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1,138 results for “Modis”
ModIs Dust AeroSol (MIDAS): A global fine resolution dust optical depth dataset
<p>Monitoring and describing the spatiotemporal variability of dust aerosols is crucial to understand their multiple effects, related feedbacks and impacts within the Earth system. This study describes the development of the MIDAS (ModIs Dust AeroSol) dataset. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at global scale and fine spatial resolution (0.1° x 0.1°) over a 15-year period (2003-2017). This new dataset combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1, Level 2), along with DOD-to-AOD ratios provided by MERRA-2 reanalysis to derive DOD on the MODIS native grid. The uncertainties of MODIS AOD and MERRA-2 dust fraction with respect to AERONET and LIVAS, respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the “dust belt”, in the Tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere where dust sources are smaller. MIDAS, MERRA-2 and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with collocated global DOD averages of 0.033, 0.031 and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89), only showing a small positive bias (0.004 or 2.7%). Among the major dust areas of the planet, the highest R values (> 0.9) are found at sites of N. Africa, Middle East and Asia. MIDAS expands, complements and upgrades existing observational capabilities of dust aerosols and it is suitable for dust climatological studies, model evaluation and data assimilation.</p>
Cloud-free snow cover area in the Pyrenees from MODIS
<p>This dataset contains the output of a gapfilling algorithm applied to MODIS snow products for the Pyrenees mountains as presented by Gascoin et al. (2015) and updated to the period 2000-Sep-01 to 2015-08-31 (15 hydrological years)</p> <ol> <li>Pirineos_gapfilled.tif: a multiband geotiff raster file in WGS84 UTM30N (EPSG:32630) at 500 m resolution with values 200 (snow) or 25 (no snow); <p>Corner Coordinates:<br> Upper Left ( 607750.000, 4789250.000) ( 1d40'21.72"W, 43d14'54.08"N)<br> Lower Left ( 607750.000, 4665250.000) ( 1d41'46.55"W, 42d 7'55.05"N)<br> Upper Right ( 973750.000, 4789250.000) ( 2d49'18.48"E, 43d 6'27.65"N)<br> Lower Right ( 973750.000, 4665250.000) ( 2d43' 8.76"E, 41d59'47.87"N)<br> Center ( 790750.000, 4727250.000) ( 0d32'47.24"E, 42d38'34.10"N)</p> </li> <li>Pirineos_gapfilled_dates.csv: a csv file indicating the date corresponding to each band (year, month, day)</li> <li>dem_Pirineos_UTM30_px500.tif: a geotiff raster of the elevation in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>aspect_Pirineos_UTM30_px500.tif: a geotiff raster of the slope aspect in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>Pirineos_gapfilled_probamap.png: a map of the mean annual number of snow days (snow cover duration) made from 1.</li> <li>Pirineos_gapfilled_scats.png: a plot of the timeseries of the daily snow cover area in km² over the Pyrenees mountain range from 2000-Sep-01 to 2015-08-31 made from 1.</li> </ol> <p><strong>Reference</strong></p> <p>Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337-2351, doi:10.5194/hess-19-2337-2015, 2015. http://doi.org/10.5194/hess-19-2337-2015</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/63NQASRDPDB0.</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Aqua Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/ZFAEMQGSR4XD.</p>
MODIS-adjusted NDVI3g for Central Europe v1.1
<p>The noise filtered and MODIS-adjusted NDVI3g dataset was created from the original NDVI3g (Pinzon and Tucker, 2014) to support vegetation related research in Central Europe. Details about the method used for the creation of this dataset are available in Kern et al. (2016). Further information and datasets can be found at http://nimbus.elte.hu/NDVI_CE/.</p> <p> </p> <p>References</p> <p>Kern, A., Marjanović, H., Barcza, Z., 2016. Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013. Remote Sensing 8, 955. doi:10.3390/rs8110955</p> <p>Pinzon, J.E.; Tucker, C.J., 2014. A Non-Stationary 1981-2012 AVHRR NDVI<sub>3g</sub> Time Series. Remote Sensing 6, 6929-6960. doi:10.3390/rs6086929</p> <p> </p>
modis
