{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T05:19:50Z","timestamp":1775279990151,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2014,9,26]],"date-time":"2014-09-26T00:00:00Z","timestamp":1411689600000},"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>The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) \u201cIndex-then-Blend\u201d (IB); and (ii) \u201cBlend-then-Index\u201d (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.<\/jats:p>","DOI":"10.3390\/rs6109213","type":"journal-article","created":{"date-parts":[[2014,9,26]],"date-time":"2014-09-26T11:27:58Z","timestamp":1411730878000},"page":"9213-9238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of \u201cIndex-then-Blend\u201d and  \u201cBlend-then-Index\u201d Approaches"],"prefix":"10.3390","volume":"6","author":[{"given":"Abdollah","family":"Jarihani","sequence":"first","affiliation":[{"name":"School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0877-8285","authenticated-orcid":false,"given":"Tim","family":"McVicar","sequence":"additional","affiliation":[{"name":"CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia"}]},{"given":"Thomas","family":"Van Niel","sequence":"additional","affiliation":[{"name":"CSIRO Land and Water, Private Bag No. 5, Wembley, WA 6913, Australia"}]},{"given":"Irina","family":"Emelyanova","sequence":"additional","affiliation":[{"name":"CSIRO Land and Water, Private Bag No. 5, Wembley, WA 6913, Australia"}]},{"given":"John","family":"Callow","sequence":"additional","affiliation":[{"name":"School of Earth and Environment, University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia"}]},{"given":"Kasper","family":"Johansen","sequence":"additional","affiliation":[{"name":"School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2014,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection","volume":"133","author":"Emelyanova","year":"2013","journal-title":"Remote Sens. Environ"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.3390\/rs4061856","article-title":"Preparing Landsat Image Time Series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia","volume":"4","author":"Bhandari","year":"2012","journal-title":"Remote Sens"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial\u2014and temporal\u2014resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1016\/j.rse.2009.05.011","article-title":"Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.rse.2011.10.014","article-title":"Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology","volume":"117","author":"Walker","year":"2012","journal-title":"Remote Sens. Environ"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2014.01.007","article-title":"Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data","volume":"144","author":"Walker","year":"2014","journal-title":"Remote Sens. Environ"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_10","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Washington, DC, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rse.2013.08.022","article-title":"Using satellite based soil moisture to quantify the water driven variability in NDVI: A case study over mainland Australia","volume":"140","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.rse.2013.10.019","article-title":"Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel","volume":"141","author":"Jamali","year":"2014","journal-title":"Remote Sens. Environ"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.3390\/rs5062795","article-title":"Mapping rubber plantations and Natural Forests in Xishuangbanna (Southwest China) using multi-spectral phenological metrics from MODIS time series","volume":"5","author":"Senf","year":"2013","journal-title":"Remote Sens"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1080\/01431161.2011.592865","article-title":"A comparison of Landsat TM and MODIS vegetation indices for estimating forage phenology in desert bighorn sheep (Ovis canadensis nelsoni) habitat in the Sonoran Desert, USA","volume":"33","author":"Sesnie","year":"2011","journal-title":"Int. J. Remote Sens"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(03)00054-3","article-title":"Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series","volume":"86","author":"Lu","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/S0034-4257(02)00037-8","article-title":"Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach","volume":"82","author":"Ceccato","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1080\/0143116021000009921","article-title":"Calculating environmental moisture for per-field discrimination of rice crops","volume":"24","author":"McVicar","year":"2003","journal-title":"Int. J. Remote Sens"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of Normalised Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chang, N.-B., and Vannah, B. (2013, January 13\u201316). Comparative Data fusion between Genetic Programing and Neural Network Models for remote sensing images of water quality monitoring. Manchester, UK.","DOI":"10.1109\/SMC.2013.182"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2010.08.005","article-title":"Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery","volume":"115","author":"Watts","year":"2011","journal-title":"Remote Sens. Environ"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/01431169508954478","article-title":"NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa","volume":"16","author":"Kerdiles","year":"1995","journal-title":"Int. J. Remote Sens"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4255","DOI":"10.3390\/rs5094255","article-title":"Mapping and evaluation of NDVI trends from synthetic time series obtained by blending Landsat and MODIS data around a coalfield on the Loess Plateau","volume":"5","author":"Tian","year":"2013","journal-title":"Remote Sens"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.jhydrol.2005.08.022","article-title":"Modelling streamflow in a large anastomosing river of the arid zone, Diamantina River, Australia","volume":"323","author":"Costelloe","year":"2006","journal-title":"J. Hydrol"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/S0022-1694(01)00498-X","article-title":"An event-based approach to the hydrology of arid zone rivers in the Channel Country of Australia","volume":"254","author":"Knighton","year":"2001","journal-title":"J. Hydrol"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/S1364-8152(03)00071-9","article-title":"Modelling the flow regime of an arid zone floodplain river, Diamantina River, Australia","volume":"18","author":"Costelloe","year":"2003","journal-title":"Environ. Modell. Softw"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.5194\/hess-11-1633-2007","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"11","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci"},{"key":"ref_28","unstructured":"Available online: http:\/\/www.lakeeyrebasin.gov.au\/about-basin\/water\/cooper-creek-region."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1071\/AR03149","article-title":"Current and potential uses of optical remote sensing in rice-based irrigation systems: A review","volume":"55","author":"McVicar","year":"2004","journal-title":"Aust. J. Agr. Res"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2004.06.003","article-title":"Determining temporal windows for crop discrimination with remote sensing: A case study in south-eastern Australia","volume":"45","author":"McVicar","year":"2004","journal-title":"Comput. Electron. Agric"},{"key":"ref_31","first-page":"257","article-title":"An evaluation of the use of atmospheric and BRDF correction to standardize Landsat data","volume":"3","author":"Fuqin","year":"2010","journal-title":"J-STARS"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3390\/rs5010083","article-title":"An operational scheme for deriving standardised surface reflectance from Landsat TM\/ETM+ and SPOT HRG imagery for Eastern Australia","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens"},{"key":"ref_33","unstructured":"Paget, M.J., and King, E.A. MODIS land data sets for the Australian region. Available online: https:\/\/remote-sensing.nci.org.au\/u39\/public\/html\/modis\/lpdaac-mosaics-cmar\/doc\/ModisLand_PagetKing_20081203-final.pdf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1109\/TGRS.2004.840643","article-title":"Terra MODIS on-orbit spatial characterization and performance","volume":"43","author":"Xiong","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_35","unstructured":"Available online: http:\/\/idlastro.gsfc.nasa.gov\/ftp\/pro\/image\/correl_optimize.pro."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"L12704","DOI":"10.1029\/2010GL043323","article-title":"Partitioning the variance between space and time","volume":"37","author":"Sun","year":"2010","journal-title":"Geophys. Res. Lett"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1109\/TGRS.2005.860205","article-title":"Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors","volume":"44","author":"Brown","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/10\/9213\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:16:21Z","timestamp":1760217381000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/10\/9213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9,26]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2014,10]]}},"alternative-id":["rs6109213"],"URL":"https:\/\/doi.org\/10.3390\/rs6109213","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,9,26]]}}}