ESTIMATION OF NDVI FOR CLOUDY PIXELS USING MACHINE LEARNING Article Swipe
Rishabh Agrawal
,
J. D. Mohite
,
Suryakant Sawant
,
A. Pandit
,
S. Pappula
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022
The Normalized Difference Vegetation Index (NDVI) is a useful index for vegetation monitoring. However, due to cloud cover the observations of NDVI are discrete and vary in the intensity. Therefore, there is a need to estimate the NDVI during cloud cover using alternative sources of satellite observations. The main objective of this study is to estimate NDVI during cloudy conditions using moderate resolution multi-spectral and synthetic aperture radar (SAR) observations. Two approaches were identified: 1) pixel replacement and 2) machine learning based regression analysis to estimate cloud free NDVI. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day NDVI composite, Sentinel-1 SAR and cloud masked Sentinel-2 multi-spectral observations were collected for entire cropping season. The satellite observations were selected only for agricultural areas by applying the agriculture, non-agriculture land use land cover mask. Machine learning algorithms such as Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were used for NDVI estimation. Regression analysis was performed using Sentinel-2 NDVI as an independent variable and VV, VH, Cross Ratio (i.e., VV/VH), and MODIS NDVI as dependent variables. NDVI of the cloudy pixel was estimated using the trained regression models over the agriculture areas. A regression model was trained and applied to each Sentinel-2 tile that covers an area of 100 km × 100 km. The RFR and SVR showed the highest R2 of 0.73 and a RMSE of 0.12. A visual comparison of time series graphs showed good alignment between actual (Sentinel-2) and predicted NDVI and usual crop growth trend.
Related Topics
Concepts
Normalized Difference Vegetation Index
Remote sensing
Moderate-resolution imaging spectroradiometer
Land cover
Regression analysis
Linear regression
Random forest
Cloud cover
Support vector machine
Satellite
Pixel
Environmental science
Regression
Mathematics
Geography
Cloud computing
Statistics
Machine learning
Computer science
Climate change
Artificial intelligence
Land use
Geology
Operating system
Engineering
Civil engineering
Oceanography
Aerospace engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022
- https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/813/2022/isprs-archives-XLIII-B3-2022-813-2022.pdf
- OA Status
- diamond
- Cited By
- 8
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4282016880
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4282016880Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022Digital Object Identifier
- Title
-
ESTIMATION OF NDVI FOR CLOUDY PIXELS USING MACHINE LEARNINGWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-30Full publication date if available
- Authors
-
Rishabh Agrawal, J. D. Mohite, Suryakant Sawant, A. Pandit, S. PappulaList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022Publisher landing page
- PDF URL
-
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/813/2022/isprs-archives-XLIII-B3-2022-813-2022.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/813/2022/isprs-archives-XLIII-B3-2022-813-2022.pdfDirect OA link when available
- Concepts
-
Normalized Difference Vegetation Index, Remote sensing, Moderate-resolution imaging spectroradiometer, Land cover, Regression analysis, Linear regression, Random forest, Cloud cover, Support vector machine, Satellite, Pixel, Environmental science, Regression, Mathematics, Geography, Cloud computing, Statistics, Machine learning, Computer science, Climate change, Artificial intelligence, Land use, Geology, Operating system, Engineering, Civil engineering, Oceanography, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 4, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
13Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4282016880 |
|---|---|
| doi | https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022 |
| ids.doi | https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022 |
| ids.openalex | https://openalex.org/W4282016880 |
| fwci | 1.71496915 |
| type | article |
| title | ESTIMATION OF NDVI FOR CLOUDY PIXELS USING MACHINE LEARNING |
| biblio.issue | |
| biblio.volume | XLIII-B3-2022 |
| biblio.last_page | 818 |
| biblio.first_page | 813 |