<p>Notes from the Author</p> <p>Creators</p> <p>Open source MODIS dataset[NASA. Dr.Hayes and Dr.Dekhtyar modified the original dataset and created an answerset with the help of analysts.</p> <p>Past Usage</p> <ul> <li>Improving Requirements Tracing via Information Retrieval, Jane Huffman Hayes, Alex Dekhtyar, and James Osborne, in Proceedings, 11th International Requirements Engineering Conference (RE 2003), pp. 151-161, September 2003, Monterey Bay, CA.</li> <li>Helping Analysts Trace Requirements: An Objective Look (2004) Jane Huffman Hayes, Alex Dekhtyar, Senthil Karthikeyan Sundaram, and Sarah Howard, in Proceedings, 12th International Requirements Engineering Conference (RE 2004), pp. 249-261, September 2004, Kyoto, Japan.</li> <li>A Framework for Comparing Requirements Tracing Experiments, Jane Huffman Hayes, Alex Dekhtyar, accepted, pending revisions, International Journal on Software Engineering and Knowledge Engineering (IJSEKE), special issue, 2004(5).</li> <li>Jane Huffman Hayes, Alex Dekhtyar, and James M. Carigan, “Recommending a Framework for Comparison of Requirements Tracing Experiments”, in on-line proceedings of the Workshop on Empirical Studies of Software Maintenance (WESS 2004), Chicago, IL, September 2004.</li> <li>Text Mining for Software Engineering: How Analyst Feedback Impacts Final Results, Jane Huffman Hayes, Alex Dekhtyar and Senthil Karthikeyan Sundaram, accepted, MSR’2005: Second International Workshop on Mining Software Repositories, St. Louis, MO, May 2005.</li> </ul> <p>Relevant Information</p> <p>Requirements tracing is defined as “the ability to describe and follow the life of a requirement, in both a forward and backward direction, through the whole systems lifecycle1.” It helps us in assuring that all requirements have been implimented.</p> <p>We have implemented and evaluated a variety of IR methods including tf-idf vector retrieval, tf-idf retrieval with simple thesaurus, and Latent Semantic Indexing (LSI).</p> <p>TF-IDF model: “vector model (also known as tf-idf model) for information retrieval is defined as follows. Let V = {k1,…,kN} be the vocabulary of a given document collection. Then, a vecto r model of a document d is a vector (w1,…,wN) of keywords weights, where wi is computed as wi = tfi(d)*idf , where tfi(d) is the so-called term frequency: the frequency of keyword ki in the document d, and idfi, called inverse document frequency is computed as idfi = log(n/dfi), where n is the number of documents in the document collection and dfi is the number of documents in which keyword ki occurs. Given a document vector d=(w1,…,wN) and a similarly computed query vector q=(q1,…,qN) the similarity between d and q is defined as the cosine of the angle between the vectors.[+ Simple Thesaurus: “This method extends tf-idf model with a simple thes aurus of terms and key phrases. A simple thesaurus T is a set of triples <t1,t2,a>, where t1 and t2 are matching thesaurus terms and a is the similarity coefficient between them. Thesaurus terms can be either single keywords or key phrases(sequences of two or more keywords). The vector model is augmented to account for thesaurus matches as follows. First, all thesaurus terms that are not keywords (i.e., thesaurus terms that consist of more than one keyword) are added as separate keywords to the document collection vocabulary. Given a thesaurus T={<ki,kj,aij>}, and document/query vectors d=(w1,…,wN), q=(q1,…,qN), the similarity between d and q is compute d[2](2]”</p> <p>TF-IDF).”</p> <p>Latent Semantic Indexing (LSI) : “LSI is a dimension reduction technique based on Singular Value Decomposition (SVD) of the term-by-document matrix that can be constructed by putting tf-idf vectors of all documents in a single matrix. SVD transforms the original matrix into a product of two orthogonal matrices and a diagonal matrix of eigenvalues. By considering only the top k eigenvalues, we can obtain an appro ximation of the original matrix by a smaller matrix. Rows of the matrix can be compared to each other using the cosine similarity described above.[Feedback : To incorporate interactive work with analyst into RETRO, we have implemented relevance feedback for the IR methods studied. Relevance feedback works as follows: the analyst conveys to RETRO both positive (true link found) and negative (false positive found) information. The relevance feedback processor recomputes the vector qnew for the query q by adding to it positive information and subtracting negative information as specified in [2](3]”</p> <p>Relevance).</p> <p>Dataset details : This dataset is a modified dataset from [based on open source NASA Moderate Resolution Imaging Spectroradiometer (MODIS) documents. The dataset contains 19 high level and 49 low-level requirements. The trace for the dataset was manually verified. The “theoretical true trace” (answerset) built for this dataset consisted of 41 correct links. Each of the high and low-leve files contain the text of one requirement element. Take, for example, SDP3.2-2. The file with the name SDP3.2-2 is a text file that contains:</p> <p>Each MODIS standard data product shall be produced within the data volume and processing load allocation shown in Table B-1 in plain text.