Mapping Microbial Biomass Carbon Dynamics in an Arid Region Under Global Change Using a Novel Remote Sensing-Microbial Model
Fuente:
Zenodo
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Recurso digital |
| Pubblicazione: |
Zenodo
2025
|
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866901449428959232 |
|---|---|
| author | Zhang, Yifan Ma, Xu Yan, Ju Zhang, Yingjie Wang, Weihua Zhang, Fei Liu, Ying Chan, Ngai Weng Oke, Saheed Adeyinka |
| author_facet | Zhang, Yifan Ma, Xu Yan, Ju Zhang, Yingjie Wang, Weihua Zhang, Fei Liu, Ying Chan, Ngai Weng Oke, Saheed Adeyinka |
| contents | <h3><strong>1. Summary</strong></h3> <p>This dataset presents a high-resolution retrieval of <strong>Microbial Biomass Carbon (MBC)</strong> dynamics in arid and semi-arid regions (case study: Xinjiang, China). The data was generated using a novel <strong>Remote Sensing–Microbial Decomposition Process Model</strong>, which integrates satellite-derived environmental variables with a Bayesian-optimized microbial process framework. </p> <h3><strong>2. Directory Structure and Content</strong></h3> <p>The dataset is organized into two primary folders representing inputs and outputs:</p> <h4><strong>I. inputdata</strong></h4> <ul> <li><strong>observed-data</strong>: Field observation data of soil microbial biomass carbon collected from <strong>2022 to 2024</strong>.</li> <li><strong>Environmental Variables</strong>: Annual gridded datasets used to drive the model, including: <ul> <li><strong>GPP</strong> (Gross Primary Productivity)</li> <li><strong>LST</strong> (Land Surface Temperature)</li> <li><strong>PRE</strong> (Precipitation)</li> <li><strong>SM</strong> (Soil Moisture)</li> <li><strong>BD</strong> (Bulk Density)</li> <li><strong>TMP</strong> (Temperature)</li> </ul> </li> </ul> <h4><strong>II. output (Model Results & Post-processing)</strong></h4> <p>This directory is divided into two sub-folders:</p> <ul> <li><strong>output_model</strong>: Contains the year-by-year simulation results of the retrieval model.</li> </ul> <p>Each folder contains spatial raster files and attribute data (e.g., <code>.dbf</code>, <code>.shp</code>, <code>.shx</code> for SOC/MBC maps) representing the annual microbial biomass carbon distribution.</p> <ul> <li><strong>output_chuli (Post-processed Data)</strong>: Contains statistical and visual analysis results, including: <ul> <li><code>MBC_yearmean</code> & <code>SOC_yearmean</code>: Multi-year average dynamics.</li> <li><code>Five year synthetic data</code>: Aggregated datasets for trend analysis.</li> <li><code>arcgis-drawing</code>: Ready-to-use map layers for visualization.</li> <li><code>zhifangtu.m</code>: MATLAB script used for generating frequency distribution histograms.</li> </ul> </li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18093653 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Mapping Microbial Biomass Carbon Dynamics in an Arid Region Under Global Change Using a Novel Remote Sensing-Microbial Model Zhang, Yifan Ma, Xu Yan, Ju Zhang, Yingjie Wang, Weihua Zhang, Fei Liu, Ying Chan, Ngai Weng Oke, Saheed Adeyinka <h3><strong>1. Summary</strong></h3> <p>This dataset presents a high-resolution retrieval of <strong>Microbial Biomass Carbon (MBC)</strong> dynamics in arid and semi-arid regions (case study: Xinjiang, China). The data was generated using a novel <strong>Remote Sensing–Microbial Decomposition Process Model</strong>, which integrates satellite-derived environmental variables with a Bayesian-optimized microbial process framework. </p> <h3><strong>2. Directory Structure and Content</strong></h3> <p>The dataset is organized into two primary folders representing inputs and outputs:</p> <h4><strong>I. inputdata</strong></h4> <ul> <li><strong>observed-data</strong>: Field observation data of soil microbial biomass carbon collected from <strong>2022 to 2024</strong>.</li> <li><strong>Environmental Variables</strong>: Annual gridded datasets used to drive the model, including: <ul> <li><strong>GPP</strong> (Gross Primary Productivity)</li> <li><strong>LST</strong> (Land Surface Temperature)</li> <li><strong>PRE</strong> (Precipitation)</li> <li><strong>SM</strong> (Soil Moisture)</li> <li><strong>BD</strong> (Bulk Density)</li> <li><strong>TMP</strong> (Temperature)</li> </ul> </li> </ul> <h4><strong>II. output (Model Results & Post-processing)</strong></h4> <p>This directory is divided into two sub-folders:</p> <ul> <li><strong>output_model</strong>: Contains the year-by-year simulation results of the retrieval model.</li> </ul> <p>Each folder contains spatial raster files and attribute data (e.g., <code>.dbf</code>, <code>.shp</code>, <code>.shx</code> for SOC/MBC maps) representing the annual microbial biomass carbon distribution.</p> <ul> <li><strong>output_chuli (Post-processed Data)</strong>: Contains statistical and visual analysis results, including: <ul> <li><code>MBC_yearmean</code> & <code>SOC_yearmean</code>: Multi-year average dynamics.</li> <li><code>Five year synthetic data</code>: Aggregated datasets for trend analysis.</li> <li><code>arcgis-drawing</code>: Ready-to-use map layers for visualization.</li> <li><code>zhifangtu.m</code>: MATLAB script used for generating frequency distribution histograms.</li> </ul> </li> </ul> |
| title | Mapping Microbial Biomass Carbon Dynamics in an Arid Region Under Global Change Using a Novel Remote Sensing-Microbial Model |
| url | https://doi.org/10.5281/zenodo.18093653 |