Forest Biomass Estimation of Linpan in Western Sichuan Using Multi-Source Remote Sensing Article Swipe
Jiaming Lai
,
Yuxuan Lin
,
Yan Lu
,
Minghao Yue
,
Gang Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/su17177855
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/su17177855
Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha−1, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R2 = 0.768), whereas the SVM was more suitable for bamboo Linpan (R2 = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su17177855
- https://www.mdpi.com/2071-1050/17/17/7855/pdf?version=1756636938
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413922452
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4413922452Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/su17177855Digital Object Identifier
- Title
-
Forest Biomass Estimation of Linpan in Western Sichuan Using Multi-Source Remote SensingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-31Full publication date if available
- Authors
-
Jiaming Lai, Yuxuan Lin, Yan Lu, Minghao Yue, Gang ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/su17177855Publisher landing page
- PDF URL
-
https://www.mdpi.com/2071-1050/17/17/7855/pdf?version=1756636938Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2071-1050/17/17/7855/pdf?version=1756636938Direct OA link when available
- Concepts
-
Biomass (ecology), Estimation, Remote sensing, Environmental science, Forestry, Geography, Geology, Engineering, Systems engineering, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4413922452 |
|---|---|
| doi | https://doi.org/10.3390/su17177855 |
| ids.doi | https://doi.org/10.3390/su17177855 |
| ids.openalex | https://openalex.org/W4413922452 |
| fwci | 0.0 |
| type | article |
| title | Forest Biomass Estimation of Linpan in Western Sichuan Using Multi-Source Remote Sensing |
| awards[0].id | https://openalex.org/G3181545694 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 32201281 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 17 |
| biblio.volume | 17 |
| biblio.last_page | 7855 |
| biblio.first_page | 7855 |
| topics[0].id | https://openalex.org/T10111 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9980000257492065 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2303 |
| topics[0].subfield.display_name | Ecology |
| topics[0].display_name | Remote Sensing in Agriculture |
| topics[1].id | https://openalex.org/T11164 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9962999820709229 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Remote Sensing and LiDAR Applications |
| topics[2].id | https://openalex.org/T11880 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9915000200271606 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2309 |
| topics[2].subfield.display_name | Nature and Landscape Conservation |
| topics[2].display_name | Forest ecology and management |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C115540264 |
| concepts[0].level | 2 |
| concepts[0].score | 0.683395504951477 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2945560 |
| concepts[0].display_name | Biomass (ecology) |
| concepts[1].id | https://openalex.org/C96250715 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6781858205795288 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q965330 |
| concepts[1].display_name | Estimation |
| concepts[2].id | https://openalex.org/C62649853 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6510225534439087 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[2].display_name | Remote sensing |
| concepts[3].id | https://openalex.org/C39432304 |
| concepts[3].level | 0 |
| concepts[3].score | 0.5950530171394348 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[3].display_name | Environmental science |
| concepts[4].id | https://openalex.org/C97137747 |
| concepts[4].level | 1 |
| concepts[4].score | 0.370266318321228 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q38112 |
| concepts[4].display_name | Forestry |
| concepts[5].id | https://openalex.org/C205649164 |
| concepts[5].level | 0 |
| concepts[5].score | 0.2958067059516907 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[5].display_name | Geography |
| concepts[6].id | https://openalex.org/C127313418 |
| concepts[6].level | 0 |
| concepts[6].score | 0.19079440832138062 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[6].display_name | Geology |
| concepts[7].id | https://openalex.org/C127413603 |
| concepts[7].level | 0 |
| concepts[7].score | 0.11194416880607605 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[7].display_name | Engineering |
| concepts[8].id | https://openalex.org/C201995342 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q682496 |
| concepts[8].display_name | Systems engineering |
| concepts[9].id | https://openalex.org/C111368507 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q43518 |
| concepts[9].display_name | Oceanography |