</p> <p>The files in the high and the low directory are in the same format.</p> <p>The handtrace.txt file is the answerset. It maps high-level requirements to their low level children.</p> <p>The handtrace.txt file has this format:</p> <p>% SDP3.2-2 L1APR01-I-3 % SDP3.3-1 L1APR01-I-1 % SDP3.3-2 L1APR03-I-2 L1APR01-I-2 % SDP3.3-4 L1APR03-I-2 L1APR01-I-2 L1APR01-I-1 .. .. .. ..</p> <p>where SDP3.2-2 is the identifier of a high level requirement, and L1APR01-I-3 is the identifier of the only low level requirement that traces to it. The items are separated by tabs.</p> <p>So, for example, high-level requirement SDP3.3-4 has three children requirements: L1APR03-I-2, L1APR01-I-2, L1APR01-I-1.</p> <p>Metrics:</p> <p>a) RECALL is the percentage of the actual matches that are found.</p> <p>recall = number of links found/ total number of links</p> <p>b) PRECISION is the ratio of the true links found and the number of candidate links generated.</p> <p>precision = number of links found/ total number of candidate links</p> <p>Please refer to 2 for the definition of other primary and secondary metrics.</p> <p>Feedback strategy:</p> <p>“For each method, four different feedback strategies or behaviors, called Top 1, Top 2, Top 3 and Top 4 were tested. The Top i behavior meant that at each iteration, we simulated correct analyst feedback for the top i unmarked candidate links from the list for each high-level requirement. For example, for each high level requirement, Top 1 behavior examined the top candidate link suggested by the IR procedure that had not yet been marked as true. If the link was found in the verified trace, it was marked as true, otherwise as false. After repeating the Top i relevance feedback procedure for each high level requirement, the answers were submitted to the feedback processing module. At this point, the Standard Rochio procedure was used to update query (high-level requirement) keyword weig hts, and to submit the new queries to the IR method. The process continued for a maximum of eight iterations or until the results had converged.[technique:</p> <p>A filtering technique is a simple decision procedure that examines each candida te link produced by the IR method and decides whether to show it to the analyst. In our study, in addition to the test run involving no filtering, we used filters with different threshold values. For example, filter with threshold value 0.1 will throw out all the candidate links that have relevance less than 0.1.</p> <p>Expreimental Results:</p> <p>TABLE 1</p> <p>Method Prec. Recall Sel. tf-idf 7.9% 75.6% 41.9% tf-idf+TH 10.1% 100.0% 43.1% LSI (10/100) 6.3% 92.6% 64.1% LSI+TH (10/100) 6.5% 95.1% 63.7% LSI (19/200) 4.2% 63.4% 65.2% LSI+TH (29/200) 5.4% 80.4% 65.8%</p> <p>The table shows the results of running different methods on Modis dataset. Please refer to [2](3]”</p> <p>filtering) and for more results.</p> <p>References:</p> <p># J. Matthias. Requirements tracing. Communications of the ACM, 41(12), 1998. # Jane Huffman Hayes, Alexander Dekhtyar, Senthil Sundaram, Sarah Howard, “Helping Analysts Trace Requirements: An Objective Look”, in Proceedings of IEEE Requirements Engineering Congerence (RE) 2004, Kyoto, Japan, September 2004, pp. 249-261. # Baselines in Requirements Tracing, Senthil Karthikeyan Sundaram, Jane Huffman Hayes and Alex Dekhtyar, accepted, PROMISE’2005: International Workshop on Predictor Models in Software Engineering , St. Louis, MO, May 2005. # Level 1A (L1A) and Geolocation Processing Software Requirements Specification, SDST-0591, GSFC SBRS, September 11, 1997. # MODIS Science Data Processing Software Requirements Specification Version 2, SDST-089, GSFC SBRS, November 10, 1997.</p> <p>Number of Instances</p> <ul> <li>49 low level requirements</li> <li>19 high level requirements</li> </ul> <p>Directory Content</p> <p>The ModisDataset folder contains answerset (handtrace.txt), the high directory and the low directory. The high directory contains the high-level requirements, the low directory contains the low level requirements.</p> <p>Reference</p> <p>See above section “Relevant Information” for further reference.</p> <p>People</p> <ul> <li>Jane Huffman Hayes</li> </ul>
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot.
