Optical Satellite Sensor Radiation Performance Evaluation And Cross Calibration Based On Remote Sensing Big Data

With the rapid development of remote sensing technology,remote sensing data plays an important role in many fields,and the requirements for the accuracy of remote sensing data are getting higher and higher.Due to the limited observation angle of a single satellite sensor and poor temporal and spatial continuity,it is caused by a single satellite sensor.Satellite products produced by satellite sensors are not highly accurate.The comprehensive application of multi-source remote sensing data has become the main means to solve this problem.The primary task of the comprehensive application of multi-source remote sensing data is to realize the normalization of multi-source remote sensing data through the radiometric calibration of the sensor.However,remote sensing data has the characteristics of various types and complex data formats.The traditional single-machine centralized management method is messy and inefficient,and cannot meet the requirements of realtime data extraction and calculation.Therefore,a way to manage and use massive amounts of remote sensing data is needed to solve a series of problems caused by the continuous expansion of data.At the same time,due to the complexity of the data structure,different sensors have different data processing methods when cross-calibrated,which creates a big obstacle to the joint use of multi-source satellite data.Based on the Hadoop cluster,this research proposes to construct remote sensing data with a linear quad-tree model,which organizes and reconstructs many unstructured data such as band information,latitude and longitude information,calibration parameters,etc.,which greatly reduces data manipulation Complexity.Under the premise of large data volume and long time series,in order to avoid a series of problems such as weather,cloud cover,and excessive observation angle of the calibration site,it is proposed to use deep convective cloud(DCC)as the calibration scene.FY-4A/AGRI,FY-2D,Meteosat-10,MTSAT-2 and other on-orbit optical satellite sensors conducted satellite radiation performance evaluation based on DCC targets;based on the evaluation results and calculated data,the thick ice cloud model was further selected to complete the target DCC image A series of correction operations such as BRDF correction of metadata,spectral correction of multi-source satellites,and distance correction between the sun and earth,etc.,Unified the cross-calibration process for handling massive amounts of remote sensing data.Through the comparative analysis of the corrected data,it can be seen that the errors caused by the observation angle,atmospheric scattering and other reasons are effectively avoided.Using the SpringCloud microservice architecture,the initial realization of the remote sensing big data platform was completed,and the data visualization realized through Vue,and finally completed the radiation performance evaluation and cross-radiation calibration work.