| keywords[0].id | https://openalex.org/keywords/biomass |
| keywords[0].score | 0.683395504951477 |
| keywords[0].display_name | Biomass (ecology) |
| keywords[1].id | https://openalex.org/keywords/estimation |
| keywords[1].score | 0.6781858205795288 |
| keywords[1].display_name | Estimation |
| keywords[2].id | https://openalex.org/keywords/remote-sensing |
| keywords[2].score | 0.6510225534439087 |
| keywords[2].display_name | Remote sensing |
| keywords[3].id | https://openalex.org/keywords/environmental-science |
| keywords[3].score | 0.5950530171394348 |
| keywords[3].display_name | Environmental science |
| keywords[4].id | https://openalex.org/keywords/forestry |
| keywords[4].score | 0.370266318321228 |
| keywords[4].display_name | Forestry |
| keywords[5].id | https://openalex.org/keywords/geography |
| keywords[5].score | 0.2958067059516907 |
| keywords[5].display_name | Geography |
| keywords[6].id | https://openalex.org/keywords/geology |
| keywords[6].score | 0.19079440832138062 |
| keywords[6].display_name | Geology |
| keywords[7].id | https://openalex.org/keywords/engineering |
| keywords[7].score | 0.11194416880607605 |
| keywords[7].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.3390/su17177855 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S10134376 |
| locations[0].source.issn | 2071-1050 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2071-1050 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Sustainability |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2071-1050/17/17/7855/pdf?version=1756636938 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sustainability |
| locations[0].landing_page_url | https://doi.org/10.3390/su17177855 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5012120871 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7028-9979 |
| authorships[0].author.display_name | Jiaming Lai |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].raw_affiliation_string | Forest Ecology and Conservation in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province, Chengdu 611130, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I37574244 |
| authorships[0].affiliations[1].raw_affiliation_string | College of Forestry, Sichuan Agricultural University, Chengdu 611130, China |
| authorships[0].institutions[0].id | https://openalex.org/I37574244 |
| authorships[0].institutions[0].ror | https://ror.org/0388c3403 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I37574244 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Sichuan Agricultural University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiaming Lai |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Forestry, Sichuan Agricultural University, Chengdu 611130, China, Forest Ecology and Conservation in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province, Chengdu 611130, China |
| authorships[1].author.id | https://openalex.org/A5083027719 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0638-2620 |
| authorships[1].author.display_name | Yuxuan Lin |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I37574244 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Forestry, Sichuan Agricultural University, Chengdu 611130, China |
| authorships[1].institutions[0].id | https://openalex.org/I37574244 |
| authorships[1].institutions[0].ror | https://ror.org/0388c3403 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I37574244 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Sichuan Agricultural University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yuxuan Lin |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | College of Forestry, Sichuan Agricultural University, Chengdu 611130, China |
| authorships[2].author.id | https://openalex.org/A5080449172 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9420-7090 |
| authorships[2].author.display_name | Yan Lu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I37574244 |
| authorships[2].affiliations[0].raw_affiliation_string | College of Forestry, Sichuan Agricultural University, Chengdu 611130, China |
| authorships[2].institutions[0].id | https://openalex.org/I37574244 |
| authorships[2].institutions[0].ror | https://ror.org/0388c3403 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I37574244 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Sichuan Agricultural University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yan Lu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | College of Forestry, Sichuan Agricultural University, Chengdu 611130, China |
| authorships[3].author.id | https://openalex.org/A5065116246 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-5367-8021 |
| authorships[3].author.display_name | Minghao Yue |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210118955 |
| authorships[3].affiliations[0].raw_affiliation_string | Sichuan Forestry and Grassland Survey and Planning Institute (Sichuan Forestry and Grassland Ecological Environment Monitoring Center), Chengdu 610084, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210118955 |