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot.
Fig.ç7.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3980), Nomarski photomicrographs. A, Segments 5 and 6, ventral view; B, segments 8 and 9, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; si, sieve plate; sp, sternal plate; tp, tergal plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç7.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3980), Nomarski photomicrographs. A, Segments 5 and 6, ventral view; B, segments 8 and 9, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; si, sieve plate; sp, sternal plate; tp, tergal plate.
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot.
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate.
Fortnight Climatology Average of Corrected Chlorophyll-a Concentration from MODIS Level-2 Data in the Algerian Basin (2003-2018)
<p>We deposited the corrected MODIS Level-2 Chlorophyll data after a specific processing steps applied for the first time to the standard cloud-corrected Level-2 Chlorophyll fields to detect and remove spurious local patterns that affect data quality, even in time series averages.</p> <p>From a purely spatial point of view, the quality of the Chlorophyll field is improved more significantly by the outlier removal than the previous synthetic representation suggests.</p>
STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Jing, Y., Li, X., and Shen, H.: STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) </strong></p> <p><strong>for China, Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, 2022.</strong></p> <p>1. A recent 20-year cloud-free MODIS normalized difference snow index (NDSI) collection for </p> <p>China (excluding sea area) is generated using a Spatio-Temporal Adaptive fusion method with </p> <p>erroR correction (STAR).</p> <p>2. The STAR NDSI collection is derived from daily 1-km MODIS NDSI datasets (MOD10A1 and </p> <p>MYD10A1).</p> <p>3. It is provided using a WGS_1984_UTM_48N projection, with the data format of TIFF images.</p> <p>4. Each ZIP contains NDSI data for one hydrological year (August to next July). After </p> <p>uncompressing into the TIFF format, the files are named as "NDSI_ yyyymmdd_Daily_500M_</p> <p>V01.tif" for NDSI data and "QA_yyyymmdd_Daily_500M_V01.tif" for quality assessment (QA) data. </p> <p>5. The accuracy of this collection has been well validated by the in-situ snow depth observations </p> <p>and Landsat OLI NDSI maps. The detailed information can be found in the published paper.</p> <p>6. The NDSI value ranges from 0, 1-100. The value of water is set to 255. The fill value is set to 127.</p> <p>7. Disclaimer: The spatial scope of the dataset is for research convenience. Please refer to official</p> <p>release for a standard map of China, http://211.159.149.56/index.html.</p> <p>8. The new version includes data from 2001 to 2020, while the old version includes data from</p> <p>2012 to 2020 (https://doi.org/10.5281/zenodo.5644386).</p> <p>9. In case any questions arise, please do not hesitate to contact us (yhjing@whu.edu.cn).</p>
TimeSpec4LULC: A Smart-Global Dataset of Multi-Spectral Time Series of MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to perform LULC Mapping
<p>TimeSpec4LULC is a smart open-source global dataset of multi-spectral time series for 29 Land Use and Land Cover (LULC) classes ready to train machine learning models. It was built based on the seven spectral bands of the MODIS sensors at 500 m resolution from 2000 to 2021 (262 observations in each time series). Then, was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE).</p> <p>TimeSpec4LULC contains two datasets: the original dataset distributed over 6,076,531 pixels, and the balanced subset of the original dataset distributed over 29000 pixels.</p> <p>The original dataset contains 30 folders, namely "Metadata", and 29 folders corresponding to the 29 LULC classes. The folder "Metadata" holds 29 different CSV files describing the metadata of the 29 LULC classes. The remaining 29 folders contain the time series data for the 29 LULC classes. Each folder holds 262 CSV files corresponding to the 262 months. Inside each CSV file, we provide the seven values of the spectral bands as well as the coordinates for all the LULC class-related pixels.