Codes and Datasets for “A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing"

Codes and Datasets for “A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing"Contacts: Zhengjie Xu ([email protected]); Yan Li* ([email protected])*Correspondance: Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.Abstract:This document describes the codes and datasets for“A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing”(Xu et al. 2023 Solar Energy). The data include MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), albedo data, monthly air temperature and precipitation data from TERRACLIMATE, monthly solar radiation data from ERA5 and the Solar Farm(SF) database from Solar Wiki (wiki-solar.org). Please read this document for more details. The codes and datasets can be used freely under the CCY4.0 License. Users should cite the original paper of Xu et al. (2023) and the dataset (DOI: https://doi.org/10.6084/m9.figshare.24152766) when using it.ReferencesXu, Z., Li, Y., Qin, Y., & Bach, E. (2024). A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing. Solar Energy, 268, 112198. https://doi.org/10.1016/j.solener.2023.112198</p

Forest cover change data from 2000 to 2013 in China from multiple satellite data

<div><b>Source of remote sensing-based forest cover change datasets: </b></div><div>(1) MODIS NBR (2000-2013. modisnbr, 2000-2013)<br></div><div>(2) MODIS land cover (LC) (modislc, 2000-2012)</div><div>(3) MODIS vegetation continuous field (modisvcf, 2000-2013)</div><div>(4) Global forest change (gfc, 2000-2012)</div><div><br></div><div><b>File Description: </b></div><div>1. Figure files from the paper (7 figures). </div><div><br></div><div>2. The 5km forest gain and loss (aggregated from 500m) . </div><div>Format: netcdf. </div><div>Unit: percentage.</div><div>Example: xxxxx_entireChina_5km.nc</div><div><br></div><div>3. The 500m forest gain and loss and mainland China mask.</div><div>Format: geotiff. Unit: binary or percentage. </div><div>MODIS sinusoidal projection.</div><div>Example: xxxxx_entireChina_sinusoid_500m.zip </div><div><br></div><div>More information about the data can be found in the paper:</div><div><u>Li et al. Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions. 2017, Scientific Reports, 7, 8748, doi:10.1038/s41598-017-07732-5</u></div><div><br></div><div>Correspondence: Yan Li, [email protected]</div

Global ecosystem iso/anisohydry estimates based on QuikSCAT backscatter and AMSR-E VOD

<div><b><div><b>Data file:</b></div></b><br></div><div><b>1. fig_1_quikscat_isohydricity.nc</b><br></div><div>The QuikSCAT backscatter iso/anisohydry estimates at 0.05 degree, including both V- and H-polarizations.</div><div><br></div><div><b>2. fig_1_vod_isohydricity.nc</b><br></div><div>The AMSR-E VOD (UMT algorithm) iso/anisohydry estimates at 0.25 degree.<b><br></b></div><div><br></div><div><b>3. quikscat_vod_isohydricity_1deg.nc</b></div><div>The QuikSCAT and AMSR-E VOD (UMT algorithm) iso/anisohydry estimates at 1 degree<br></div><div><br></div><div><b>4. quikscat_vod_isohydricity_range_1deg.nc<br></b></div><div><div>Confidence interval range (95% by t-test) of the QuikSCAT and AMSR-E VOD (UMT algorithm) iso/anisohydry estimates at 1 degree.<br></div></div><div><br></div><div><b>5. vod_konings_isohydricity_1deg.nc</b></div><div>The AMSR-E VOD (LPRM algorithm) iso/anisohydry estimates at 1 degree using data from Konings and Gentine 2017 <b><br></b></div><div><br></div><div>Find more details in </div><div>Yan Li, Kaiyu Guan, Pierre Gentine, Alexandra G. Konings, Frederick C. Meinzer, John S. Kimball, Xiangtao Xu, William R. L. Anderegg, Nate G. McDowell, Jordi Martínez-Vilalta, David G. Long, Stephen P. Good. Estimating global ecosystem iso/anisohydry using active and passive microwave satellite data, Journal of Geophysical Research, Biogeosciences, 2017.</div><div><br></div><div><br></div><div><b><br></b></div><div><b><br></b></div

