Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters Article Swipe
Mohammadmehdi Saberioon
,
Vahid Khosravi
,
Jakub Brom
,
Asa Gholizadeh
,
Karl Segl
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecoinf.2023.102058
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecoinf.2023.102058
Our study investigates the capability of the environmental mapping and analysis program (EnMAP) scenes simulated using the EnMAP end-to-end simulator software (EeteS) based on the AISA Eagle airborne data to predict chlorophyll-a (Chl-a) and total suspended solids (TSS) as two of the most crucial water quality indicators. Three machine learning (ML) approaches (principal component regression(PCR), partial least square regression (PLSR) and random forest (RF)) were employed to establish links between the simulated image spectra and the above-mentioned water attributes of the samples collected from several inland water reservoirs within the southern part of the Czech Republic. Airborne hyperspectral images were also used to develop a model to compare its performance with models developed based on the simulated EnMAP data. Adequate prediction accuracy was obtained for both Chl-a (R2 = 0.89, RMSE = 43.06 μg/L, and Lin's concordance correlation coefficient (LCCC) = 0.91) and TSS (R2 = 0.91, RMSE = 17.53 mg/L, and LCCC = 0.94), which were close enough to those obtained from the airborne hyperspectral images. Chl-a and TSS correlated with the wavelengths around 550 nm and 700 to 750 nm of the red and near-infrared (NIR) regions. In addition, the spatial distribution maps derived from the simulated EnMAP were comparable to those obtained from the AISA Eagle airborne data. Overall, it can be concluded that the simulated EnMAP image successfully and reliably predicted and spatially mapped the selected biophysical properties of the small inland water bodies.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ecoinf.2023.102058
- https://ars.els-cdn.com/content/image/1-s2.0-S1574954123000870-ga1_lrg.jpg
- OA Status
- hybrid
- Cited By
- 19
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4324257321
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4324257321Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.ecoinf.2023.102058Digital Object Identifier
- Title
-
Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland watersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-13Full publication date if available
- Authors
-
Mohammadmehdi Saberioon, Vahid Khosravi, Jakub Brom, Asa Gholizadeh, Karl SeglList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ecoinf.2023.102058Publisher landing page
- PDF URL
-
https://ars.els-cdn.com/content/image/1-s2.0-S1574954123000870-ga1_lrg.jpgDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://ars.els-cdn.com/content/image/1-s2.0-S1574954123000870-ga1_lrg.jpgDirect OA link when available
- Concepts
-
Hyperspectral imaging, Remote sensing, Environmental science, Mean squared error, Correlation coefficient, Partial least squares regression, Principal component analysis, Mathematics, Statistics, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 4, 2023: 5Per-year citation counts (last 5 years)
- References (count)
-
63Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4324257321 |
|---|---|
| doi | https://doi.org/10.1016/j.ecoinf.2023.102058 |
| ids.doi | https://doi.org/10.1016/j.ecoinf.2023.102058 |
| ids.openalex | https://openalex.org/W4324257321 |
| fwci | 4.83775525 |
| type | article |
| title | Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters |
| biblio.issue | |
| biblio.volume | 75 |
| biblio.last_page | 102058 |
| biblio.first_page | 102058 |
| topics[0].id | https://openalex.org/T14249 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9922000169754028 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2311 |
| topics[0].subfield.display_name | Industrial and Manufacturing Engineering |
| topics[0].display_name | Water Quality Monitoring and Analysis |
| topics[1].id | https://openalex.org/T10032 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9790999889373779 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1910 |
| topics[1].subfield.display_name | Oceanography |
| topics[1].display_name | Marine and coastal ecosystems |
| topics[2].id | https://openalex.org/T10111 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9595000147819519 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2303 |
| topics[2].subfield.display_name | Ecology |
| topics[2].display_name | Remote Sensing in Agriculture |
| is_xpac | False |
| apc_list.value | 2510 |
| apc_list.currency | USD |
| apc_list.value_usd | 2510 |
| apc_paid.value | 2230 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2405 |
| concepts[0].id | https://openalex.org/C159078339 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9187734723091125 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q959005 |
| concepts[0].display_name | Hyperspectral imaging |
