2,030 research outputs found
Estimating irrigated cropland from satellite and model soil moisture data over the contiguous US
No full textESA CCI Soil moisture: Recent improvements of the soil moisture retrievals from passive microwave sensors
No full textHomogeneity testing of the global ESA CCI multi-satellite soil moisture climate data record
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OEWI-Regio airborne Laserscanning toolbox. User's manual.
No full textThis document gives an overview of the OEWI toolbox software package developed by the Institute of Photogrammetry and Remote Sensing (I.P.F.) of the Vienna University of Technology, Austria, for the Department of Forest Inventory at the Federal Research and Training Centre for Forests, Natural Hazards and Landscape (BFW), Austria. The package includes tools for the pre-processing of laser scanning and forest inventory (FI) data, the co-registration of both, the calibration of spatial downscaling models for estimating growing stock, and routines for (cross-)validating available growing stock maps. The current document describes the handling and input parameters of the single routines even as the underlying theoretical basis.
The OEWI Toolbox was developed within the framework of the ÖWI-Regio project "Downscaling of forest inventory data by means of airborne laser scanning methods" funded by the Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management (BMLFUW).
The routines were written in the IDL language making use of some predefined ENVI routines. An installation of both IDL (6.0 or higher) and ENVI (4.0 or higher) are therefore necessary. We are still working on the OEWI toolbox, so if you find any bugs or have suggestions for improvement, you are kindly invited to report them to Wouter Dorigo ([email protected]; +43 (0)1-58801-12243). No rights can be claimed from the use of the softwar
Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge
No full textAutonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system dynamics. An example of a typical ADS problem that is used in this thesis is that of a wheeled robot driving through puddles of mud that, when entered, alters the velocity of the robot. When the robot is in such a puddle, it is in an ADS 'mud', and when it is not, it is in an ADS 'free'. ADS can be indirectly inferred through the analysis of lower-level data such as the velocity of the robot. The goal of this thesis is to design a general abstract discrete state estimator (ADSE) operating with limited prior knowledge. An ADSE is a hierarchical system for detecting changes in ADS. The ADSE should be general; applicable to multiple ADSE problems. The ADSE should further operate under limited prior knowledge: only assuming that the amount of ADS and the ADS that describes the regular operation are known. The basis for the ADSE designed in this thesis is a Gaussian hidden Markov model (GHMM), a hidden Markov model enhanced with Gaussian emissions. Randomly generated experiments are done on a simple but general ADSE problem. Two unsupervised learning methods derived from Expectation Maximization are evaluated, namely Baum-Welch (BW) and forward extraction (FWE). FWE is introduced in this thesis and is a simpler implementation of Viterbi extraction, leveraging assumptions of ADSE to in theory gain computational efficiency. We found that both BW and FWE exhibit superior performance compared to a likelihood-based baseline estimator when the maximum score of the learning curve is considered. When the final score is considered, in some cases, FWE displays a deteriorating learning curve, resulting in worse final scores compared to the baseline. Furthermore, it was found that the lower the overlap coefficient (therefore the less similar the ADS), the higher the maximum reached score. It was further shown that BW exhibits better convergence than FWE to the true model parameters. Besides this, FWE obtained comparable or in some cases even superior scores compared to BW. In general, from the results, the diversity of the experiments conducted, and the assumptions made we can conclude that the GHMM can be a general method for an ADSE with limited prior knowledge. To quantify the suitability of the GHMM for ADSE, further research should include the evaluation of different ADSE methods on the same problem. There exists a tradeoff between the lower computational cost FWE and the more stable but more computationally intensive BW learning. Therefore, future research can include a combination of these methods. Other extensions include extending the GHMM to a Gaussian mixture hidden Markov model to allow for the modeling of more complex distributions, or the application to multiple states or a changing environment.https://github.com/Wouter-deBoer/adseMechanical Engineering | Vehicle Engineering | Cognitive Robotic