World population and major greenhouse gases data over the 10,000 years – Code and dataset for “Bidirectional coupling between the Earth and human systems is essential for modeling sustainability”

<p><b>Abstract: </b>The dataset includes data for the world human population, atmospheric concentrations of Carbon Dioxide (CO₂), Methane (CH₄), and Nitrous Oxide (N₂O) over the last 10,000 years compiled from various sources, and python code to plot the figure in the article "Bidirectional coupling between the Earth and human systems is essential for modeling sustainability " by Fu and Li, published in National Science Review, 2016. The codes and data can be used freely under the MIT License (see License.txt). Users are encouraged to cite the original paper of Fu and Li (2016) and cite or acknowledge this dataset (doi: 10.6084/m9.figshare.4029369).</p> <div><div><div></div></div></div

Figure file and plotting code

Figure files for the paper "Li et al 2017, Estimating global ecosystem iso/anisohydry using active and passive microwave satellite data, JGR" and plotting codes in Python.<div><br></div

Upscaling the in-situ measured iso/anisohydry of 102 species to 1-degree resolution using GBIF species occurrence and MODIS land cover data

<p>See more details in Li et al 2017 JGR.</p><p><b>Data file:</b><br></p><p><strong>1. gbif_isohydricity.nc</strong><br></p><p>The final upscaled GBIF isohydricity at 1 degree</p><p><b>2. MartinezVilalta_IsohydryDatabase_v1.xlsx</b></p><p>This is the isohydry database for 102 species from Martínez-Vilalta, J., Poyatos, R., Aguadé, D., Retana, J., Mencuccini, M., 2014. A new look at water transport regulation in plants. New Phytol. 204, 105–115. doi:10.1111/nph.12912</p><p><b>3. species_occurence_results_2fields.csv.tar.gz<br></b></p><p>The 102 species occurrence records extracted from the huge GBIF plant occurrence database (output of <i>extract_species_occurrence_record.py</i>). Each occurrence record comes with latitude and longitude information. <br></p><p><b>4. species_grid_2fields.npy.tar.gz</b></p><p>The 102 species occurrence record aggregated to 1-degree grid cell.</p><p><b>5. MODIS_LC_fraction_multiple_res.mat</b></p><p>MODIS land cover fraction (17 classes) at multiple spatial resolutions (0.05, 0.25, 0.5, 1, and 2 degree).</p><p><br></p><p><b>Code: </b></p><p><b>1. extract_species_occurrence_record.py (Python)</b></p><p>Extract occurrence records for the 102 species from GBIF data file</p><p><b>2. construct_spatial_record_land_cover.py (Python)</b></p><p>Calculate grid cell isohydricity value for each PFT group based on species occurrence. It also gives the number of record, and the number of species at the grid cell. The outputs are 3 (variables) x 7 (PFT group)=21 text files. </p><p><b>3. func_gbif_isohydricity_map.m (Matlab)</b></p><p>It utilize the text files outputed from <i>construct_spatial_record_land_cover.py to </i>calculate grid cell weighed isohydricity value based on the areal fraction of each PFT group.</p><p><br></p><p>Find more details in</p><p>Yan Li, Kaiyu Guan, Pierre Gentine, Alexandra G. Konings, Frederick C. Meinzer, John S. Kimball, Xiangtao Xu, William R. L. Anderegg, Nate G. McDowell, Jordi Martínez-Vilalta, David G. Long, Stephen P. Good. Estimating global ecosystem iso/anisohydry using active and passive microwave satellite data, Journal of Geophysical Research, Biogeosciences, 2017.</p><p><br></p

Codes and Datasets for “Potential and Actual impacts of deforestation and afforestation on land surface temperature”

<p> </p><p>The dataset includes MODIS Land Surface Temperate (LST), CRU air temperature, MODIS Land Cover (LC), and Global Forest Change GFC data. The codes are written in Matlab and Python. Users can use the codes and datasets to reproduce all results in the paper or explore new topics with their own interest. Please read the user guide document for more details. The codes and data can be used freely under the MIT License (see License.txt). Users should cite the original paper of Li et al (2016) and cite or acknowledge this dataset (DOI: 10.6084/m9.figshare.2444446) when using it. </p