| 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 | 0.9994999766349792 |
| 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 | 0.9810000061988831 |
| 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/T10226 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9758999943733215 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2306 |
| topics[2].subfield.display_name | Global and Planetary Change |
| topics[2].display_name | Land Use and Ecosystem Services |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C1549246 |
| concepts[0].level | 3 |
| concepts[0].score | 0.943514347076416 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q718775 |
| concepts[0].display_name | Normalized Difference Vegetation Index |
| concepts[1].id | https://openalex.org/C62649853 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6494146585464478 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[1].display_name | Remote sensing |
| concepts[2].id | https://openalex.org/C2777007095 |
| concepts[2].level | 3 |
| concepts[2].score | 0.649131715297699 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q676840 |
| concepts[2].display_name | Moderate-resolution imaging spectroradiometer |
| concepts[3].id | https://openalex.org/C2780648208 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6387830972671509 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3001793 |
| concepts[3].display_name | Land cover |
| concepts[4].id | https://openalex.org/C152877465 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5549941062927246 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q208042 |
| concepts[4].display_name | Regression analysis |
| concepts[5].id | https://openalex.org/C48921125 |
| concepts[5].level | 2 |
| concepts[5].score | 0.529310941696167 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q10861030 |
| concepts[5].display_name | Linear regression |
| concepts[6].id | https://openalex.org/C169258074 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5004489421844482 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[6].display_name | Random forest |
| concepts[7].id | https://openalex.org/C206887242 |
| concepts[7].level | 3 |
| concepts[7].score | 0.45999017357826233 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q830457 |
| concepts[7].display_name | Cloud cover |
| concepts[8].id | https://openalex.org/C12267149 |
| concepts[8].level | 2 |
| concepts[8].score | 0.44220787286758423 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[8].display_name | Support vector machine |
| concepts[9].id | https://openalex.org/C19269812 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4332200288772583 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q26540 |
| concepts[9].display_name | Satellite |
| concepts[10].id | https://openalex.org/C160633673 |
| concepts[10].level | 2 |
| concepts[10].score | 0.42710360884666443 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q355198 |
| concepts[10].display_name | Pixel |
| concepts[11].id | https://openalex.org/C39432304 |
| concepts[11].level | 0 |
| concepts[11].score | 0.4262318015098572 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[11].display_name | Environmental science |
| concepts[12].id | https://openalex.org/C83546350 |
| concepts[12].level | 2 |
| concepts[12].score | 0.414227157831192 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1139051 |
| concepts[12].display_name | Regression |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.35468101501464844 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C205649164 |
| concepts[14].level | 0 |
| concepts[14].score | 0.2632738947868347 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[14].display_name | Geography |
| concepts[15].id | https://openalex.org/C79974875 |
| concepts[15].level | 2 |
| concepts[15].score | 0.24977251887321472 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[15].display_name | Cloud computing |
| concepts[16].id | https://openalex.org/C105795698 |
| concepts[16].level | 1 |
| concepts[16].score | 0.2328609824180603 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[16].display_name | Statistics |
| concepts[17].id | https://openalex.org/C119857082 |
| concepts[17].level | 1 |
| concepts[17].score | 0.20227450132369995 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[17].display_name | Machine learning |
