Earth observation from space data for vegetation monitoring in Victoria: project closure report

2016 ◽  
Author(s):  
A. McIntyre ◽  
N. Mueller ◽  
J. Pring
Keyword(s):  
Earth Observation ◽  
Vegetation Monitoring ◽  
Project Closure ◽  
Space Data

scholarly journals Sentinel-1 SLC Preprocessing Workflow for Polarimetric Applications: A Generic Practice for Generating Dual-pol Covariance Matrix Elements in SNAP S-1 Toolbox

2019 ◽  
Author(s):  
Dipankar Mandal ◽  
Divya Sekhar Vaka ◽  
Narayana Rao Bhogapurapu ◽  
V. S. K. Vanama ◽  
Vineet Kumar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Covariance Matrix ◽  
Natural Hazards ◽  
Urban Growth ◽  
Data Preprocessing ◽  
Earth Observation ◽  
Matrix Elements ◽  
Vegetation Monitoring ◽  
Radar Remote Sensing ◽  
Application Fields

Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community.

scholarly journals A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

2021 ◽  
Vol 13 (2) ◽  
pp. 287
Author(s):  
Katja Berger ◽  
Juan Pablo Rivera Caicedo ◽  
Luca Martino ◽  
Matthias Wocher ◽  
Tobias Hank ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Earth Observation ◽  
Training Data ◽  
Data Sets ◽  
Sampled Data ◽  
Vegetation Monitoring ◽  
Regression Algorithms ◽  
Regression Problems ◽  
Sampling Procedures

The current exponential increase of spatiotemporally explicit data streams from satellite-based Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.

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