</p> <p>The balanced subset of the original dataset contains the metadata and the time series data for 1000 pixels per class representative of the globe. It holds 29 different JSON files following the names of the 29 LULC classes.</p> <p>The features of the dataset are:</p> <p>- ".geo": the geometry and coordinates (longitude and latitude) of the pixel center.</p> <p>- "ADM0_Code": the GAUL country code.</p> <p>- "ADM1_Code": the GAUL first-level administrative unit code.</p> <p>- GHM_Index": the average of the global human modification index.</p> <p>- "Products_Agreement_Percentage": the agreement percentage over the 15 global LULC products available in GEE.</p> <p>- "Temporal_Availability_Percentage": the percentage of non-missing values in each band.</p> <p>- "Pixel_TS": the time series values of the seven spectral bands.</p>
Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots
<p><strong>Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots</strong></p> <p>Fire regimes are areas having similar fire characteristics, and show the spatial pattern, frequency and intensity of fires that prevail in that area over long periods of time. Fire regimes are created and maintained by multivariate interactions between climate, vegetation/fuels, and ignitions. Like ecoregions, fire regimes indicate the extent and overlap of particular vegetative/fuel communities and climatic conditions, and are important for understanding, monitoring, predicting and managing fire.</p> <p>More than 83M MODIS “hotspot” thermal detections from 2002-2019 were grouped into 10km cells, and 21 derived variables describing fire characteristics of fire intensity, return frequency, and seasonality within each cell were developed and subjected to unsupervised Multivariate Geographic Clustering to produce world maps of Global Fire Regimes (GFRs), each having similar fire intensity and timing characteristics.</p> <p>Methodology behind these datasets are described in manuscript currently in review.</p> <p><strong>W. W. Hargrove, Jitendra Kumar, Steven P. Norman, Forrest M. Hoffman (2022), "Empirical Characterization of Global Fire Regimes Show Shared Fire Relationships" 2022 (in review)</strong></p> <p>This data collection includes:</p> <p>1. Multivariate Geographic Clustering Global Fire Regimes at 3000, 1000, 500, 100, 50, 20, 10 levels of divisions in form of geospatial raster in IMG formats, and associated color tables.</p> <p>2. Characteristics of GFRs</p> <p>3. Location groups</p> <p>4. Geospatial maps of global fire frequency modes, global seasonality strength, and 12 types of global fires.</p> <p>5. PNG maps for all data products </p> <p>6. Description and script for global date transform algorithm.</p>
FABIAN: a daily product of Fractional Austral-summer Blue Ice over ANtarctica during 2000-2021 based on MODIS imagery using Google Earth Engine
<p>This is a supplementary data set for FABIAN: a daily product of Fractional Austral-summer Blue Ice over ANtarctica during<br> 2000{2021 based on MODIS imagery using Google Earth Engine</p> <p>The files include:</p> <p>(1) Snow spectra modelled by TARTES, a two-stream radiative transfer model for light in snow (Libois et al., 2013).</p> <p>(2) Spectra extracted from MODIS, using AUTO-EM.</p> <p>(3) Endmember selection results, including ESS, EMC, and AMUSES.</p> <p>The other hyperspectral data from field measurements can be acquired by contact the original authors.</p> <p>Libois, Q., Picard, G., France, J., Arnaud, L., Dumont, M., Carmagnola, C., King, M., 2013. Influence of grain shape on light penetration in snow. The Cryosphere 7, 1803-1818.</p>
MODIS Sea ice leads detections using a U-Net
<p>Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2002 for the season between November through April.</p>
Sentinel-3 SLSTR and MODIS satellite images of Raikoke 2019 and Eyjafjallajökull 2010 eruptions