| authorships[3].institutions[0].ror | https://ror.org/02bfkc760 |
| authorships[3].institutions[0].type | facility |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210118955 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Sichuan Academy of Forestry |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Mingdi Yue |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Sichuan Forestry and Grassland Survey and Planning Institute (Sichuan Forestry and Grassland Ecological Environment Monitoring Center), Chengdu 610084, China |
| authorships[4].author.id | https://openalex.org/A5100636769 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-7844-9025 |
| authorships[4].author.display_name | Gang Chen |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210118955 |
| authorships[4].affiliations[0].raw_affiliation_string | Sichuan Mt. Emei Forest Ecosystem National Observation and Research Station, Chengdu 611130, China |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I37574244 |
| authorships[4].affiliations[1].raw_affiliation_string | College of Forestry, Sichuan Agricultural University, Chengdu 611130, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210118955 |
| authorships[4].institutions[0].ror | https://ror.org/02bfkc760 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210118955 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Sichuan Academy of Forestry |
| authorships[4].institutions[1].id | https://openalex.org/I37574244 |
| authorships[4].institutions[1].ror | https://ror.org/0388c3403 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I37574244 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | Sichuan Agricultural University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Gang Chen |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | College of Forestry, Sichuan Agricultural University, Chengdu 611130, China, Sichuan Mt. Emei Forest Ecosystem National Observation and Research Station, Chengdu 611130, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2071-1050/17/17/7855/pdf?version=1756636938 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Forest Biomass Estimation of Linpan in Western Sichuan Using Multi-Source Remote Sensing |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10111 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9980000257492065 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2303 |
| primary_topic.subfield.display_name | Ecology |
| primary_topic.display_name | Remote Sensing in Agriculture |
| related_works | https://openalex.org/W2121524756, https://openalex.org/W2576994247, https://openalex.org/W2608353378, https://openalex.org/W4249206767, https://openalex.org/W782553550, https://openalex.org/W1987967678, https://openalex.org/W2563559453, https://openalex.org/W2382330008, https://openalex.org/W613780472, https://openalex.org/W4313466491 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.3390/su17177855 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S10134376 |
| best_oa_location.source.issn | 2071-1050 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2071-1050 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Sustainability |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2071-1050/17/17/7855/pdf?version=1756636938 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sustainability |
| best_oa_location.landing_page_url | https://doi.org/10.3390/su17177855 |
| primary_location.id | doi:10.3390/su17177855 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S10134376 |
| primary_location.source.issn | 2071-1050 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2071-1050 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Sustainability |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2071-1050/17/17/7855/pdf?version=1756636938 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sustainability |
| primary_location.landing_page_url | https://doi.org/10.3390/su17177855 |
| publication_date | 2025-08-31 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2960342348, https://openalex.org/W4311364161, https://openalex.org/W3025252400, https://openalex.org/W3183784132, https://openalex.org/W2109631166, https://openalex.org/W1992426417, https://openalex.org/W2101888226, https://openalex.org/W4408076418, https://openalex.org/W4313443393, https://openalex.org/W1991933965, https://openalex.org/W2044465660, https://openalex.org/W2062493000, https://openalex.org/W2969675905, https://openalex.org/W4392241927, https://openalex.org/W4404819545, https://openalex.org/W2008056655, https://openalex.org/W4392952137, https://openalex.org/W4401646802, https://openalex.org/W4388194683, https://openalex.org/W4407779571, https://openalex.org/W4410898539, https://openalex.org/W3007304305, https://openalex.org/W1977761893, https://openalex.org/W2124837560, https://openalex.org/W2000872026, https://openalex.org/W2061427081, https://openalex.org/W2008674189, https://openalex.org/W2171747502, https://openalex.org/W4410005132, https://openalex.org/W4399389944, https://openalex.org/W1978642993, https://openalex.org/W4200170805, https://openalex.org/W2012519352, https://openalex.org/W4214533736, https://openalex.org/W4296095919, https://openalex.org/W4291121437, https://openalex.org/W2362862017, https://openalex.org/W2423967844, https://openalex.org/W2967947583 |
