and compiled.Based on these validation technologies and the in situ data from the national network of GF remote sensing product validation field sites,the GF common product validation platform and product algorithm determination system platform can ensure the high quality of GF common products.Building such a technical system for GF common product generation and validation has great relevance for ensuring high accuracy and high quality to improve the efficiency of application services further.It requires the cooperation of multiple researchers from different units to research and develop common product retrieval algorithms.Moreover,the algorithm should be continuously tested to improve the accuracy of common products.