<p>Dataset used for the study presented in the paper "Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case" (Petracca, I., De Santis, D., Picchiani, M., Corradini, S., Guerrieri, L., Prata, F., Merucci, L., Stelitano, D., Del Frate, F., Salvucci, G., and Schiavon, G.: Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-173, in review, 2022.).<br> </p> <p> </p>
Global monthly percentage of vegetation cover (MODIS FCover MODV1B product: Eurasia, Africa, Oceania)
<p>Monthly Global FCover product generated from MODIS data. Dataset represent monthly gap-filled FCover estimates the period 2000-2015 over Eurasia, Africa and Oceania. FCover was estimated using linear spectral mixture analysis and interpolated using empirical orthogonal functions algorithm to take advantage of all non-missing available pixels in both the spatial and temporal dimensions to gap-fill missing satellite observations. The global product of vegetation cover (as percentage of cover) based on MODIS images with monthly variation can be used as a critical support for several indicators related to ecologically based modelling.</p>
A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003–2017
<p>The LSTC dataset contains land surface temperature data in China (about 9.6 million square kilometers of land) during the period of 2003-2017, in monthly temporal and 5600 m spatial resolution. It combines MODIS daily data, monthly data and meteorological station data to reconstruct the true LST under cloud coverage, and then the data performance is further improved by establishing a regression analysis model. The accuracy analysis shows that the reconstruction result is closely correlated with the in-situ measurements, with an average RMSE is 1.39 °C, an average MAE of 1.30 ° C and an R<sup>2</sup> of 0.97. The dataset can be used for the spatiotemporal evaluation of LST and will be useful for high temperature and drought studies and food security.</p>
Runoff from Greenland's Firn Area - Why do MODIS, RCMs and a Firn Model disagree? - Code and Data
<h1>README – Overview code and data for obtaining MODIS runoff limits and comparison to and between MAR and RACMO / IMAU-FDM</h1> <p> </p> <h2>Basic workflow</h2> <p>Table 1 lists the projects, the pieces of code in the projects and the key output of each piece of code. This serves at illustrating the basic workflow. More detailed information on required input is provided below. The README files of the various projects provide details on how to use the code.</p> <p> </p> <p><strong><em>Table 1:</em></strong><em> This <strong>table might not display properly, please refer to the file _README_overview.pdf.</strong> General project overview. The projects and code are shown in the order they are intended to be used. In italic are parts of code that were not used for Machguth et al. (in review) but instead for Machguth et al. (2022). They might not be fully compatible anymore with runoff limits Y<sub>r</sub> calculated along flowline-polygons.</em></p> <table> <tbody> <tr> <td> <p><strong>Project</strong></p> </td> <td> <p><strong>Code</strong></p> </td> <td> <p><strong>Output</strong></p> </td> </tr> <tr> <td> <p>flowlines</p> </td> <td> <p>crop_gdalwarp.py</p> </td> <td> <p>DEM, cropped and reprojected to required size and grid (identical to the MODIS files)</p> </td> </tr> <tr> <td> <p>flowline_seedpoints.py</p> </td> <td> <p>Seedpoints to calculate flowlines</p> </td> </tr> <tr> <td> <p>flowline.py</p> </td> <td> <p>Flowlines and flowline- polygons</p> </td> </tr> <tr> <td> <p>MODIS_Greenland</p> </td> <td> <p>MODIS_array_filter.py</p> </td> <td> <p>Daily MODIS grids filtered for outliers</p> </td> </tr> <tr> <td> <p>MODIS_stddev_spatial.py</p> </td> <td> <p>Daily grids of MODIS spatial standard deviation</p> </td> </tr> <tr> <td> <p>MODIS_mean_stddev.py</p> </td> <td> <p>Greenland-wide map of background MODIS spatial standard deviation</p> </td> </tr> <tr> <td> <p>MODIS_NDWI_tiff_to_nc.py</p> </td> <td> <p>MODIS NDWI converted from tiff to netCDF</p> </td> </tr> <tr> <td> <p>MODIS_find_slush_limit.py</p> </td> <td> <p>Table of all detected daily <em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>Greenland_RCM_analysis_prep</p> </td> <td> <p>modis_vs_mar.py</p> </td> <td> <p>Tables of daily MAR <em>Y<sub>r</sub></em> for all