| concepts[18].id | https://openalex.org/C41008148 |
| concepts[18].level | 0 |
| concepts[18].score | 0.1834215521812439 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[18].display_name | Computer science |
| concepts[19].id | https://openalex.org/C132651083 |
| concepts[19].level | 2 |
| concepts[19].score | 0.16669395565986633 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q7942 |
| concepts[19].display_name | Climate change |
| concepts[20].id | https://openalex.org/C154945302 |
| concepts[20].level | 1 |
| concepts[20].score | 0.16291865706443787 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[20].display_name | Artificial intelligence |
| concepts[21].id | https://openalex.org/C4792198 |
| concepts[21].level | 2 |
| concepts[21].score | 0.1487501859664917 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q1165944 |
| concepts[21].display_name | Land use |
| concepts[22].id | https://openalex.org/C127313418 |
| concepts[22].level | 0 |
| concepts[22].score | 0.09131857752799988 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[22].display_name | Geology |
| concepts[23].id | https://openalex.org/C111919701 |
| concepts[23].level | 1 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[23].display_name | Operating system |
| concepts[24].id | https://openalex.org/C127413603 |
| concepts[24].level | 0 |
| concepts[24].score | 0.0 |
| concepts[24].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[24].display_name | Engineering |
| concepts[25].id | https://openalex.org/C147176958 |
| concepts[25].level | 1 |
| concepts[25].score | 0.0 |
| concepts[25].wikidata | https://www.wikidata.org/wiki/Q77590 |
| concepts[25].display_name | Civil engineering |
| concepts[26].id | https://openalex.org/C111368507 |
| concepts[26].level | 1 |
| concepts[26].score | 0.0 |
| concepts[26].wikidata | https://www.wikidata.org/wiki/Q43518 |
| concepts[26].display_name | Oceanography |
| concepts[27].id | https://openalex.org/C146978453 |
| concepts[27].level | 1 |
| concepts[27].score | 0.0 |
| concepts[27].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[27].display_name | Aerospace engineering |
| keywords[0].id | https://openalex.org/keywords/normalized-difference-vegetation-index |
| keywords[0].score | 0.943514347076416 |
| keywords[0].display_name | Normalized Difference Vegetation Index |
| keywords[1].id | https://openalex.org/keywords/remote-sensing |
| keywords[1].score | 0.6494146585464478 |
| keywords[1].display_name | Remote sensing |
| keywords[2].id | https://openalex.org/keywords/moderate-resolution-imaging-spectroradiometer |
| keywords[2].score | 0.649131715297699 |
| keywords[2].display_name | Moderate-resolution imaging spectroradiometer |
| keywords[3].id | https://openalex.org/keywords/land-cover |
| keywords[3].score | 0.6387830972671509 |
| keywords[3].display_name | Land cover |
| keywords[4].id | https://openalex.org/keywords/regression-analysis |
| keywords[4].score | 0.5549941062927246 |
| keywords[4].display_name | Regression analysis |
| keywords[5].id | https://openalex.org/keywords/linear-regression |
| keywords[5].score | 0.529310941696167 |
| keywords[5].display_name | Linear regression |
| keywords[6].id | https://openalex.org/keywords/random-forest |
| keywords[6].score | 0.5004489421844482 |
| keywords[6].display_name | Random forest |
| keywords[7].id | https://openalex.org/keywords/cloud-cover |
| keywords[7].score | 0.45999017357826233 |
| keywords[7].display_name | Cloud cover |
| keywords[8].id | https://openalex.org/keywords/support-vector-machine |
| keywords[8].score | 0.44220787286758423 |
| keywords[8].display_name | Support vector machine |
| keywords[9].id | https://openalex.org/keywords/satellite |
| keywords[9].score | 0.4332200288772583 |
| keywords[9].display_name | Satellite |
| keywords[10].id | https://openalex.org/keywords/pixel |
| keywords[10].score | 0.42710360884666443 |
| keywords[10].display_name | Pixel |
| keywords[11].id | https://openalex.org/keywords/environmental-science |
| keywords[11].score | 0.4262318015098572 |
| keywords[11].display_name | Environmental science |
| keywords[12].id | https://openalex.org/keywords/regression |
| keywords[12].score | 0.414227157831192 |
| keywords[12].display_name | Regression |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.35468101501464844 |
| keywords[13].display_name | Mathematics |
| keywords[14].id | https://openalex.org/keywords/geography |
| keywords[14].score | 0.2632738947868347 |
| keywords[14].display_name | Geography |
| keywords[15].id | https://openalex.org/keywords/cloud-computing |
| keywords[15].score | 0.24977251887321472 |
| keywords[15].display_name | Cloud computing |
| keywords[16].id | https://openalex.org/keywords/statistics |
| keywords[16].score | 0.2328609824180603 |
| keywords[16].display_name | Statistics |
| keywords[17].id | https://openalex.org/keywords/machine-learning |
| keywords[17].score | 0.20227450132369995 |
| keywords[17].display_name | Machine learning |
| keywords[18].id | https://openalex.org/keywords/computer-science |
| keywords[18].score | 0.1834215521812439 |
| keywords[18].display_name | Computer science |
| keywords[19].id | https://openalex.org/keywords/climate-change |
| keywords[19].score | 0.16669395565986633 |
| keywords[19].display_name | Climate change |
| keywords[20].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[20].score | 0.16291865706443787 |
| keywords[20].display_name | Artificial intelligence |
| keywords[21].id | https://openalex.org/keywords/land-use |
| keywords[21].score | 0.1487501859664917 |
| keywords[21].display_name | Land use |
| keywords[22].id | https://openalex.org/keywords/geology |
| keywords[22].score | 0.09131857752799988 |
| keywords[22].display_name | Geology |
| language | en |
| locations[0].id | doi:10.5194/isprs-archives-xliii-b3-2022-813-2022 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2737215817 |
| locations[0].source.issn | 1682-1750, 1682-1777, 2194-9034 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1682-1750 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences |
| locations[0].source.host_organization | https://openalex.org/P4310313756 |
| locations[0].source.host_organization_name | Copernicus Publications |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310313756 |
| locations[0].source.host_organization_lineage_names | Copernicus Publications |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/813/2022/isprs-archives-XLIII-B3-2022-813-2022.pdf |
| 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 | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| locations[0].landing_page_url | https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022 |
| locations[1].id | pmh:oai:doaj.org/article:5ebc8a050d2f44fd8f13afb32e6ef664 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2022, Pp 813-818 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/5ebc8a050d2f44fd8f13afb32e6ef664 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5039483511 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7368-5130 |
| authorships[0].author.display_name | Rishabh Agrawal |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I55215948 |
| authorships[0].affiliations[0].raw_affiliation_string | TCS Research and Innovation, Tata Consultancy Services, Mumbai, India |
| authorships[0].institutions[0].id | https://openalex.org/I55215948 |
| authorships[0].institutions[0].ror | https://ror.org/01b9n8m42 |
| authorships[0].institutions[0].type | company |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210086519, https://openalex.org/I55215948 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Tata Consultancy Services (India) |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | R. Agrawal |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | TCS Research and Innovation, Tata Consultancy Services, Mumbai, India |
| authorships[1].author.id | https://openalex.org/A5083491157 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | J. D. Mohite |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I55215948 |
| authorships[1].affiliations[0].raw_affiliation_string | TCS Research and Innovation, Tata Consultancy Services, Mumbai, India |
| authorships[1].institutions[0].id | https://openalex.org/I55215948 |
| authorships[1].institutions[0].ror | https://ror.org/01b9n8m42 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210086519, https://openalex.org/I55215948 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Tata Consultancy Services (India) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | J. D. Mohite |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | TCS Research and Innovation, Tata Consultancy Services, Mumbai, India |
| authorships[2].author.id | https://openalex.org/A5041057249 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5982-4432 |
| authorships[2].author.display_name | Suryakant Sawant |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I55215948 |
| authorships[2].affiliations[0].raw_affiliation_string | TCS Research and Innovation, Tata Consultancy Services, Mumbai, India |
| authorships[2].institutions[0].id | https://openalex.org/I55215948 |
| authorships[2].institutions[0].ror | https://ror.org/01b9n8m42 |
| authorships[2].institutions[0].type | company |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210086519, https://openalex.org/I55215948 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Tata Consultancy Services (India) |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | S. A. Sawant |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | TCS Research and Innovation, Tata Consultancy Services, Mumbai, India |
| authorships[3].author.id | https://openalex.org/A5047095985 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | A. Pandit |
| authorships[3].countries | IN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I55215948 |
| authorships[3].affiliations[0].raw_affiliation_string | TCS Research and Innovation, Tata Consultancy Services, Mumbai, India |
| authorships[3].institutions[0].id | https://openalex.org/I55215948 |
| authorships[3].institutions[0].ror | https://ror.org/01b9n8m42 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210086519, https://openalex.org/I55215948 |
| authorships[3].institutions[0].country_code | IN |
| authorships[3].institutions[0].display_name | Tata Consultancy Services (India) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | A. Pandit |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | TCS Research and Innovation, Tata Consultancy Services, Mumbai, India |
| authorships[4].author.id | https://openalex.org/A5036037319 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | S. Pappula |
| authorships[4].countries | IN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I55215948 |
| authorships[4].affiliations[0].raw_affiliation_string | TCS Research and Innovation, Tata Consultancy Services, Hyderabad, India |
| authorships[4].institutions[0].id | https://openalex.org/I55215948 |
| authorships[4].institutions[0].ror | https://ror.org/01b9n8m42 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210086519, https://openalex.org/I55215948 |
| authorships[4].institutions[0].country_code | IN |
| authorships[4].institutions[0].display_name | Tata Consultancy Services (India) |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | S. Pappula |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | TCS Research and Innovation, Tata Consultancy Services, Hyderabad, India |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/813/2022/isprs-archives-XLIII-B3-2022-813-2022.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | ESTIMATION OF NDVI FOR CLOUDY PIXELS USING MACHINE LEARNING |
| has_fulltext | True |
| 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 | 0.9994999766349792 |
| 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/W3207046288, https://openalex.org/W3023446922, https://openalex.org/W4324030030, https://openalex.org/W3183766399, https://openalex.org/W2021379394, https://openalex.org/W2736619525, https://openalex.org/W2921923888, https://openalex.org/W1968232416, https://openalex.org/W2908695806, https://openalex.org/W2086038705 |
| cited_by_count | 8 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| 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 | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.5194/isprs-archives-xliii-b3-2022-813-2022 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2737215817 |
| best_oa_location.source.issn | 1682-1750, 1682-1777, 2194-9034 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1682-1750 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences |
| best_oa_location.source.host_organization | https://openalex.org/P4310313756 |
| best_oa_location.source.host_organization_name | Copernicus Publications |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310313756 |
| best_oa_location.source.host_organization_lineage_names | Copernicus Publications |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/813/2022/isprs-archives-XLIII-B3-2022-813-2022.pdf |