Assessing the potential of using a soil moisture climatology based on L-band passive microwave observations for the ESA CCI Soil Moisture
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ESA CCI SM RZSM Long-term Climate Record of Root-Zone Soil Moisture from merged multi-satellite observations
No full text<h3><strong>Context and methodology</strong></h3>
<div>This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB" ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). </div>
<div>It contains information on the Root-Zone Soil Moisture (RZSM) content at different depth layers as derived from Surface SM satellite observations of the ESA CCI SM products<em>.</em></div>
<div><em> </em></div>
<div>The RZSM estimates and relative uncertainties are derived using the method of Pasik et al. (2023) forced with observations of the ESA CCI SM Combined product (Dorigo et al., 2017; Gruber et al., 2019; Preimesberger et al., 2021).</div>
<h3><strong>Technical details</strong></h3>
<div>The dataset provides global daily estimates for the 1978-2023 period at 0.25° (~25 km) horizontal resolution. The compressed downloadable rzsm_v09.1_1978_2023.tar.gz file is structured in sub-directories each including all files for a specific year.</div>
<div>Each netCDF file contains the data of a specific day (DD), month (MM), and year (YYYY) in a 2-dimensional (longitude, latitude) grid system. The file name has the following convention:</div>
<div>ESA_CCI_RZSM-YYYYMMDD000000-fv0.9.1.nc</div>
<div>The RZSM data reflects the estimates calibrated for 4 depth layers:</div>
<ul>
<li>rzsm1: 0-10 cm</li>
<li>rzsm2: 10-40 cm</li>
<li>rzsm3: 40-100 cm</li>
<li>rzsm4: 0-100 cm</li>
</ul>
<div>A package is available in python for reading the data as daily images and converting these images to time series and reading them. The source code for our python package and installation instructions are available here: <a href="https://github.com/TUW-GEO/esa_cci_sm" target="_blank" rel="noopener noreferrer">https://github.com/TUW-GEO/esa_cci_sm</a></div>
<ul>
<li>The package can be installed via pip using "pip install esa_cci_sm"</li>
<li>The documentation for this package is available here: <a href="https://esa-cci-sm.readthedocs.io/en/latest/" target="_blank" rel="noopener noreferrer">https://esa-cci-sm.readthedocs.io/en/latest/</a></li>
<li>The "parameter" argument (e.g., <a href="https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39" target="_blank" rel="noopener noreferrer">https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39</a>) can be specified to any of the layer variables (rzsm1, rzsm2, ...)</li>
</ul>
<div>Any software that can handle CF conform data should be able to import the raw netCDF files (e.g. <a href="https://code.mpimet.mpg.de/projects/cdo" target="_blank" rel="noopener noreferrer">CDO</a>, <a href="http://nco.sourceforge.net/" target="_blank" rel="noopener noreferrer">NCO</a>, <a href="https://www.qgis.org/" target="_blank" rel="noopener noreferrer">QGIS</a>, ArCGIS, Matlab, R, ...). You can also use the GUI software <a href="https://www.giss.nasa.gov/tools/panoply/" target="_blank" rel="noopener noreferrer">Panoply</a> to view each file.</div>
<h3>Reference</h3>
<p><strong>Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations, Geosci. Model Dev., 16, 4957–4976, </strong><a href="https://doi.org/10.5194/gmd-16-4957-2023,%202023"><strong>https://doi.org/10.5194/gmd-16-4957-2023, 2023</strong></a></p>
<h3>Additional citations</h3>
<p>Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, <a href="https://doi.org/10.1016/j.rse.2017.07.001">https://doi.org/10.1016/j.rse.2017.07.001</a>.</p>
<p>Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, <a href="https://doi.org/10.5194/essd-11-717-2019">https://doi.org/10.5194/essd-11-717-2019</a></p>
<p>Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W. (2021). Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record, in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.</p>
<h2>Related Records</h2>
<p>The following records are all part of the <a href="../communities/soilmoisture-climaterecords/records?q=&l=list&p=1&s=10&sort=newest">Soil Moisture Climate Data Records from satellites</a> community</p>
<table>
<tbody>
<tr>
<td>1</td>
<td>
<p>ESA CCI SM MODELFREE Surface Soil Moisture Record </p>
</td>
<td><a href="../doi/10.48436/rqfmp-jp420">10.48436/rqfmp-jp420</a></td>
</tr>
<tr>
<td>2</td>
<td>
<p>ESA CCI SM GAPFILLED Surface Soil Moisture Record </p>
</td>
<td><a href="../doi/10.48436/hcm6n-t4m35">10.48436/hcm6n-t4m35</a></td>
</tr>
</tbody>
</table>
<p> </p>
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