flowline-polygons</p> </td> </tr> <tr> <td> <p>modis_vs_racmo.py</p> </td> <td> <p>Table of daily RACMO <em>Y<sub>r</sub></em> for all flowline-polygons</p> </td> </tr> <tr> <td> <p>MODIS_Greenland_analysis</p> </td> <td> <p>MODIS_A_SLmax.py</p> </td> <td> <p>Calculates annual maxima of the Yr, written into table. C<em>reates plots visualizing MODIS Y<sub>r</sub> spatial and temporal distribution – only used in Machguth et al. (2022)</em></p> </td> </tr> <tr> <td> <p><em>MODIS_A_SLanalyis.py</em></p> </td> <td> <p><em>Plots that visualize progression and forcing behind MODIS Y<sub>r</sub> – only used in Machguth et al. (2022)</em></p> </td> </tr> <tr> <td> <p>MODIS_comp_RCM_maxYs.py</p> </td> <td> <p>For all of Greenland: Plots that compare RCM and MODIS max<em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>MODIS_comp_RCM_Ys.py</p> </td> <td> <p>For all of Greenland: Plots that compare RCM and MODIS daily <em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>Greenland_RCM_analysis</p> </td> <td> <p>RCM_analysis_comp_Ktransect.py</p> </td> <td> <p>For the K-transect: Plots/tables that compare RACMO/FDM and MAR Yr as well as various RCM parameters</p> </td> </tr> <tr> <td> <p>Jupyter notebooks</p> </td> <td> <p>K-transect_MAR_Greenland-wide_to_K-transect.ipynb</p> </td> <td> <p>Writes MAR .nc files that contain data only for the K-Transect</p> </td> </tr> <tr> <td> <p>K-transect_RACMO-MAR_depth-data-plot.ipynb</p> </td> <td> <p>For the K-transect: Plots to compare depth information of MAR and RACMO/FDM</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <h2>Input and output data</h2> <p>Unless mentioned otherwise, all input data required is provided in this repository. Input files for one piece of code are at the same time the output of a piece of code that needs to be run before.</p> <p><em>Note 1:</em> due to space constraints only the so called “test” data set for runoff limit detection is provided. The full input data to Greenland-wide runoff limit detection are too large in volume but can be obtained from the authors.</p> <p>Note 2: While we only provide the test data for the MODIS runoff limit detection, we provide the full output of the Greenland-wide runoff limit detection (these data are small in volume).</p> <h3>Input to flowlines/crop_gdalwarp.py</h3> <p>arcticdem_mosaic_100m_v30_greenland_icesheet_geoidCorr.tif</p> <h3> </h3> <h3>Input to <em>flowlines/flowline_seedpoints.py</em></h3> <p>seedline_v2.1.shp</p> <h3> </h3> <h3>Input to <em>flowlines/flowline.py</em></h3> <p>è In the case of using the test data, no seed file exists. Do not provide one, seed points will be calculated along a north-south line as specified.</p> <p>greenland_vel_mosaic200_2015-2018_vx_v02-composite-crop.tif</p> <p>greenland_vel_mosaic200_2015-2018_vy_v02-composite-crop.tif</p> <p>dem_test_gapfilled.tif</p> <p><em>or for Greenland-wide:</em></p> <p>greenland_vel_mosaic500_2015-2018_vx_v02-composite-crop.tif</p> <p>greenland_vel_mosaic500_2015-2018_vy_v02-composite-crop.tif</p> <p>arcticdem_mosaic_500m_v30_greenland_icesheet_geoidCorr_GapFilled.tif</p> <p>seedpoints_v3.4.shp</p> <h3> </h3> <h3>Input to <em>MODIS_Greenland/MODIS_array_filter.py</em></h3> <p>/sat_modis_proc_GR_l1test/*</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_stddev_spatial.py</em></h3> <p>/sat_modis_proc_test_l2/*</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_mean_stddev.py</em></h3> <p>/sat_modis_proc_test_l3/*</p> <p>dem_test_gapfilled.tif</p> <p> </p> <h3>Input to <em>MODIS_Greenland/ MODIS_NDWI_tiff_to_nc.py</em></h3> <p>è Input data were computed directly on Google Earth Engine, they have not been preserved, only output data exist</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_find_slush_limit.py</em></h3> <p>/sat_modis_proc_test_l3/*</p> <p>/sat_modis_proc_test_l2_NDWI/*</p> <p>mask_greenland_icesheet/dem_test_gapfilled.tif</p> <p>Ys_polygons__test_W20km.shp</p> <p>flowlines__test_W20km.shp</p> <p>test_MOD10A1.l3.v4_yrs2000-2021_doy126-136_stddev_median.tif</p> <p> </p> <h3>Input to <em>Greenland_RCM_analysis_prep/modis_vs_mar.py</em></h3> <p>/flash/tedstona/MARv.HorstRCMStudy_20240624/*</p> <p>what is the DEM, probably the same as used elsewhere?</p> <p>flowline Polygons</p> <p>/flash/tedstona/_list_PolyIDs.xlsx</p> <p>Why output still to 'MAR-v3.12.1-rlim-slush.nc'? Probably simply not changed without any effect?</p> <h3> </h3> <h3>Input to <em>Greenland_RCM_analysis_prep/modis_vs_racmo.py</em></h3> <p>/flash/tedstona/RACMO/1km/runoff/*</p> <p>arcticdem_mosaic_500m_v30_greenland_icesheet_GeoidCorr_GapFilled_RACMO1km.tif</p> <p>racmo_polys.nc</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_A_SLmax.py</em></h3> <p>_test_slush-limit_output_table.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>_GR_slush-limit_output_table.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_A_SLanalyis.py</em></h3> <p>KAN_U_hourly_v3_fewer_columns.xlsx</p> <p>KAN_M_hourly_v3_fewer_columns.xlsx</p> <p>(selected_SL_years_and_stripes_20km.xlsx: <em>not found and was also only used in Machguth et al., 2022)</em></p> <p>__test_table_complete_annual_max_SL.xlsx</p> <p>__test_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p>__GR_test_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_comp_RCM_maxYs.py</em></h3> <p>è This file has not been tested whether it also works with the “test” data</p> <p>RACMO2.3p2_ERA5_3h_FGRN055.1km-rlim-RUa1mm.xlsx</p> <p>MAR-v.20240624-rlim-RUa1mm.xlsx</p> <p>_stripes_with_aquifers.xlsx</p> <p>__test_table_simple_annual_max_SL.xlsx</p> <p>__test_table_complete_annual_max_SL.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>__GR_table_simple_annual_max_SL.xlsx</p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_comp_RCM_Ys.py</em></h3> <p>è This file has not been tested whether it also works with the “test” data</p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p>flowlines_daily_rlims_RACMO_1mmEvents_10mmAnnual_2000_2021.xlsx</p> <p>flowlines_daily_rlims_MAR-v.20240624_1mmEvents_10mmAnnual_2000_2021.xlsx</p> <p> </p> <h3>Input to <em>Greenland_RCM_analysis/RCM_analysis_comp_Ktransect.py</em></h3> <p>è Certain data sets (indicated below) are not provided as they would exceed the available space in the repository. They can be obtained at no conditions from the authors</p> <p>è This file has not been tested whether it would also works with the “test” data</p> <p><em>Too large: /FDM_Greenland-wide/* </em></p> <p><em>Too large: /RACMO-FDM_K-Transect_updated/* </em></p> <p><em>Too large: /RACMO_Greenland-wide/* </em></p> <p><em>Too large: /MARv3.14_K-transect_continuous/*</em></p> <p><em>Too large: FGRN055_Masks.nc</em></p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p>__GR_table_simple_annual_max_SL.xlsx</p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p> </p> <h3>Input to <em>_notebooks\K-transect_MAR_Greenland-wide_to_K-transect.ipynb</em></h3> <p> </p> <h3>Input to <em>_notebooks\K-transect_RACMO-MAR_depth-data-plot.ipynb</em></h3> <p> </p> <h2>Output data</h2> <p>The basic output files of the runoff limit detection for all of Greenland are:</p> <p>_GR_slush-limit_output_table.xlsx (written by MODIS_Greenland/MODIS_find_slush_limit.py)</p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__GR_table_simple_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__GR_table_complete_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p> </p> <p>The basic output files of the runoff limit detection for the “test” data set are:</p> <p>_test_slush-limit_output_table.xlsx (written by MODIS_Greenland/MODIS_find_slush_limit.py)</p> <p>__test_slush-limit_output_table_OnlyValidEntries.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__test_table_simple_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__test_table_complete_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p> </p> <p> </p> <p> </p>
Normalized Difference Vegetation Index for Andalusia Region based on MODIS
<p><strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. </p> <p>NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation.</p> <p><br> Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Grape Wine sector, WP4- Durum wheat pasta sector)</p> <p>AoI: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_ndvi</p> <p> </p>
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