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6 results for “ground-based remote sensing”
Supplementary files for ground-based remote sensing of sesame drydown
<p>This is a collection of supplementary materials in support of the article entitled "Using normalized difference vegetation index to estimate sesame drydown and seed yield," which was published on Dec. 10, 2020 in Journal of Crop Improvement, 35:4, 508-521. </p> <p>Supplementary file 1 - list of computer program. This list documents Minitab macro, dryrate.mac, that was written to do regression analysis for sesame drydown data collected at the Texas A&M AgriLife Research Center at Uvalde. The macro automates the process of running m x 9 linear regressions, where m represents the number of sesame plots/genotypes and 9 the number of vegetation indices.</p> <p>Supplementary file 2 - sample input data to macro dryrate.mac. The file contains nine vegetation indices measured five times from six plots during sesame drydown in the 2019 growing season.</p> <p>Supplementary file 3 - sample output data. This file contains the outputs of dryrate, including selected regression coefficients for<br>each of the six sesame plots.</p> <p>Supplementary file 4 - Figure S1. Scatter plots of average values of nine vegetation indices for 60 sesame genotypes measured five<br>times from 6 September 2019 to 6 October 2019.</p> <p>Supplementary file 5 - Table S1. Values of the slopes depicting the regressions between nine vegetation indices and time for 60 sesame genotypes.</p> <p>Supplementary file 6 - Table S2. Average values of different vegetation indices measured on Day 9 during drydown for 60 sesame<br>genotypes.</p> <p>Supplementary file 7 - Figure S2. Relationships between sesame seed yield and the slopes of regressions between nine vegetation indices and time during the drydown period.</p> <p>Supplementary file 8 - Figure S3. Histograms of the coefficients of determinations for the 60 linear regressions relating different<br>vegetation indices with time (days during sesame drydown).</p> <p>Supplementary file 9 - Figure S4. Ranking from the highest to the lowest of the maximum NDVI (normalized difference vegetation index) for the 60 sesame genotypes, along with the measured seed yields for the corresponding genotypes.<br> </p>
Ground-Based Remote Sensing Observations at Marquette, Michigan
<p>This dataset contains observations used in "Multi-year analysis of rain-snow levels at Marquette, Michigan," Shates, Pettersen, L'Ecuyer, and Kulie, submitted to Journal of Geophysical Research - Atmospheres, in revision.</p> <p> </p> <p>The dataset includes daily files of Micro Rain Radar2 (MRR) and Precipitation Imaging Package (PIP) observations. The MRR and PIP are both hosted at the National Weather Service office in Marquette, MI (Pettersen, Kulie, et al., 2020; Kulie et al., 2021). The MRR is a 24 GHz vertically profiling radar and observations have been post-processed using Maahn and Kollias (2012). Key variables include radar reflectivity, Doppler velocity and spectral width. The PIP is a custom video disdrometer that records shadows of hydrometeors to obtain key microphysical variables that include particle size distributions and vertical velocity distributions (Pettersen, Bliven, et al., 2020). Additional processing provides precipitation rates in liquid water equivalent and the effective density (Pettersen et al., 2021). </p> <p>The files are separated into daily MRR files, daily PIP Particle Size Distribution (PSD) files, daily PIP Vertical Velocity Distribution (VVD) files, and daily PIP precipitation rate (rain and non-rain) and effective density (edensity) files. The PSD and VVD files contain one-minute resolution particle counts and fall speeds, respectively, for particle diameter bins.</p>
Dataset - Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2020) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.txt</strong></em> file.</p> <p>The dataset contains:</p> <p>- libRadtran input files</p> <p>- libRadtran output</p> <p>- name lists</p> <p>- GRAL simulation outputs</p>
Dataset: Six years ground-based remote sensing of microphysical properties of stratiform liquid clouds at Mace Head, Ireland
<p>A total of 118 stratiform water clouds observed by ground-based remote sensing instruments at the Mace Head Atmospheric Research Station at the West coast of Ireland from 2009 to 2015 were analyzed in terms of microphysical and optical characteristics as well as the impact of aerosols on these properties. The microphysical and optical cloud properties in the files were obtained using the algorithm SYRSOC (SYnergistic Remote Sensing Of Clouds).</p>
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
<p>Vertical <span>profile </span>observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profil<span>e</span> data. This study <span>introduces </span>a high-<span>time-</span>resolution (approximately 15 minutes) dataset of vertical <span>profile </span>observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal <span>variations and </span>diurnal <span>pattern</span> of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing <span>would </span>facilitate the scientific community <span>in </span>explor<span>ing</span> of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate <span>and </span>feedback mechanisms.</p>
Case stuides of ground-based remote sensing observations from Cabauw, NL, as obtained during the ACCEPT campaign in 2014.