| 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 | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| best_oa_location.landing_page_url | https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022 |
| primary_location.id | doi:10.5194/isprs-archives-xliii-b3-2022-813-2022 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2737215817 |
| primary_location.source.issn | 1682-1750, 1682-1777, 2194-9034 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1682-1750 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences |
| primary_location.source.host_organization | https://openalex.org/P4310313756 |
| primary_location.source.host_organization_name | Copernicus Publications |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310313756 |
| primary_location.source.host_organization_lineage_names | Copernicus Publications |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/813/2022/isprs-archives-XLIII-B3-2022-813-2022.pdf |
| 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 | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| primary_location.landing_page_url | https://doi.org/10.5194/isprs-archives-xliii-b3-2022-813-2022 |
| publication_date | 2022-05-30 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2228578885, https://openalex.org/W1974047452, https://openalex.org/W2912380233, https://openalex.org/W2742413277, https://openalex.org/W2404325781, https://openalex.org/W2149580466, https://openalex.org/W3206245048, https://openalex.org/W2745131289, https://openalex.org/W2807404598, https://openalex.org/W2023912087, https://openalex.org/W2953239899, https://openalex.org/W2012159909, https://openalex.org/W2121025662 |
| referenced_works_count | 13 |
| abstract_inverted_index.A | 193, 229 |
| abstract_inverted_index.a | 8, 33, 225 |
| abstract_inverted_index.1) | 75 |
| abstract_inverted_index.2) | 79 |
| abstract_inverted_index.R2 | 221 |
| abstract_inverted_index.an | 161, 206 |
| abstract_inverted_index.as | 135, 160, 174 |
| abstract_inverted_index.by | 121 |
| abstract_inverted_index.in | 27 |
| abstract_inverted_index.is | 7, 32, 54 |
| abstract_inverted_index.km | 210 |
| abstract_inverted_index.of | 21, 45, 51, 178, 208, 222, 227, 232 |
| abstract_inverted_index.to | 16, 35, 55, 85, 200 |
| abstract_inverted_index.× | 211 |
| abstract_inverted_index.100 | 209, 212 |
| abstract_inverted_index.RFR | 215 |
| abstract_inverted_index.SAR | 99 |
| abstract_inverted_index.SVR | 217 |
| abstract_inverted_index.The | 1, 48, 112, 214 |
| abstract_inverted_index.Two | 71 |
| abstract_inverted_index.VH, | 166 |
| abstract_inverted_index.VV, | 165 |
| abstract_inverted_index.and | 25, 65, 78, 100, 143, 164, 171, 198, 216, 224, 242, 245 |
| abstract_inverted_index.are | 23 |
| abstract_inverted_index.due | 15 |
| abstract_inverted_index.for | 11, 108, 118, 150 |
| abstract_inverted_index.km. | 213 |
| abstract_inverted_index.the | 19, 28, 37, 123, 179, 185, 190, 219 |
| abstract_inverted_index.use | 127 |
| abstract_inverted_index.was | 155, 182, 196 |
| abstract_inverted_index.0.73 | 223 |
| abstract_inverted_index.NDVI | 22, 38, 57, 96, 151, 159, 173, 177, 244 |
| abstract_inverted_index.RMSE | 226 |
| abstract_inverted_index.area | 207 |
| abstract_inverted_index.crop | 247 |
| abstract_inverted_index.each | 201 |
| abstract_inverted_index.free | 88 |
| abstract_inverted_index.good | 237 |
| abstract_inverted_index.land | 126, 128 |
| abstract_inverted_index.main | 49 |
| abstract_inverted_index.need | 34 |
| abstract_inverted_index.only | 117 |
| abstract_inverted_index.over | 189 |
| abstract_inverted_index.such | 134 |
| abstract_inverted_index.that | 204 |
| abstract_inverted_index.this | 52 |
| abstract_inverted_index.tile | 203 |
| abstract_inverted_index.time | 233 |
| abstract_inverted_index.used | 149 |
| abstract_inverted_index.vary | 26 |
| abstract_inverted_index.were | 73, 106, 115, 148 |
| abstract_inverted_index.(LR), | 138 |
| abstract_inverted_index.(SAR) | 69 |
| abstract_inverted_index.(SVR) | 147 |
| abstract_inverted_index.0.12. | 228 |
| abstract_inverted_index.8-day | 95 |
| abstract_inverted_index.Cross | 167 |
| abstract_inverted_index.Index | 5 |
| abstract_inverted_index.MODIS | 172 |
| abstract_inverted_index.NDVI. | 89 |
| abstract_inverted_index.Ratio | 168 |
| abstract_inverted_index.areas | 120 |
| abstract_inverted_index.based | 82 |
| abstract_inverted_index.cloud | 17, 40, 87, 101 |
| abstract_inverted_index.cover | 18, 41, 129 |
| abstract_inverted_index.index | 10 |
| abstract_inverted_index.mask. | 130 |
| abstract_inverted_index.model | 195 |
| abstract_inverted_index.pixel | 76, 181 |
| abstract_inverted_index.radar | 68 |
| abstract_inverted_index.study | 53 |
| abstract_inverted_index.there | 31 |
| abstract_inverted_index.using | 42, 61, 157, 184 |
| abstract_inverted_index.usual | 246 |
| abstract_inverted_index.(NDVI) | 6 |
| abstract_inverted_index.(RFR), | 142 |
| abstract_inverted_index.(i.e., | 169 |
| abstract_inverted_index.Forest | 140 |
| abstract_inverted_index.Linear | 136 |
| abstract_inverted_index.Random | 139 |
| abstract_inverted_index.Vector | 145 |
| abstract_inverted_index.actual | 240 |
| abstract_inverted_index.areas. | 192 |
| abstract_inverted_index.cloudy | 59, 180 |
| abstract_inverted_index.covers | 205 |
| abstract_inverted_index.during | 39, 58 |
| abstract_inverted_index.entire | 109 |
| abstract_inverted_index.graphs | 235 |
| abstract_inverted_index.growth | 248 |
| abstract_inverted_index.masked | 102 |
| abstract_inverted_index.models | 188 |
| abstract_inverted_index.series | 234 |
| abstract_inverted_index.showed | 218, 236 |
| abstract_inverted_index.trend. | 249 |
| abstract_inverted_index.useful | 9 |
| abstract_inverted_index.visual | 230 |
| abstract_inverted_index.(MODIS) | 94 |
| abstract_inverted_index.Imaging | 92 |
| abstract_inverted_index.Machine | 131 |
| abstract_inverted_index.Support | 144 |
| abstract_inverted_index.VV/VH), | 170 |
| abstract_inverted_index.applied | 199 |
| abstract_inverted_index.between | 239 |
| abstract_inverted_index.highest | 220 |
| abstract_inverted_index.machine | 80 |
| abstract_inverted_index.season. | 111 |
| abstract_inverted_index.sources | 44 |
| abstract_inverted_index.trained | 186, 197 |
| abstract_inverted_index.However, | 14 |
| abstract_inverted_index.Moderate | 90 |
| abstract_inverted_index.analysis | 84, 154 |
| abstract_inverted_index.aperture | 67 |
| abstract_inverted_index.applying | 122 |
| abstract_inverted_index.cropping | 110 |
| abstract_inverted_index.discrete | 24 |
| abstract_inverted_index.estimate | 36, 56, 86 |
| abstract_inverted_index.learning | 81, 132 |
| abstract_inverted_index.moderate | 62 |
| abstract_inverted_index.selected | 116 |
| abstract_inverted_index.variable | 163 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.alignment | 238 |
| abstract_inverted_index.collected | 107 |
| abstract_inverted_index.dependent | 175 |
| abstract_inverted_index.estimated | 183 |
| abstract_inverted_index.objective | 50 |
| abstract_inverted_index.performed | 156 |
| abstract_inverted_index.predicted | 243 |
| abstract_inverted_index.satellite | 46, 113 |
| abstract_inverted_index.synthetic | 66 |
| abstract_inverted_index.Difference | 3 |
| abstract_inverted_index.Normalized | 2 |
| abstract_inverted_index.Regression | 137, 141, 146, 153 |
| abstract_inverted_index.Resolution | 91 |
| abstract_inverted_index.Sentinel-1 | 98 |
| abstract_inverted_index.Sentinel-2 | 103, 158, 202 |
| abstract_inverted_index.Therefore, | 30 |
| abstract_inverted_index.Vegetation | 4 |
| abstract_inverted_index.algorithms | 133 |
| abstract_inverted_index.approaches | 72 |
| abstract_inverted_index.comparison | 231 |
| abstract_inverted_index.composite, | 97 |
| abstract_inverted_index.conditions | 60 |
| abstract_inverted_index.intensity. | 29 |
| abstract_inverted_index.regression | 83, 187, 194 |
| abstract_inverted_index.resolution | 63 |
| abstract_inverted_index.variables. | 176 |
| abstract_inverted_index.vegetation | 12 |
| abstract_inverted_index.agriculture | 191 |
| abstract_inverted_index.alternative | 43 |
| abstract_inverted_index.estimation. | 152 |
| abstract_inverted_index.identified: | 74 |
| abstract_inverted_index.independent | 162 |
| abstract_inverted_index.monitoring. | 13 |
| abstract_inverted_index.replacement | 77 |
| abstract_inverted_index.(Sentinel-2) | 241 |
| abstract_inverted_index.agricultural | 119 |
| abstract_inverted_index.agriculture, | 124 |
| abstract_inverted_index.observations | 20, 105, 114 |
| abstract_inverted_index.observations. | 47, 70 |
| abstract_inverted_index.multi-spectral | 64, 104 |
| abstract_inverted_index.non-agriculture | 125 |
| abstract_inverted_index.Spectroradiometer | 93 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.80954262 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |