This lecture describes the theoretical process chain of sensor forward simulation. In particular, the individual steps are explained as the signal undergoes a transformation from surface material reflectance via top of atmosphere radiance up to sensor DN (level-0 data), the raw product of real sensors.
This slide collection is one of the basic lectures offered by HYPERedu. It contains all essential information for understanding, but it is recommended to have worked through the lecture “Pre-processing of hyperspectral remote sensing data” beforehand.
Data Processing
EnMAP-Box
The EnMAP-Box is a free and open source plug-in for QGIS. It is designed to process imaging spectroscopy data and particularly developed to handle data from the upcoming EnMAP satellite. The EnMAP-Box provides state-of-the-art applications for the processing of high dimensional spectral and temporal remote sensing data and a graphical user interface that enhances the GIS oriented visualization capabilities in QGIS by applications for visualization and exploration of imaging spectroscopy raster data and spectral libraries.
SAR Processing and Data Analysis
This presentation is part of a four-part series on the basics on Synthetic Aperture Radar.
Learning Objectives:
- Understand Sentinel Data
- Perform image preprocessing
- Analyze SAR imagery to classify land and water
Erica Podest
NASA/Jet Propulsion Laboratory
Dr. Erika Podest is a scientist with the Carbon Cycle and Ecosystems Group at NASA’s Jet Propulsion Laboratory. Her research focuses on using Earth observing satellites, particularly microwave sensors, for characterizing and monitoring wetland ecosystems and seasonal freeze/thaw dynamics in the northern high latitudes as related to the global carbon and water cycles and climate change. She is working on the Soil Moisture Active Passive (SMAP) mission, a NASA Earth observing satellite that launched on Jan. 31 2015, which is improving our understanding of Earth’s water and carbon cycles and our ability to manage water resources. (Source: NASA ARSET)
Procesamiento y análisis de datos de SAR
Esta presentación es parte de una serie de cuatro tomos sobre los conceptos básicos del Radar de Apertura Sintética.
Al finalizar esta presentación los participantes podrán:
- Entender los parámetros físicos de las imágenes SAR
- Describir la interacción de la señal de SAR con la superficie terrestre
- Describir los pasos necesarios para pre procesar las imágenes
- Entender la información que se puede extraer de las imágenes SAR
Erica Podest
NASA/Jet Propulsion Laboratory
Dr. Erika Podest is a scientist with the Carbon Cycle and Ecosystems Group at NASA’s Jet Propulsion Laboratory. Her research focuses on using Earth observing satellites, particularly microwave sensors, for characterizing and monitoring wetland ecosystems and seasonal freeze/thaw dynamics in the northern high latitudes as related to the global carbon and water cycles and climate change. She is working on the Soil Moisture Active Passive (SMAP) mission, a NASA Earth observing satellite that launched on Jan. 31 2015, which is improving our understanding of Earth’s water and carbon cycles and our ability to manage water resources. (Source: NASA ARSET)
Introducción SAR polarimétrico
Esta presentación es parte de una serie de cuatro tomos sobre los conceptos básicos del Radar de Apertura Sintética.
El objetivo es darles una introducción básica sobre la polarimetría y familiarizarlos con
- Expresiones matemáticas
- El formato de los datos
- El procesamiento de los datos para crear mapas de cobertura terrestre
Erica Podest
NASA/Jet Propulsion Laboratory
Dr. Erika Podest is a scientist with the Carbon Cycle and Ecosystems Group at NASA’s Jet Propulsion Laboratory. Her research focuses on using Earth observing satellites, particularly microwave sensors, for characterizing and monitoring wetland ecosystems and seasonal freeze/thaw dynamics in the northern high latitudes as related to the global carbon and water cycles and climate change. She is working on the Soil Moisture Active Passive (SMAP) mission, a NASA Earth observing satellite that launched on Jan. 31 2015, which is improving our understanding of Earth’s water and carbon cycles and our ability to manage water resources. (Source: NASA ARSET)
Introduction to NEST
In this tutorial you can learn how to get started with NEST, beginning with a brief description and download instructions. You will learn how to process geocoded, calibrated, Multi Look Intensity (MLI) images from Single Look Complex (SLC) data.
NEST is a free, open-source processing software for SAR data.
Under ‘related resources’ you find the TerraSAR-X sample data used in this tutorial, please make sure to download this data set with the tutorial.
Introduction to SARscape
In this tutorial you can learn how to get started with SARscape, beginning with a brief description and order instructions. You will learn how to process geocoded, calibrated, Multi Look Intensity (MLI) images from Single Look Complex (SLC) data.
SARscape is a commercial processing software for SAR data.
Under ‘related resources’ you find the TerraSAR-X sample data used in this tutorial, please make sure to download this data set with the tutorial.
Introduction to GAMMA
In this tutorial you can learn how to get started with GAMMA, beginning with a brief description and order instructions. You will learn how to process geocoded, calibrated, Multi Look Intensity (MLI) images from Single Look Complex (SLC) data. GAMMA is a commercial toolbox for
- RAW data processing
- InSAR
- DInSAR
- IPTA
Under ‘related resources’ you find the TerraSAR-X sample data used in this tutorial, please make sure to download this data set with the tutorial.
Introduction to SNAP
In this tutorial you can learn how to get started with SNAP, beginning with a brief description and download instructions. You will learn how to process geocoded, calibrated, Multi Look Intensity (MLI) images from Single Look Complex (SLC) data.
SNAP is a free, open-source processing software for SAR data.
On the website given within this tutorial, you will find Sentinel-1 sample data, please make sure to download this data set with the tutorial.