<p>Datasets of Mira-35 NMRA and Mira-35 MBR4 cloud radars, PollyXT multiwavelength polarization lidar, and the corresponding Cloudnet categorization data for the case-study periods of 3 November and 7 November 2014. A detailed description of each dataset is provided below.</p> <p>1. 20141103_cesar_categorize.nc</p> <ul> <li>Categorization file produced with Cloudnet algorithm, as document on http://cloudnet.fmi.fi.</li> <li>The data were collected on November 3, 2014. They are based on Mira-35 NMRA radar, microwave radiometer, lidar ceilometer, and optical disdrometer. </li> </ul> <p>2. 20141107_cesar_categorize.nc</p> <ul> <li>Categorization file produced with Cloudnet algorithm, as document on http://cloudnet.fmi.fi.</li> <li>The data were collected on November 7, 2014. They are based on Mira-35 NMRA radar, microwave radiometer, lidar ceilometer, and optical disdrometer. </li> </ul> <p>3. 20141103_2000.pdm</p> <ul> <li>This dataset was collected on November 3, 2014, between 20:00 and 20:15 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> <p>4. 20141107_0915.pdm</p> <ul> <li>This dataset was collected on November 7, 2014, between 09:15 and 09:30 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> <p>5. 20141107_0945.pdm</p> <ul> <li>This dataset was collected on November 7, 2014, between 09:45 and 10:00 UTC, and includes four RHI scans performed using the Mira-35 MBR4 radar. It serves as the primary input for the technique used to retrieve the shape and orientation of ice particles.</li> </ul> <p>6. 20141107_0800.mmclx</p> <ul> <li>This dataset was collected on November 7, 2014, between 08:00 and 10:00 UTC, using the Mira-35 MBR4 radar. During this period, the radar performed eight PPI scans, with the data from each scan used to calculate wind speed and direction in 15-minute intervals. In this study, the data from the sixth and eighth scans were utilized to calculate wind speed and direction for the time intervals 09:15–09:30 UTC and 09:45–10:00 UTC, respectively.</li> </ul> <p>7. 20141103_2000.mmclx</p> <ul> <li>This dataset was collected on November 3, 2014, between 20:00 and 22:00 UTC, using the Mira-35 MBR4 radar. During this period, the radar performed eight PPI scans, with the data from each scan used to calculate wind speed and direction in 15-minute intervals. In this study, the data from the first scan were utilized to calculate wind speed and direction for the time intervals 20:00–2015 UTC.</li> </ul> <p>8. 20141103_cesar_pollyxt.nc</p> <ul> <li>This dataset was collected on November 03, 2014, between 08:00 and 10:00 UTC. Amongst others, the file provides time-height cross sections of 1064-nm attenuated backscatter coefficient and 532-nm volume depolarization ratio, which can be used to evaluate an observed cloud and aerosol scene for the occurrence of ice crystals and liquid water.</li> </ul> <p>9. 20141107_cesar_pollyxt.nc</p> <ul> <li>This dataset was collected on November 07, 2014, between 20:00 and 21:00 UTC. Amongst others, the file provides time-height cross sections of 1064-nm attenuated backscatter coefficient and 532-nm volume depolarization ratio, which can be used to evaluate an observed cloud and aerosol scene for the occurrence of ice crystals and liquid water.</li> </ul> <p> </p> <p> </p>
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