| concepts[1].id | https://openalex.org/C62649853 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6563137173652649 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[1].display_name | Remote sensing |
| concepts[2].id | https://openalex.org/C39432304 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6367806792259216 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[2].display_name | Environmental science |
| concepts[3].id | https://openalex.org/C139945424 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6208963990211487 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1940696 |
| concepts[3].display_name | Mean squared error |
| concepts[4].id | https://openalex.org/C2780092901 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5131812691688538 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3433612 |
| concepts[4].display_name | Correlation coefficient |
| concepts[5].id | https://openalex.org/C22354355 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5113963484764099 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q422009 |
| concepts[5].display_name | Partial least squares regression |
| concepts[6].id | https://openalex.org/C27438332 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4326438307762146 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2873 |
| concepts[6].display_name | Principal component analysis |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.21306216716766357 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C105795698 |
| concepts[8].level | 1 |
| concepts[8].score | 0.1487249732017517 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[8].display_name | Statistics |
| concepts[9].id | https://openalex.org/C127313418 |
| concepts[9].level | 0 |
| concepts[9].score | 0.13496732711791992 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[9].display_name | Geology |
| keywords[0].id | https://openalex.org/keywords/hyperspectral-imaging |
| keywords[0].score | 0.9187734723091125 |
| keywords[0].display_name | Hyperspectral imaging |
| keywords[1].id | https://openalex.org/keywords/remote-sensing |
| keywords[1].score | 0.6563137173652649 |
| keywords[1].display_name | Remote sensing |
| keywords[2].id | https://openalex.org/keywords/environmental-science |
| keywords[2].score | 0.6367806792259216 |
| keywords[2].display_name | Environmental science |
| keywords[3].id | https://openalex.org/keywords/mean-squared-error |
| keywords[3].score | 0.6208963990211487 |
| keywords[3].display_name | Mean squared error |
| keywords[4].id | https://openalex.org/keywords/correlation-coefficient |
| keywords[4].score | 0.5131812691688538 |
| keywords[4].display_name | Correlation coefficient |
| keywords[5].id | https://openalex.org/keywords/partial-least-squares-regression |
| keywords[5].score | 0.5113963484764099 |
| keywords[5].display_name | Partial least squares regression |
| keywords[6].id | https://openalex.org/keywords/principal-component-analysis |
| keywords[6].score | 0.4326438307762146 |
| keywords[6].display_name | Principal component analysis |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.21306216716766357 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/statistics |
| keywords[8].score | 0.1487249732017517 |
| keywords[8].display_name | Statistics |
| keywords[9].id | https://openalex.org/keywords/geology |
| keywords[9].score | 0.13496732711791992 |
| keywords[9].display_name | Geology |
| language | en |
| locations[0].id | doi:10.1016/j.ecoinf.2023.102058 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S195809937 |
| locations[0].source.issn | 1574-9541, 1878-0512 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1574-9541 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Ecological Informatics |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://ars.els-cdn.com/content/image/1-s2.0-S1574954123000870-ga1_lrg.jpg |
| 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 | Ecological Informatics |
| locations[0].landing_page_url | https://doi.org/10.1016/j.ecoinf.2023.102058 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5026895527 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1627-4957 |
| authorships[0].author.display_name | Mohammadmehdi Saberioon |
| authorships[0].countries | DE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210152878 |
| authorships[0].affiliations[0].raw_affiliation_string | Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Section |
| authorships[0].institutions[0].id | https://openalex.org/I4210152878 |
| authorships[0].institutions[0].ror | https://ror.org/04z8jg394 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I4210152878 |
| authorships[0].institutions[0].country_code | DE |
| authorships[0].institutions[0].display_name | GFZ Helmholtz Centre for Geosciences |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Mohammadmehdi Saberioon |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Section |
| authorships[1].author.id | https://openalex.org/A5032061318 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7472-199X |
