{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T07:14:09Z","timestamp":1770534849667,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,17]],"date-time":"2019-08-17T00:00:00Z","timestamp":1566000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000192","name":"NOAA","doi-asserted-by":"publisher","award":["18-087-000-A597"],"award-info":[{"award-number":["18-087-000-A597"]}],"id":[{"id":"10.13039\/100000192","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 \u00b1 0.14 to 0.08 \u00b1 0.07 and from 0.91 \u00b1 0.64 to 0.18 \u00b1 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.<\/jats:p>","DOI":"10.3390\/rs11161931","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"1931","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Hua","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Engineering and Computing Sciences, Texas A&amp;M University-Corpus Christi, Corpus Christi, TX 78412, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6541-2055","authenticated-orcid":false,"given":"Paul V.","family":"Zimba","sequence":"additional","affiliation":[{"name":"Center for Coastal Studies, Texas A&amp;M University-Corpus Christi, Corpus Christi, TX 78412, USA"}]},{"given":"Emmanuel U.","family":"Nzewi","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Prairie View A&amp;M University, Prairie View, TX 77446, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"737","DOI":"10.14358\/PERS.83.10.737","article-title":"Land Cover Classification and Feature Extraction from National Agriculture Imagery Program (NAIP) Orthoimagery: A Review","volume":"83","author":"Maxwell","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Singh, K.K., Madden, M., Gray, J., and Meentemeyer, R.K. (2018). The managed clearing: An overlooked land-cover type in urbanizing regions?. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192822"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.14358\/PERS.83.1.63","article-title":"High-resolution Land Cover and Impervious Surface Classifications in the Twin Cities Metropolitan Area with NAIP Imagery","volume":"82","author":"Nagel","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.scitotenv.2017.06.065","article-title":"Implications of changing spatial dynamics of irrigated pasture, California\u2019s third largest agricultural water use","volume":"605\u2013606","author":"Shapero","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Anderson, C., Carter, G., and Funderburk, W. (2016). The Use of Aerial RGB Imagery and LIDAR in Comparing Ecological Habitats and Geomorphic Features on a Natural versus Man-Made Barrier Island. Remote Sens., 8.","DOI":"10.3390\/rs8070602"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.isprsjprs.2018.03.019","article-title":"A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States","volume":"139","author":"Byrd","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Woodward, B.D., Evangelista, P.H., Young, N.E., Vorster, A.G., West, A.M., Carroll, S.L., Girma, R.K., Hatcher, E.Z., Anderson, R., and Vahsen, M.L. (2018). CO-RIP: A Riparian Vegetation and Corridor Extent Dataset for Colorado River Basin Streams and Rivers. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100397"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.06.015","article-title":"Detection of spruce beetle-induced tree mortality using high- and medium-resolution remotely sensed imagery","volume":"168","author":"Hart","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.agrformet.2017.02.026","article-title":"Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak\/grass savanna and open grassland across spatial scales","volume":"237\u2013238","author":"Liu","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.3390\/rs6031954","article-title":"Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR","volume":"6","author":"Bishop","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hartfield, K., and van Leeuwen, W. (2018). Woody Cover Estimates in Oklahoma and Texas Using a Multi-Sensor Calibration and Validation Approach. Remote Sens., 10.","DOI":"10.3390\/rs10040632"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1080\/13504509.2017.1409821","article-title":"The potential implementation of green infrastructure assessment using high-resolution National Agriculture Imagery Program data for sustainable hazard mitigation","volume":"25","author":"Lee","year":"2017","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.ufug.2017.12.001","article-title":"Estimation of urban woody vegetation cover using multispectral imagery and LiDAR","volume":"29","author":"Ucar","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hogland, J., Anderson, N., St. Peter, J., Drake, J., and Medley, P. (2018). Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040140"},{"key":"ref_15","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, The Guilford Press. [5th ed.]."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.04.015","article-title":"Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine","volume":"228","author":"Wu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0034-4257(98)00045-5","article-title":"MODTRAN cloud and multiple scattering upgrades with application to AVIRIS","volume":"65","author":"Berk","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/S0034-4257(02)00019-6","article-title":"Multitemporal analysis of urban reflectance","volume":"81","author":"Small","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/0034-4257(91)90062-B","article-title":"Radiometric rectification-toward a common radiometric response among multidate, multisensor images","volume":"35","author":"Hall","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(88)90116-2","article-title":"Radiometric scene normalization using pseudoinvariant features","volume":"26","author":"Schott","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2005.05.021","article-title":"A simple and effective radiometric correction method to improve landscape change detection across sensors and across time","volume":"98","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(00)00169-3","article-title":"Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?","volume":"75","author":"Song","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wehrhan, M., Rauneker, P., and Sommer, M. (2016). UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes--A Case Study from the CarboZALF Experimental Area. Sensors, 16.","DOI":"10.3390\/s16020255"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yang, C., Zhao, B., Song, H., Clint Hoffmann, W., Shi, Y., Zhang, D., and Zhang, G. (2017). Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras. Remote Sens., 9.","DOI":"10.3390\/rs9101054"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2017.03.011","article-title":"Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland","volume":"128","author":"Lu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4878","DOI":"10.1109\/TGRS.2017.2655365","article-title":"Commercial Off-the-Shelf Digital Cameras on Unmanned Aerial Vehicles for Multitemporal Monitoring of Vegetation Reflectance and NDVI","volume":"55","author":"Berra","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.rse.2016.09.020","article-title":"Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes","volume":"186","author":"Buffington","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_31","unstructured":"Hickson, B. (2014). Using Classification and Regression Tree and Valley Bottom Modeling Techniques to Identify Riparian Vegetation in Pinal County, Arizona. [Master\u2019s Thesis, The University of Arizona]."},{"key":"ref_32","unstructured":"Kilic, A. (2015). Google Earth Engine App for Residential Water Use and Preservation."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jones, J.W., Starbuck, M.J., and Jenkerson, C.B. (2013). Landsat Surface Reflectance Quality Assurance Extraction.","DOI":"10.3133\/tm11C7"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","first-page":"192","article-title":"The use of selected pseudo-invariant targets for the application of atmospheric correction in multi-temporal studies using satellite remotely sensed imagery","volume":"11","author":"Hadjimitsis","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Padr\u00f3, J.-C., Pons, X., Aragon\u00e9s, D., D\u00edaz-Delgado, R., Garc\u00eda, D., Bustamante, J., Pesquer, L., Domingo-Marimon, C., Gonz\u00e1lez-Guerrero, \u00d2., and Crist\u00f3bal, J. (2017). Radiometric Correction of Simultaneously Acquired Landsat-7\/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy. Remote Sens., 9.","DOI":"10.3390\/rs9121319"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2013.02.031","article-title":"Global surface reflectance products from Landsat: Assessment using coincident MODIS observations","volume":"134","author":"Feng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"243","article-title":"Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images","volume":"33","author":"Pons","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1109\/36.298020","article-title":"Processing of multitemporal landsat tm imagery to optimize extraction of forest cover change features","volume":"32","author":"Coppin","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/0034-4257(95)00152-2","article-title":"The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM","volume":"56","author":"Lenney","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7952","DOI":"10.3390\/rs6097952","article-title":"Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI, for Both Top-of-Atmosphere and Surface Reflectance: A Study in the Australian Landscape","volume":"6","author":"Flood","year":"2014","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2016.03.036","article-title":"Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000\u20132014)","volume":"185","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1931\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:55Z","timestamp":1760188315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1931"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,17]]},"references-count":44,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11161931"],"URL":"https:\/\/doi.org\/10.3390\/rs11161931","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,17]]}}}