{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:45:01Z","timestamp":1779907501018,"version":"3.53.1"},"reference-count":38,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000203","name":"USGS EROS","doi-asserted-by":"publisher","award":["G19AS00001"],"award-info":[{"award-number":["G19AS00001"]}],"id":[{"id":"10.13039\/100000203","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The expanded Trend-to-Trend (T2T) cross-calibration technique has the potential to calibrate two sensors in much less time and provides trends on a daily assessment basis. The trend obtained from the expanded technique aids in evaluating the differences between satellite sensors. Therefore, this technique was validated with several trusted cross-calibration techniques to evaluate its accuracy. Initially, the expanded T2T technique was validated with three independent RadCaTS RRV, DIMITRI-PICS, and APICS models, and results show a 1% average difference with other models over all bands. Further, this technique was validated with other SDSU techniques to calibrate the newly launched satellite Landsat 9 with 8, demonstrating good agreement in all bands within 0.5%. This technique was also validated for Terra MODIS and ETM+, showing consistency within 1% for all bands compared to four PICS sites. Additionally, the T2T technique was applied to a global scale using EPICS Global sites. The expanded T2T cross-calibration gain result obtained for Landsat 8 versus Landsat 7\/8, Sentinel 2A\/2B, and Terra\/Aqua MODIS presented that the difference between these pairs was within 0.5\u20131% for most of the spectral bands. Total uncertainty obtained for these pairs of sensors using Monte Carlo Simulation varies from 2.5\u20134% for all bands except for SWIR2 bands, which vary up to 5%. The difference between EPICS Global and EPICS North Africa was calculated using the ratio of trend gain; the difference among them was within 0.5\u20131% difference on average for all the sensors and bands within a 0.5% uncertainty level difference.<\/jats:p>","DOI":"10.3390\/rs14246216","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:23:49Z","timestamp":1670556229000},"page":"6216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Validation of Expanded Trend-to-Trend Cross-Calibration Technique and Its Application to Global Scale"],"prefix":"10.3390","volume":"14","author":[{"given":"Ramita","family":"Shah","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, South Dakota State University (SDSU), Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-4768","authenticated-orcid":false,"given":"Larry","family":"Leigh","sequence":"additional","affiliation":[{"name":"Imaging Center, Image Processing Laboratory, Department of Electrical Engineering and Computer Science, South Dakota State University (SDSU), Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morakot","family":"Kaewmanee","sequence":"additional","affiliation":[{"name":"Imaging Center, Image Processing Laboratory, Department of Electrical Engineering and Computer Science, South Dakota State University (SDSU), Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2895-8697","authenticated-orcid":false,"given":"Cibele Teixeira","family":"Pinto","sequence":"additional","affiliation":[{"name":"Imaging Center, Image Processing Laboratory, Department of Electrical Engineering and Computer Science, South Dakota State University (SDSU), Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2011.12.001","article-title":"Impact of sensor degradation on the MODIS NDVI time series","volume":"119","author":"Wang","year":"2012","journal-title":"Remote Sens. 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