| authorships[1].author.display_name | Vahid Khosravi |
| authorships[1].countries | CZ |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I205984670 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, Prague 16500, Czech Republic |
| authorships[1].institutions[0].id | https://openalex.org/I205984670 |
| authorships[1].institutions[0].ror | https://ror.org/0415vcw02 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I205984670 |
| authorships[1].institutions[0].country_code | CZ |
| authorships[1].institutions[0].display_name | Czech University of Life Sciences Prague |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Vahid Khosravi |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, Prague 16500, Czech Republic |
| authorships[2].author.id | https://openalex.org/A5000622534 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6156-346X |
| authorships[2].author.display_name | Jakub Brom |
| authorships[2].countries | CZ |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I3124161356 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Applied Ecology, Faculty of Agriculture and Technology, University of South Bohemia in C̆eské Budĕjovice, Studentská 1668, C̆eské Budĕjovice 37005, Czech Republic |
| authorships[2].institutions[0].id | https://openalex.org/I3124161356 |
| authorships[2].institutions[0].ror | https://ror.org/033n3pw66 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I3124161356 |
| authorships[2].institutions[0].country_code | CZ |
| authorships[2].institutions[0].display_name | University of South Bohemia in České Budějovice |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jakub Brom |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Applied Ecology, Faculty of Agriculture and Technology, University of South Bohemia in C̆eské Budĕjovice, Studentská 1668, C̆eské Budĕjovice 37005, Czech Republic |
| authorships[3].author.id | https://openalex.org/A5011992634 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4419-5463 |
| authorships[3].author.display_name | Asa Gholizadeh |
| authorships[3].countries | CZ |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I205984670 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, Prague 16500, Czech Republic |
| authorships[3].institutions[0].id | https://openalex.org/I205984670 |
| authorships[3].institutions[0].ror | https://ror.org/0415vcw02 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I205984670 |
| authorships[3].institutions[0].country_code | CZ |
| authorships[3].institutions[0].display_name | Czech University of Life Sciences Prague |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Asa Gholizadeh |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, Prague 16500, Czech Republic |
| authorships[4].author.id | https://openalex.org/A5023560610 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-8909-1006 |
| authorships[4].author.display_name | Karl Segl |
| authorships[4].countries | DE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210152878 |
| authorships[4].affiliations[0].raw_affiliation_string | Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Section |
| authorships[4].institutions[0].id | https://openalex.org/I4210152878 |
| authorships[4].institutions[0].ror | https://ror.org/04z8jg394 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I4210152878 |
| authorships[4].institutions[0].country_code | DE |
| authorships[4].institutions[0].display_name | GFZ Helmholtz Centre for Geosciences |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Karl Segl |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Section |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ars.els-cdn.com/content/image/1-s2.0-S1574954123000870-ga1_lrg.jpg |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T14249 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9922000169754028 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2311 |
| primary_topic.subfield.display_name | Industrial and Manufacturing Engineering |
| primary_topic.display_name | Water Quality Monitoring and Analysis |
| related_works | https://openalex.org/W2072166414, https://openalex.org/W3209970181, https://openalex.org/W2070598848, https://openalex.org/W3034375524, https://openalex.org/W2060875994, https://openalex.org/W2801661703, https://openalex.org/W3150155395, https://openalex.org/W2363756343, https://openalex.org/W4382982879, https://openalex.org/W4311044000 |
| cited_by_count | 19 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 10 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 5 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1016/j.ecoinf.2023.102058 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S195809937 |
| best_oa_location.source.issn | 1574-9541, 1878-0512 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1574-9541 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Ecological Informatics |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://ars.els-cdn.com/content/image/1-s2.0-S1574954123000870-ga1_lrg.jpg |