| referenced_works_count | 39 |
| abstract_inverted_index.= | 279, 291 |
| abstract_inverted_index.a | 234, 252, 309 |
| abstract_inverted_index.t | 257 |
| abstract_inverted_index.19 | 152 |
| abstract_inverted_index.RF | 270 |
| abstract_inverted_index.VH | 207 |
| abstract_inverted_index.an | 109 |
| abstract_inverted_index.as | 146 |
| abstract_inverted_index.by | 34, 57, 148, 239, 260 |
| abstract_inverted_index.in | 43, 101, 242, 248, 275 |
| abstract_inverted_index.is | 73 |
| abstract_inverted_index.of | 40, 51, 61, 70, 78, 105, 111, 129, 255 |
| abstract_inverted_index.on | 74, 97 |
| abstract_inverted_index.to | 3, 9, 30, 137, 183, 312 |
| abstract_inverted_index.(1) | 201 |
| abstract_inverted_index.(2) | 231 |
| abstract_inverted_index.(3) | 268 |
| abstract_inverted_index.(R2 | 278, 290 |
| abstract_inverted_index.SVM | 283 |
| abstract_inverted_index.The | 68, 195, 269, 293 |
| abstract_inverted_index.and | 12, 38, 63, 90, 116, 165, 178, 192, 205, 217, 245, 265 |
| abstract_inverted_index.are | 7 |
| abstract_inverted_index.for | 19, 120, 287 |
| abstract_inverted_index.gap | 33 |
| abstract_inverted_index.has | 22 |
| abstract_inverted_index.key | 153, 196 |
| abstract_inverted_index.not | 23 |
| abstract_inverted_index.the | 36, 49, 59, 75, 81, 86, 94, 98, 102, 127, 143, 199, 211, 243, 249, 282 |
| abstract_inverted_index.was | 135, 284 |
| abstract_inverted_index.GF-2 | 117 |
| abstract_inverted_index.SAR, | 113 |
| abstract_inverted_index.This | 27 |
| abstract_inverted_index.band | 159 |
| abstract_inverted_index.been | 24 |
| abstract_inverted_index.data | 119, 204, 301 |
| abstract_inverted_index.fill | 31 |
| abstract_inverted_index.mean | 253 |
| abstract_inverted_index.more | 285 |
| abstract_inverted_index.that | 296 |
| abstract_inverted_index.this | 32, 71 |
| abstract_inverted_index.tree | 82, 262 |
| abstract_inverted_index.were | 156, 220 |
| abstract_inverted_index.with | 142, 214, 229, 251 |
| abstract_inverted_index.data, | 131 |
| abstract_inverted_index.data. | 67 |
| abstract_inverted_index.field | 149 |
| abstract_inverted_index.focus | 69 |
| abstract_inverted_index.lower | 246 |
| abstract_inverted_index.model | 184, 271 |
| abstract_inverted_index.radar | 64 |
| abstract_inverted_index.seeks | 29 |
| abstract_inverted_index.south | 244 |
| abstract_inverted_index.study | 28 |
| abstract_inverted_index.these | 20, 44, 130 |
| abstract_inverted_index.three | 169 |
| abstract_inverted_index.value | 254 |
| abstract_inverted_index.vital | 46 |
| abstract_inverted_index.which | 84 |
| abstract_inverted_index.while | 223 |
| abstract_inverted_index.(MSR), | 173 |
| abstract_inverted_index.(SVM), | 177 |
| abstract_inverted_index.161.97 | 256 |
| abstract_inverted_index.China, | 6 |
| abstract_inverted_index.Linpan | 0, 99, 277, 289, 304 |
| abstract_inverted_index.across | 186 |
| abstract_inverted_index.bamboo | 193, 288 |
| abstract_inverted_index.carbon | 13 |
| abstract_inverted_index.driven | 259 |
| abstract_inverted_index.during | 210 |
| abstract_inverted_index.forest | 76, 144, 180 |
| abstract_inverted_index.higher | 240 |
| abstract_inverted_index.layer, | 83 |
| abstract_inverted_index.marked | 235 |
| abstract_inverted_index.north, | 250 |
| abstract_inverted_index.random | 179 |
| abstract_inverted_index.remote | 53, 65, 122, 299 |
| abstract_inverted_index.robust | 310 |
| abstract_inverted_index.roots. | 92 |
| abstract_inverted_index.trunk, | 87 |
| abstract_inverted_index.unique | 227 |
| abstract_inverted_index.values | 241, 247 |
| abstract_inverted_index.vector | 175 |
| abstract_inverted_index.0.768), | 280 |
| abstract_inverted_index.0.892). | 292 |
| abstract_inverted_index.Biomass | 232 |
| abstract_inverted_index.Linpan, | 79 |
| abstract_inverted_index.Linpan. | 194 |
| abstract_inverted_index.Pearson | 132 |
| abstract_inverted_index.Through | 126 |
| abstract_inverted_index.biomass | 17, 41, 77, 124, 145, 185, 305 |
| abstract_inverted_index.factors | 155 |
| abstract_inverted_index.focused | 96 |
| abstract_inverted_index.ha−1, | 258 |
| abstract_inverted_index.improve | 313 |
| abstract_inverted_index.include | 198 |
| abstract_inverted_index.indices | 216, 225 |
| abstract_inverted_index.leaves, | 89 |
| abstract_inverted_index.machine | 176 |
| abstract_inverted_index.optical | 62 |
| abstract_inverted_index.optimal | 273 |
| abstract_inverted_index.sensing | 54, 66, 300 |
| abstract_inverted_index.species | 263 |
| abstract_inverted_index.summer, | 212 |
| abstract_inverted_index.support | 174 |
| abstract_inverted_index.systems | 21 |
| abstract_inverted_index.texture | 163, 218 |
| abstract_inverted_index.through | 48 |
| abstract_inverted_index.western | 4, 106 |
| abstract_inverted_index.whereas | 281 |
| abstract_inverted_index.District | 104 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.Sichuan, | 5, 107 |
| abstract_inverted_index.Wenjiang | 103 |
| abstract_inverted_index.accuracy | 37 |
| abstract_inverted_index.analysis | 134 |
| abstract_inverted_index.biomass. | 230 |
| abstract_inverted_index.combined | 213 |
| abstract_inverted_index.critical | 221 |
| abstract_inverted_index.cycling. | 14 |
| abstract_inverted_index.distinct | 2 |
| abstract_inverted_index.dominant | 261 |
| abstract_inverted_index.employed | 182 |
| abstract_inverted_index.enhances | 303 |
| abstract_inverted_index.findings | 197 |
| abstract_inverted_index.identify | 138 |
| abstract_inverted_index.includes | 85 |
| abstract_inverted_index.indices, | 162 |
| abstract_inverted_index.integral | 8 |
| abstract_inverted_index.modeling | 154 |
| abstract_inverted_index.offering | 308 |
| abstract_inverted_index.regional | 10 |
| abstract_inverted_index.research | 72, 95, 294 |
| abstract_inverted_index.spectral | 203 |
| abstract_inverted_index.stepwise | 171 |
| abstract_inverted_index.suggests | 295 |
| abstract_inverted_index.suitable | 286 |
| abstract_inverted_index.surveys. | 150 |
| abstract_inverted_index.accuracy, | 307 |
| abstract_inverted_index.branches, | 88 |
| abstract_inverted_index.conducted | 136 |
| abstract_inverted_index.deciduous | 188 |
| abstract_inverted_index.displayed | 233 |
| abstract_inverted_index.enhancing | 35 |
| abstract_inverted_index.evergreen | 190 |
| abstract_inverted_index.exhibited | 226 |
| abstract_inverted_index.features, | 164, 219 |
| abstract_inverted_index.framework | 311 |
| abstract_inverted_index.gradient, | 237 |
| abstract_inverted_index.including | 158 |
| abstract_inverted_index.precision | 39 |
| abstract_inverted_index.selected, | 157 |
| abstract_inverted_index.strengths | 60 |
| abstract_inverted_index.utilizing | 108 |
| abstract_inverted_index.variables | 139 |
| abstract_inverted_index.Sentinel-1 | 112, 206 |
| abstract_inverted_index.Sentinel-2 | 114, 202 |
| abstract_inverted_index.correlated | 141 |
| abstract_inverted_index.determined | 147 |
| abstract_inverted_index.ecosystems | 100 |
| abstract_inverted_index.estimation | 18, 42, 306, 314 |
| abstract_inverted_index.following: | 200 |
| abstract_inverted_index.intensity. | 267 |
| abstract_inverted_index.landscapes | 47 |
| abstract_inverted_index.management | 266 |
| abstract_inverted_index.mixed-type | 276 |
| abstract_inverted_index.precision. | 315 |
| abstract_inverted_index.regression | 172 |
| abstract_inverted_index.thoroughly | 25 |
| abstract_inverted_index.vegetation | 161, 215 |
| abstract_inverted_index.(RF)—were | 181 |
| abstract_inverted_index.Ultimately, | 151 |
| abstract_inverted_index.application | 50 |
| abstract_inverted_index.backscatter | 208 |
| abstract_inverted_index.correlation | 133 |
| abstract_inverted_index.ecosystems, | 1 |
| abstract_inverted_index.estimation. | 125 |
| abstract_inverted_index.integrating | 58, 297 |
| abstract_inverted_index.integration | 110 |
| abstract_inverted_index.mixed-type, | 187 |
| abstract_inverted_index.performance | 274 |
| abstract_inverted_index.predictors, | 222 |
| abstract_inverted_index.techniques, | 55 |
| abstract_inverted_index.underground | 91 |
| abstract_inverted_index.biodiversity | 11 |
| abstract_inverted_index.broadleaved, | 189, 191 |
| abstract_inverted_index.coefficients | 209 |
| abstract_inverted_index.correlations | 228 |
| abstract_inverted_index.demonstrated | 272 |
| abstract_inverted_index.distribution | 264 |
| abstract_inverted_index.ecologically | 45 |
| abstract_inverted_index.encompassing | 80 |
| abstract_inverted_index.information, | 160 |
| abstract_inverted_index.multi-source | 52, 121, 298 |
| abstract_inverted_index.phenological | 166, 224 |
| abstract_inverted_index.specifically | 56 |
| abstract_inverted_index.Specifically, | 93 |
| abstract_inverted_index.Subsequently, | 168 |
| abstract_inverted_index.characterized | 238 |
| abstract_inverted_index.comprehensive | 16 |
| abstract_inverted_index.investigated. | 26 |
| abstract_inverted_index.north–south | 236 |
| abstract_inverted_index.preprocessing | 128 |
| abstract_inverted_index.sensing-based | 123 |
| abstract_inverted_index.significantly | 140, 302 |
| abstract_inverted_index.multispectral, | 115 |
| abstract_inverted_index.high-resolution | 118 |
| abstract_inverted_index.characteristics. | 167 |
| abstract_inverted_index.algorithms—multiple | 170 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100636769 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I37574244, https://openalex.org/I4210118955 |
| citation_normalized_percentile.value | 0.37776334 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |