Find research datasets worth reusing

opencc-by-4.0Dec 2019View details →

Sentinel-2 Multitemporal Cities Pairs

This dataset contains N=1520 Sentinel-2 level 1C image pairs focused on urban areas around the world. Bands with a spatial resolution smaller than 10 m are resampled to 10 m and images are cropped to approximately 600x600 pixels. The size of some images is smaller than 600x600 pixels as result of the fact that some coordinates were located close to the edge of a Sentinel tile, the images were then cropped to the tile border. Geometric or radiometric corrections are not performed. The dataset is released with the conference article: Marrit Leenstra, Diego Marcos, Francesca Bovolo and Devis Tuia, Self-supervised pre-training enhances change detection in Sentinel-2 imagery, PRRS workshop, ICPR 2020

opencc-by-4.0Oct 2016View details →

Sequence of snow maps produced from Sentinel-2 type of observations (SPOT-5 Take 5) over the Deux Alpes and Alpe d'Huez ski resorts

This is a series of snow cover maps between April 11 and September 8, 2015 over a region that covers the Deux Alpes and Alpe d'Huez ski resorts in France. The snow maps were produced from a SPOT-5 Take 5 images using the "Let-it-snow" processor (v1.0, June 2016: http://tully.ups-tlse.fr/grizonnet/let-it-snow). The SEB folder contains 20 GeoTiff at 10 m résolution in Lambert-93 projection system coded as follows (cf. http://tully.ups-tlse.fr/grizonnet/let-it-snow#products-format): 0: No-snow 100: Snow 205: Cloud including cloud shadow 254: No data The "anim_no05jul_opt" animated gif was produced from these data after performing a simple temporal interpolation given by the following rules: <ul> <li>If a pixel masked by a cloud was marked as snow in the preceding image and in the following image, then it is reclassified as a snow pixel.</li> </ul> <ul> <li>If a pixel masked by a cloud was marked as no-snow in the preceding image and in the following image, then it is reclassified as a no-snow pixel.</li> </ul> The July 05 image was removed from the animation due to a cloud/snow confusion. This type of error should be avoided with Sentinel-2 data thanks to an additional "high cloud" test (see http://tully.ups-tlse.fr/grizonnet/let-it-snow/blob/master/doc/tex/ATBD_CES-Neige.pdf). These data were featured in this blog post : Gascoin, S. "Monitoring the snow cover in ski resorts using Sentinel-2" (19-Sep-2016) http://www.cesbio.ups-tlse.fr/multitemp/?p=8676

National-scale tree species/genera map for Poland from Sentinel-2 time series

Map of 16 dominant tree species/genera in Poland based on classification of time series of Sentinel-2 imagery. This dataset is associated with the article by Grabska-Szwagrzyk et al. (2024): <a href="https://essd.copernicus.org/articles/16/2877/2024/">Map of forest tree species for Poland based on Sentinel-2 data.</a> The map is provided as GeoTiff file. In addition, training and test data is provided in shapefile format. The map can be explored online in a <a href="https://ee-aweaksbarg.projects.earthengine.app/view/speciesmappl">webviewer</a>.

Identificação de nuvens em imagens do Sentinel-2 MSI

Dataset usado para desenvolver projeto na disciplina PTC3567 com rede neural destinada a estimar a probabilidade de presença de nuvem em cada pixel da imagem.

A spatiotemporal dataset for NDVI prediction on Sentinel-2 imagery

The dataset was designed for prediction of vegetation health on Sentinel-2 imagery and utilized in the AI4Copernicus service: "Long Short-Term Memory Neural Network for NDVI prediction" in which an LSTM neural network was trained in order to create an AI model for NDVI (Normalized Difference Vegetation Index) prediction with time-horizon of one month. The dataset needs to be enriched with more data in time dimension but currently it can be used for initial experimentation with an LSTM neural network for NDVI prediction.

A dataset for tree-crops prediction in Sentinel-2 imagery

The dataset was designed for semantic-segmentation-based deep learning models and utilized in the AI4Copernicus service: "Deep network for pixel-level classification of S2 patches" in which a U-net neural network was used in order to create an AI model for tree-crops prediction.The dataset needs to be enriched with more data. However, it can be used for initial experimentation with a U-net neural network and satellite imagery.

opencc-by-4.0Dec 2021View details →

Automatic Detection of Photovoltaic from Sentinel-2 observations by an Enhanced U-Net method - DataSet

Data Availability for Enhanced U-Net(E-UNET) Thank you for your interest in our dataset. Repository contents: Sentinel-2 L2A product:  The tiles that we selected containing photovoltaic. ImageFusion_Result: The image fusion results of ROI including photovoltaic which we intercepted from the Sentinel-2 data. It contains 10m resolution, 10m and 20m resolution, 10m, 20m and 60m resolution fusion results, and RGB 3-channels fusion results. Image: The RGB images for the ROI containing photovoltaic. Label:  The manual annotations for the ROI containing photovoltaic.

opencc-by-4.0May 2022View details →

Pixel-based forest classification of Sentinel-2 images using automatically generated datasets

Contains six training datasets, composed of 800, 1600 and 3200 images. Each training dataset made up of OSM (OpenStreetMap) masks or HRL (Copernicus pan-European High Resolution Layers). Additional 2 evaluation datasets based on OSM and HRL. Composed of 200 evaluation images. For study area lithuania_2018_06.tiff is provided. This contains a fully preprocessed study area (removed clouds, composed mosaic). We provide additionally a merged mosaic of Lithuanian HRL in lithuania_HRL.tiff file. OpenStreetMap database is not provided, it can be found at https://planet.openstreetmap.org.

Satellite-derived chlorophyll-a concentrations for Lake Mulargia (Sardinia, Italy) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery

This dataset contains satellite-derived chlorophyll-a data of Lake Mulargia (Sardinia, Italy) for the period 29 Mar. 2013 - 31 Jan. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery. Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following: <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment, 270, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. Frontiers in Remote Sensing, 1</li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604</li> </ul>

Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 MNDWI images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 MNDWI images of coasts. Based on SWED*** data https://openmldata.ukho.gov.uk/ These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. MNDWI (1 band): mndwi MNDWI = (Green - SWIR) / (Green + SWIR)    Green = pixel values from the green band    SWIR = pixel values from the short-wave infrared band Reference: Xu, H. "Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery." International Journal of Remote Sensing 27, No. 14 (2006): 3025-3033." (ESRI, 2018) Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.   2. '.h5' weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function  `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images   3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584

opencc-by-4.0Oct 2021View details →

WHUS2-CR, a thin cloud removal dataset for Sentinel-2 images

WHUS2-CR is a thin cloud removal dataset for Sentinel-2A images. WHUS2-CR contains 36 paired cloud and corresponding clear Sentinel-2A images evenly distributed over the world. Because the max storage limitation of one dataset is 50 GB, 5 files can not be uploaded on this dataset. They can be found on : <a href="https://doi.org/10.5281/zenodo.5616753">https://doi.org/10.5281/zenodo.5616753</a>. (The reported results in ref [1] are in CRMSS-result.rar, CRMSS-10-4.rar and CRMSS-4-4.rar, respectively.) The dataset reproducing code and model source code for ref [2] are on : <a href="https://github.com/Neooolee/WHUS2-CR">https://github.com/Neooolee/WHUS2-CR</a> If you use this dataset for your research, please cite us accordingly: #Reference:  [1] J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373–389, Aug. 2020, <a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>. [2] J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>. The training and testing image list used in reference [2] is (The training and testing small patches are listed in <a href="https://zenodo.org/api/files/bae57b05-f2cc-4729-b97c-eec7c215e9e4/filenos.xlsx">filenos.xlsx</a>): Training set <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160403T030602_N0201_R075_T50TMK_20160403T031209</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160413T031632_N0201_R075_T50TMK_20160413T031626</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181111T053041_N0207_R105_T43RGM_20181111T083104</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181121T053121_N0207_R105_T43RGM_20181121T091419</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160925T104022_N0204_R008_T32ULB_20160925T104115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160915T104022_N0204_R008_T32ULB_20160915T104018</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160528T153912_N0202_R011_T18TWL_20160528T154746</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160518T155142_N0202_R011_T18TWL_20160518T155138</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181208T170701_N0207_R069_T14QMG_20181208T202913</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181218T170711_N0207_R069_T14QMG_20181218T203015</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180809T190911_N0206_R056_T10UFB_20180810T002400</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180819T190911_N0206_R056_T10UFB_20180820T002955</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190306T132231_N0207_R038_T22KHV_20190306T164115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190224T132231_N0207_R038_T22KHV_20190224T164104</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181012T084901_N0206_R107_T37VCC_20181012T110218</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181022T085011_N0206_R107_T37VCC_20181022T110901</td> </tr> <tr> <td>9</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190619T023251_N0207_R103_T50JKP_20190619T071925</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190629T023251_N0207_R103_T50JKP_20190629T053618</td> </tr> <tr> <td>10</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190901T032541_N0208_R018_T47NPF_20190901T070148</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190911T032541_N0208_R018_T47NPF_20190911T084555</td> </tr> <tr> <td>11</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160419T083012_N0201_R021_T36RUU_20160419T083954</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160409T083012_N0201_R021_T36RUU_20160409T084024</td> </tr> <tr> <td>12</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190218T143751_N0207_R096_T19HCC_20190218T175945</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190208T143751_N0207_R096_T19HCC_20190208T180253</td> </tr> <tr> <td>13</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180609T061631_N0206_R034_T42TWL_20180609T081837</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180530T061631_N0206_R034_T42TWL_20180530T082050</td> </tr> <tr> <td>14</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191111T025941_N0208_R032_T49QGF_20191111T055938</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191101T025841_N0208_R032_T49QGF_20191101T054434</td> </tr> <tr> <td>15</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190818T103031_N0208_R108_T31SEA_20190818T124651</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190808T103031_N0208_R108_T31SEA_20190808T124427</td> </tr> <tr> <td>16</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191202T105421_N0208_R051_T29PPP_20191202T112025</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191212T105441_N0208_R051_T29PPP_20191212T111831</td> </tr> <tr> <td>17</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190919T074611_N0208_R135_T35JPL_20190919T105208</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190929T074711_N0208_R135_T35JPL_20190929T100745</td> </tr> <tr> <td>18</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190725T142801_N0208_R053_T20LMR_20190725T175149</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190804T142801_N0208_R053_T20LMR_20190804T175038</td> </tr> <tr> <td>19</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T043931_N0208_R033_T46TDK_20191101T074915</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191022T043831_N0208_R033_T46TDK_20191022T063301</td> </tr> <tr> <td>20</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160509T065022_N0202_R020_T41UNV_20160509T065018</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160519T064632_N0202_R020_T41UNV_20160519T064833</td> </tr> <tr> <td>21</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191019T012631_N0208_R131_T53LKF_20191019T030531</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191029T012721_N0208_R131_T53LKF_20191029T040003</td> </tr> <tr> <td>22</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190503T071621_N0207_R006_T38PMB_20190503T092340</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190423T071621_N0207_R006_T38PMB_20190423T093049</td> </tr> <tr> <td>23</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190724T011701_N0208_R031_T56VLM_20190724T031136</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190714T011701_N0208_R031_T56VLM_20190714T031656</td> </tr> <tr> <td>24</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181020T012651_N0206_R074_T54TXN_20181020T032526</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181010T012651_N0206_R074_T54TXN_20181010T055606</td> </tr> <tr> <td>25</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20170224T162331_N0204_R040_T16REV_20170224T162512</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20170214T162351_N0204_R040_T16REV_20170214T163022</td> </tr> <tr> <td>26</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190613T032541_N0207_R018_T49UFT_20190613T062257</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190623T032541_N0207_R018_T49UFT_20190623T061953</td> </tr> <tr> <td>27</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190208T011721_N0207_R088_T53KLP_20190208T024521</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190129T011721_N0207_R088_T53KLP_20190129T024501</td> </tr> <tr> <td>28</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190530T184921_N0207_R113_T12VVN_20190530T222535</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190520T184921_N0207_R113_T12VVN_20190520T222900</td> </tr> </tbody> </table> Testing set <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20150826T084006_N0204_R064_T37UCQ_20150826T084003</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20150905T083736_N0204_R064_T37UCQ_20150905T084002</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T000241_N0208_R030_T56HLH_20191101T012241</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191111T000241_N0208_R030_T56HLH_20191111T012137</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190711T174911_N0208_R141_T13TEE_20190711T212846</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190701T174911_N0207_R141_T13TEE_20190701T212910</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190201T093221_N0207_R136_T32PRR_20190201T113425</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190211T093121_N0207_R136_T32PRR_20190211T103706</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190314T021601_N0207_R003_T52SCF_20190314T055026</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190304T021601_N0207_R003_T52SCF_20190304T042035</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180804T045701_N0206_R119_T46VDH_20180804T065907</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180725T045701_N0206_R119_T46VDH_20180725T065359</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190707T213531_N0207_R086_T05VPJ_20190707T231819</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190627T213531_N0207_R086_T05VPJ_20190628T010801</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190605T125311_N0207_R052_T24MXV_20190605T160555</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190615T125311_N0207_R052_T24MXV_20190615T142536</td> </tr> </tbody> </table>

AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Geomedian Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Geomedian Composites  The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 GeoMAD Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 GeoMAD Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).