| 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 | Ecological Informatics |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.ecoinf.2023.102058 |
| primary_location.id | doi:10.1016/j.ecoinf.2023.102058 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S195809937 |
| primary_location.source.issn | 1574-9541, 1878-0512 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1574-9541 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Ecological Informatics |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://ars.els-cdn.com/content/image/1-s2.0-S1574954123000870-ga1_lrg.jpg |
| 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 | Ecological Informatics |
| primary_location.landing_page_url | https://doi.org/10.1016/j.ecoinf.2023.102058 |
| publication_date | 2023-03-13 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2001970696, https://openalex.org/W2066485336, https://openalex.org/W2293584046, https://openalex.org/W2261059368, https://openalex.org/W2471347751, https://openalex.org/W4205853798, https://openalex.org/W2769131015, https://openalex.org/W3087460440, https://openalex.org/W2080877396, https://openalex.org/W4207067038, https://openalex.org/W3024768525, https://openalex.org/W4205914679, https://openalex.org/W2230051944, https://openalex.org/W3209908932, https://openalex.org/W3128036660, https://openalex.org/W2513884524, https://openalex.org/W2083825310, https://openalex.org/W1986263983, https://openalex.org/W1772504446, https://openalex.org/W2943554448, https://openalex.org/W3165226483, https://openalex.org/W6727145746, https://openalex.org/W2030494178, https://openalex.org/W2161369308, https://openalex.org/W2889493124, https://openalex.org/W3039653660, https://openalex.org/W842886668, https://openalex.org/W4223655275, https://openalex.org/W4200594289, https://openalex.org/W1992233912, https://openalex.org/W2167451248, https://openalex.org/W3198588536, https://openalex.org/W6762782476, https://openalex.org/W3133424649, https://openalex.org/W2020208304, https://openalex.org/W2125240520, https://openalex.org/W2150729251, https://openalex.org/W2898245673, https://openalex.org/W1967906870, https://openalex.org/W2891166038, https://openalex.org/W2884829510, https://openalex.org/W3189321651, https://openalex.org/W2979313052, https://openalex.org/W3154489171, https://openalex.org/W2466906145, https://openalex.org/W3010208110, https://openalex.org/W3018952706, https://openalex.org/W2034742150, https://openalex.org/W1990255627, https://openalex.org/W3015988182, https://openalex.org/W1988911077, https://openalex.org/W2033788896, https://openalex.org/W1972212763, https://openalex.org/W2095473880, https://openalex.org/W2789224430, https://openalex.org/W1986621942, https://openalex.org/W3131845810, https://openalex.org/W1956581956, https://openalex.org/W3049103587, https://openalex.org/W2073503722, https://openalex.org/W2092094685, https://openalex.org/W2522545286, https://openalex.org/W4238934306 |
| referenced_works_count | 63 |
| abstract_inverted_index.= | 128, 131, 140, 145, 148, 153 |
| abstract_inverted_index.a | 104 |
| abstract_inverted_index.In | 189 |
| abstract_inverted_index.as | 38 |
| abstract_inverted_index.be | 214 |
| abstract_inverted_index.it | 212 |
| abstract_inverted_index.nm | 176, 181 |
| abstract_inverted_index.of | 5, 40, 79, 92, 182, 232 |
| abstract_inverted_index.on | 23, 114 |
| abstract_inverted_index.to | 29, 66, 102, 106, 159, 179, 202 |
| abstract_inverted_index.(R2 | 127, 144 |
| abstract_inverted_index.550 | 175 |
| abstract_inverted_index.700 | 178 |
| abstract_inverted_index.750 | 180 |
| abstract_inverted_index.Our | 0 |
| abstract_inverted_index.TSS | 143, 169 |
| abstract_inverted_index.and | 9, 33, 60, 74, 134, 142, 151, 168, 177, 185, 222, 225 |
| abstract_inverted_index.can | 213 |
| abstract_inverted_index.for | 124 |
| abstract_inverted_index.its | 108 |
| abstract_inverted_index.red | 184 |
| abstract_inverted_index.the | 3, 6, 16, 24, 41, 70, 75, 80, 89, 93, 115, 163, 172, 183, 191, 197, 206, 217, 228, 233 |
| abstract_inverted_index.two | 39 |
| abstract_inverted_index.was | 122 |
| abstract_inverted_index.(ML) | 50 |
| abstract_inverted_index.AISA | 25, 207 |
| abstract_inverted_index.LCCC | 152 |
| abstract_inverted_index.RMSE | 130, 147 |
| abstract_inverted_index.also | 100 |
| abstract_inverted_index.both | 125 |
| abstract_inverted_index.data | 28 |
| abstract_inverted_index.from | 83, 162, 196, 205 |
| abstract_inverted_index.maps | 194 |
| abstract_inverted_index.most | 42 |
| abstract_inverted_index.part | 91 |
| abstract_inverted_index.that | 216 |
| abstract_inverted_index.used | 101 |
| abstract_inverted_index.were | 64, 99, 156, 200 |
| abstract_inverted_index.with | 110, 171 |
| abstract_inverted_index.(NIR) | 187 |
| abstract_inverted_index.(RF)) | 63 |
| abstract_inverted_index.(TSS) | 37 |
| abstract_inverted_index.0.89, | 129 |
| abstract_inverted_index.0.91) | 141 |
| abstract_inverted_index.0.91, | 146 |
| abstract_inverted_index.17.53 | 149 |
| abstract_inverted_index.43.06 | 132 |
| abstract_inverted_index.Chl-a | 126, 167 |
| abstract_inverted_index.Czech | 94 |
| abstract_inverted_index.Eagle | 26, 208 |
| abstract_inverted_index.EnMAP | 17, 117, 199, 219 |
| abstract_inverted_index.Lin's | 135 |
| abstract_inverted_index.Three | 47 |
| abstract_inverted_index.based | 22, 113 |
| abstract_inverted_index.close | 157 |
| abstract_inverted_index.data. | 118, 210 |
| abstract_inverted_index.image | 72, 220 |
| abstract_inverted_index.least | 56 |
| abstract_inverted_index.links | 68 |
| abstract_inverted_index.mg/L, | 150 |
| abstract_inverted_index.model | 105 |
| abstract_inverted_index.small | 234 |
| abstract_inverted_index.study | 1 |
| abstract_inverted_index.those | 160, 203 |
| abstract_inverted_index.total | 34 |
| abstract_inverted_index.using | 15 |
| abstract_inverted_index.water | 44, 77, 86, 236 |
| abstract_inverted_index.which | 155 |
| abstract_inverted_index.(LCCC) | 139 |
| abstract_inverted_index.(PLSR) | 59 |
| abstract_inverted_index.0.94), | 154 |
| abstract_inverted_index.around | 174 |
| abstract_inverted_index.enough | 158 |
| abstract_inverted_index.forest | 62 |
| abstract_inverted_index.images | 98 |
| abstract_inverted_index.inland | 85, 235 |
| abstract_inverted_index.mapped | 227 |
| abstract_inverted_index.models | 111 |
| abstract_inverted_index.random | 61 |
| abstract_inverted_index.scenes | 13 |
| abstract_inverted_index.solids | 36 |
| abstract_inverted_index.square | 57 |
| abstract_inverted_index.within | 88 |
| abstract_inverted_index.μg/L, | 133 |
| abstract_inverted_index.(Chl-a) | 32 |
| abstract_inverted_index.(EeteS) | 21 |
| abstract_inverted_index.(EnMAP) | 12 |
| abstract_inverted_index.between | 69 |
| abstract_inverted_index.bodies. | 237 |
| abstract_inverted_index.compare | 107 |
| abstract_inverted_index.crucial | 43 |
| abstract_inverted_index.derived | 195 |
| abstract_inverted_index.develop | 103 |
| abstract_inverted_index.images. | 166 |
| abstract_inverted_index.machine | 48 |
| abstract_inverted_index.mapping | 8 |
| abstract_inverted_index.partial | 55 |
| abstract_inverted_index.predict | 30 |
| abstract_inverted_index.program | 11 |
| abstract_inverted_index.quality | 45 |
| abstract_inverted_index.samples | 81 |
| abstract_inverted_index.several | 84 |
| abstract_inverted_index.spatial | 192 |
| abstract_inverted_index.spectra | 73 |
| abstract_inverted_index.Adequate | 119 |
| abstract_inverted_index.Airborne | 96 |
| abstract_inverted_index.Overall, | 211 |
| abstract_inverted_index.accuracy | 121 |
| abstract_inverted_index.airborne | 27, 164, 209 |
| abstract_inverted_index.analysis | 10 |
| abstract_inverted_index.employed | 65 |
| abstract_inverted_index.learning | 49 |
| abstract_inverted_index.obtained | 123, 161, 204 |
| abstract_inverted_index.regions. | 188 |
| abstract_inverted_index.reliably | 223 |
| abstract_inverted_index.selected | 229 |
| abstract_inverted_index.software | 20 |
| abstract_inverted_index.southern | 90 |
| abstract_inverted_index.Republic. | 95 |
| abstract_inverted_index.addition, | 190 |
| abstract_inverted_index.collected | 82 |
| abstract_inverted_index.component | 53 |
| abstract_inverted_index.concluded | 215 |
| abstract_inverted_index.developed | 112 |
| abstract_inverted_index.establish | 67 |
| abstract_inverted_index.predicted | 224 |
| abstract_inverted_index.simulated | 14, 71, 116, 198, 218 |
| abstract_inverted_index.simulator | 19 |
| abstract_inverted_index.spatially | 226 |
| abstract_inverted_index.suspended | 35 |
| abstract_inverted_index.(principal | 52 |
| abstract_inverted_index.approaches | 51 |
| abstract_inverted_index.attributes | 78 |
| abstract_inverted_index.capability | 4 |
| abstract_inverted_index.comparable | 201 |
| abstract_inverted_index.correlated | 170 |
| abstract_inverted_index.end-to-end | 18 |
| abstract_inverted_index.prediction | 120 |
| abstract_inverted_index.properties | 231 |
| abstract_inverted_index.regression | 58 |
| abstract_inverted_index.reservoirs | 87 |
| abstract_inverted_index.biophysical | 230 |
| abstract_inverted_index.coefficient | 138 |
| abstract_inverted_index.concordance | 136 |
| abstract_inverted_index.correlation | 137 |
| abstract_inverted_index.indicators. | 46 |
| abstract_inverted_index.performance | 109 |
| abstract_inverted_index.wavelengths | 173 |
| abstract_inverted_index.distribution | 193 |
| abstract_inverted_index.investigates | 2 |
| abstract_inverted_index.successfully | 221 |
| abstract_inverted_index.chlorophyll-a | 31 |
| abstract_inverted_index.environmental | 7 |
| abstract_inverted_index.hyperspectral | 97, 165 |
| abstract_inverted_index.near-infrared | 186 |
| abstract_inverted_index.above-mentioned | 76 |
| abstract_inverted_index.regression(PCR), | 54 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5026895527 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210152878 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.93736115 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |