{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T13:04:58Z","timestamp":1784725498177,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,9,19]],"date-time":"2016-09-19T00:00:00Z","timestamp":1474243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Planet Labs (\u201cPlanet\u201d) operate the largest fleet of active nano-satellites in orbit, offering an unprecedented monitoring capacity of daily and global RGB image capture at 3\u20135 m resolution. However, limitations in spectral resolution and lack of accurate radiometric sensor calibration impact the utility of this rich information source. In this study, Planet\u2019s RGB imagery was translated into a Normalized Difference Vegetation Index (NDVI): a common metric for vegetation growth and condition. Our framework employs a data mining approach to build a set of rule-based regression models that relate RGB data to atmospherically corrected Landsat-8 NDVI. The approach was evaluated over a desert agricultural landscape in Saudi Arabia where the use of near-coincident (within five days) Planet and Landsat-8 acquisitions in the training of the regression models resulted in NDVI predictabilities with an r2 of approximately 0.97 and a Mean Absolute Deviation (MAD) on the order of 0.014 (~9%). The MAD increased to 0.021 (~14%) when the Landsat NDVI training image was further away (i.e., 11\u201316 days) from the corrected Planet image. In these cases, the use of MODIS observations to inform on the change in NDVI occurring between overpasses was shown to significantly improve prediction accuracies. MAD levels ranged from 0.002 to 0.011 (3.9% to 9.1%) for the best performing 80% of the data. The technique is generic and extendable to any region of interest, increasing the utility of Planet\u2019s dense time-series of RGB imagery.<\/jats:p>","DOI":"10.3390\/rs8090768","type":"journal-article","created":{"date-parts":[[2016,9,19]],"date-time":"2016-09-19T10:07:43Z","timestamp":1474279663000},"page":"768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":179,"title":["High-Resolution NDVI from Planet\u2019s Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture"],"prefix":"10.3390","volume":"8","author":[{"given":"Rasmus","family":"Houborg","sequence":"first","affiliation":[{"name":"King Abdullah University of Science and Technology (KAUST), Water Desalination and Reuse Center (WDRC), Biological and Environmental Science & Engineering (BESE), Thuwal 23955, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-5272","authenticated-orcid":false,"given":"Matthew","family":"McCabe","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology (KAUST), Water Desalination and Reuse Center (WDRC), Biological and Environmental Science & Engineering (BESE), Thuwal 23955, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and Narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0094-5765(99)00214-3","article-title":"NASA-GSFC Nano-satellite technology for Earth Science missions","volume":"46","author":"Esper","year":"2000","journal-title":"Acta Astronaut."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/S0034-4257(97)00045-X","article-title":"Opportunities and limitations for image-based remote sensing in precision crop management","volume":"61","author":"Moran","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision agriculture\u2014A worldwide overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1023\/A:1021171514148","article-title":"Precision agriculture: A challenge for crop nutrition management","volume":"247","author":"Robert","year":"2002","journal-title":"Plant Soil"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1126\/science.1183899","article-title":"Precision agriculture and food security","volume":"327","author":"Gebbers","year":"2010","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2011.08.026","article-title":"The next Landsat satellite: The Landsat data continuity mission","volume":"122","author":"Irons","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"600","DOI":"10.3390\/rs70100600","article-title":"The ground-based absolute radiometric calibration of Landsat 8 OLI","volume":"7","author":"McCorkel","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"520","DOI":"10.3390\/rs8060520","article-title":"An Automated Approach for sub-pixel registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery","volume":"8","author":"Yan","year":"2016","journal-title":"Remote Sens."},{"key":"ref_12","first-page":"1","article-title":"Advances in remote sensing of vegetation function and traits","volume":"3","author":"Houborg","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/0034-4257(95)00193-X","article-title":"Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS","volume":"55","author":"Kaufman","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0034-4257(02)00089-5","article-title":"Atmospheric correction of MODIS data in the visible to middle infrared: First results","volume":"83","author":"Vermote","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/S0034-4257(98)00014-5","article-title":"Biophysical and Biochemical sources of variability in canopy reflectance","volume":"64","author":"Asner","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"Rouse, J., Haas, R., Schell, J., and Deering, D. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS-1 Symposium NASA SP-351, Washington, DC, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic Infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1126\/science.227.4685.369","article-title":"African land-cover classification using satellite data","volume":"227","author":"Tucker","year":"1985","journal-title":"Science"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/386698a0","article-title":"Increased plant growth in the northern high latitudes from 1981 to 1991","volume":"386","author":"Myneni","year":"1997","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1016\/j.rse.2009.04.004","article-title":"Evaluation of earth observation based long term vegetation trends\u2014Ntercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data","volume":"113","author":"Fensholt","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2014.11.029","article-title":"Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin","volume":"159","author":"Olsen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_22","first-page":"71","article-title":"Drought monitoring with NDVI-Based standardized vegetation index","volume":"68","author":"Peters","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1175\/2009JCLI2900.1","article-title":"Use of NDVI and land surface temperature for drought assessment: Merits and limitations","volume":"23","author":"Karnieli","year":"2010","journal-title":"J. Clim."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1641\/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2","article-title":"A continuous satellite-derived measure of global terrestrial primary production","volume":"54","author":"Running","year":"2004","journal-title":"Bioscience"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2014.02.014","article-title":"Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250m resolution data","volume":"147","author":"Gitelson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5263","DOI":"10.1029\/93JD03221","article-title":"Methodology for the estimation of terrestrial net primary production from remotely sensed data","volume":"99","author":"Ruimy","year":"1994","journal-title":"J. Geophys. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.3390\/rs2102369","article-title":"Applicability of Green-Red Vegetation Index for remote sensing of vegetation phenology","volume":"2","author":"Motohka","year":"2010","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.rse.2007.07.010","article-title":"A VARI-based relative greenness from MODIS data for computing the Fire Potential Index","volume":"112","author":"Schneider","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2007JD009662","article-title":"Atmospheric correction for the monitoring of land surfaces","volume":"113","author":"Vermote","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1093\/jxb\/erl123","article-title":"Hyperspectral remote sensing of plant pigments","volume":"58","author":"Blackburn","year":"2007","journal-title":"J. Exp. Bot."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2006.07.016","article-title":"Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data","volume":"106","author":"Houborg","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2011). Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222-41"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating Multispectral images and vegetation indices for precision farming applications from UAV images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2016.08.017","article-title":"Adapting a regularized canopy reflectance model (REGFLEC) for the retrieval challenges of dryland agricultural systems","volume":"186","author":"Houborg","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"Landsat 8, Available online: http:\/\/landsat.usgs.gov\/landsat8.php."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2014.12.008","article-title":"Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)","volume":"159","author":"Houborg","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6762","DOI":"10.1364\/AO.45.006762","article-title":"Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance","volume":"45","author":"Kotchenova","year":"2006","journal-title":"Appl. Opt."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/36.581987","article-title":"Second simulation of the satellite signal in the solar spectrum, 6S: An overview","volume":"35","author":"Vermote","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1109\/TGRS.2004.824067","article-title":"Aerosol properties over bright-reflecting source regions","volume":"42","author":"Hsu","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","first-page":"15","article-title":"A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)","volume":"47","author":"Houborg","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7353","DOI":"10.1109\/TGRS.2014.2311445","article-title":"Operational data fusion framework for building frequent Landsat-like imagery","volume":"52","author":"Wang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","unstructured":"Quinlan, R. (1992, January 16\u201318). Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Australian."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Quinlan, R. (1993, January 27\u201329). Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning, Amherst, MA, USA.","DOI":"10.1016\/B978-1-55860-307-3.50037-X"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.ecolmodel.2006.05.021","article-title":"Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods","volume":"199","author":"Moisen","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.rse.2012.07.006","article-title":"Forest biomass estimation from airborne LiDAR data using machine learning approaches","volume":"125","author":"Gleason","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.14358\/PERS.74.10.1213","article-title":"Subpixel urban land cover estimation: Comparing cubist, random forests, and support vector regression","volume":"74","author":"Walton","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0034-4257(90)90037-M","article-title":"On the information content of soil reflectance spectra","volume":"33","author":"Price","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_51","first-page":"19","article-title":"Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy","volume":"43","author":"Atzberger","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/9\/768\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:31:09Z","timestamp":1760211069000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/9\/768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,19]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["rs8090768"],"URL":"https:\/\/doi.org\/10.3390\/rs8090768","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,19]]}}}