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ShareScore release 0.9.0
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2 results for “LightGBM”
MST NDSI Collection: A Cloud-free MODIS NDSI Dataset (2001–2022) for Tibetan Plateau Generated by a LightGBM-Based Method Using Multivariate Features
<p>1. Cloud-free MODIS normalized difference snow index (NDSI) dataset for Tibetan Plateau from 2001 to 2022 is generated.</p> <p>2. This dataset is derived from daily 500-m MODIS NDSI datasets (MOD10A1 and MYD10A1).</p> <p>3. This dataset is provided using a WGS_1984_UTM_45N projection, with the data format of TIFF images. </p> <p>4. The NDSI value ranges from -10,000-10,000, corresponding to the raw NDSI band of MOD10A1 and MYD10A1. The fill value is set to -32768.</p> <p>5. Due to the large amount of data, the data for each year has been divided into several split archives (YYYY.partX.rar).</p> <p>6. Each RAR contains NDSI data for one year (January to December). After uncompressing into the TIFF format, the files are named as "NDSI_Daily_YYYY_mm_dd.tif" for NDSI data and "NDSI_Daily_YYYY_mm_dd.tif".</p> <p>7. The accuracy of this collection has been well validated by simulation experiments.</p> <p>8. This version includes data from 2017 to 2022 (https://doi.org/10.5281/zenodo.14177009). Version 1 includes data from 2001 to 2004 (https://doi.org/10.5281/zenodo.14027672). Version 2 includes data from 2005 to 2010(https://doi.org/10.5281/zenodo.14038846). Version3 includes data from 2011 to 2016 (https://doi.org/10.5281/zenodo.14089925).<br><br><br></p>
Applying the LightGBM model and SHAP interpretation to understand the urban land price index and its influencing factors in China
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