{"id":2188362,"date":"2023-12-11T10:30:31","date_gmt":"2023-12-11T18:30:31","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2188362"},"modified":"2023-12-11T10:30:31","modified_gmt":"2023-12-11T18:30:31","slug":"oceanographic-analysis-using-trajectory-data","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/oceanographic-analysis-using-trajectory-data","title":{"rendered":"Oceanographic Analysis using Trajectory Data in ArcGIS Pro"},"author":10352,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[],"industry":[],"product":[36561],"class_list":["post-2188362","blog","type-blog","status-publish","format-standard","hentry","category-analytics","product-arcgis-pro"],"acf":{"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/small_banner1.png","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/big_banner.jpg","authors":[{"ID":10352,"user_firstname":"Hong","user_lastname":"Xu","nickname":"Hong Xu","user_nicename":"hxu","display_name":"Hong Xu","user_email":"hxu@esri.com","user_url":"","user_registered":"2019-12-20 16:44:39","user_description":"Hong Xu is a Principal Software Product Engineer on Esri\u2019s imagery team, where she has been contributing since 1999. Her work focuses on advancing analytical methods for Earth observation data, including image time series (image cubes), hyperspectral imagery, multidimensional raster analysis, and altimetry data. She is passionate about bridging scientific research and software development to help users better understand environmental change through geospatial analysis.","user_avatar":""},{"ID":6701,"user_firstname":"Keith","user_lastname":"VanGraafeiland","nickname":"Keith VanGraafeiland","user_nicename":"kvangraafeiland","display_name":"Keith VanGraafeiland","user_email":"KVanGraafeiland@esri.com","user_url":"https:\/\/oceans-esrioceans.hub.arcgis.com\/","user_registered":"2018-03-02 00:18:44","user_description":"Keith VanGraafeiland is a Senior Principal GIS Engineer at Esri and Ocean Content Lead for the ArcGIS Living Atlas of the World. With over 18 years of experience, Keith specializes in marine and environmental GIS, remote sensing, and advanced data science. Keith\u2019s work bridges oceanography and data science, using machine learning and analytics to turn complex data into actionable insights for the GIS community.","user_avatar":""}],"short_description":"How ArcGIS can be used to perform oceanographic analysis using altimeter data such as Sentinel 6, Sentinel 3, Jason 3, and Jason 2.","flexible_content":[{"acf_fc_layout":"content","content":"

Various forms of remote sensing data are collected to help understand our planet. Satellite imagery, which captures the reflectance of the Earth’s surface, is a familiar asset within the GIS community and serves as a vital data source for GIS analysis and visualization. Altimetry data is a relatively new addition to the world of GIS and ArcGIS Pro. Altimetry data is collected via satellite sensors that record the elevation of surface features such as canopy height, sea surface height, and ice thickness.\u00a0 This new data and capability present an opportunity for enhanced analysis and understanding. Thanks to NASA’s commitment to open science initiatives\u2014characterized by transparency, accessibility, inclusivity, and reproducibility\u2014these altimetry data are freely and readily accessible to all researchers.<\/p>\n

ArcGIS aims to be a comprehensive system capable of handling various forms of remote sensing data, including the processing and analysis of data from altimetry sensors.\u00a0\u00a0 In this example<\/a>, CryoSat-2 altimetry data is used for analysis in ArcGIS Pro to quantify the amount of change\/loss in the Greenland ice sheet over time. \u00a0This is a great example of how altimetry data can help support analysis on land.\u00a0 Altimetry data also has effective applications in the ocean environment. The following examples demonstrate how this can be achieved.<\/p>\n

The ocean is vast and dynamic. Currently, the number of instruments collecting near-real time in-situ physical oceanographic observations makes it difficult to measure change in the open-ocean with high-accuracy. Satellite altimeters provide a global and precise measurement of ocean surface topography. These instruments operate by transmitting radar pulses to the ocean surface, determining the sensor’s height above the surface by measuring the time taken for the radar pulse to return. The sea surface height is then calculated as the difference between the sensor’s height and the sensor’s height above the surface.\u00a0 These valuable insights allow for monitoring changes in sea surface height, ocean circulation, and weather prediction for tropical cyclones, among other uses.<\/p>\n

ArcGIS Pro offers support for various altimetry sensors, including Sentinel 3 SRAL<\/a>, Sentinel 6<\/a>, Jason 2 and Jason 3<\/a>, with the latter two being added recently in the ArcGIS Pro 3.2 Release.<\/p>\n

The trajectory dataset<\/h1>\n

Altimetry sensors gather point data along flight paths, which we refer to as trajectory data. ArcGIS uses a trajectory dataset to effectively manage this type of data. This is like the mosaic dataset model, which organizes a collection of images, the trajectory dataset<\/a> serves a similar purpose, but managing a compilation of trajectory files containing point measurements. The point sublayer of the trajectory dataset can be utilized as a feature class layer during processing and analysis. You can generate a trajectory dataset from a folder of trajectory files using two geoprocessing tools:<\/p>\n