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Publications Citing LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2

The following 39 publications cited the product LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2.

Year Citation
2021 Fornacca, D., G. Ren, and W. Xiao. 2021. Small fires, frequent clouds, rugged terrain and no training data: a methodology to reconstruct fire history in complex landscapes. International Journal of Wildland Fire. 30(2):125. https://doi.org/10.1071/WF20072
2021 Phan, D.C., T.H. Trung, V.T. Truong, T. Sasagawa, T.P.T. Vu, D.T. Bui, M. Hayashi, T. Tadono, and K.N. Nasahara. 2021. First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam. Scientific Reports. 11(1): https://doi.org/10.1038/s41598-021-89034-5
2021 Swetnam, T.L., S.R. Yool, S. Roy, and D.A. Falk. 2021. On the Use of Standardized Multi-Temporal Indices for Monitoring Disturbance and Ecosystem Moisture Stress across Multiple Earth Observation Systems in the Google Earth Engine. Remote Sensing. 13(8):1448. https://doi.org/10.3390/rs13081448
2020 Chen, B., Y. Song, B. Huang, and B. Xu. 2020. A novel method to extract urban human settlements by integrating remote sensing and mobile phone locations. Science of Remote Sensing. 1:100003. https://doi.org/10.1016/j.srs.2020.100003
2020 Hislop, S., A. Haywood, S. Jones, M. Soto-Berelov, A. Skidmore, and T.H. Nguyen. 2020. A satellite data driven approach to monitoring and reporting fire disturbance and recovery across boreal and temperate forests. International Journal of Applied Earth Observation and Geoinformation. 87:102034. https://doi.org/10.1016/j.jag.2019.102034
2020 Pereira, O.J.R., E.R. Merino, C.R. Montes, L. Barbiero, A.T. Rezende-Filho, Y. Lucas, and A.J. Melfi. 2020. Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolandia Lakes (Brazilian Pantanal). Remote Sensing. 12(7):1090. https://doi.org/10.3390/rs12071090
2019 Castelli, G., F. Castelli, and E. Bresci. 2019. Mesoclimate regulation induced by landscape restoration and water harvesting in agroecosystems of the horn of Africa. Agriculture, Ecosystems & Environment. 275:54-64. https://doi.org/10.1016/j.agee.2019.02.002
2019 Hillson, R., A. Coates, J.D. Alejandre, K.H. Jacobsen, R. Ansumana, A.S. Bockarie, U. Bangura, J.M. Lamin, and D.A. Stenger. 2019. Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa. International Journal of Health Geographics. 18(1): https://doi.org/10.1186/s12942-019-0180-1
2019 Hislop, S., S. Jones, M. Soto-Berelov, A. Skidmore, A. Haywood, and T.H. Nguyen. 2019. A fusion approach to forest disturbance mapping using time series ensemble techniques. Remote Sensing of Environment. 221:188-197. https://doi.org/10.1016/j.rse.2018.11.025
2019 Sanchez-Ruiz, S., A. Moreno-Martinez, E. Izquierdo-Verdiguier, M. Chiesi, F. Maselli, and M.A. Gilabert. 2019. Growing stock volume from multi-temporal landsat imagery through google earth engine. International Journal of Applied Earth Observation and Geoinformation. 83:101913. https://doi.org/10.1016/j.jag.2019.101913
2019 Sandera, J. and P. Stych. 2019. Change detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods. Geodetski vestnik. 63(03):379-394. https://doi.org/10.15292/geodetski-vestnik.2019.03.379-394
2019 Sultanov, M., M. Ibrakhimov, A. Akramkhanov, C. Bauer, and C. Conrad. 2019. Modelling End-of-Season Soil Salinity in Irrigated Agriculture Through Multi-temporal Optical Remote Sensing, Environmental Parameters, and In Situ Information. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 86(5-6):221-233. https://doi.org/10.1007/s41064-019-00062-3
2019 Zanotta, D.C., L.F. Sartorio, A.S. Lemos, E.G. Machado, and F.S. Dias. 2019. Automatic Methodology for Mass Detection of Past Deforestation in Brazilian Amazon. 6610-6613. https://doi.org/10.1109/IGARSS.2019.8898606
2018 Chen, B., X. Xiao, H. Ye, J. Ma, R. Doughty, X. Li, B. Zhao, Z. Wu, R. Sun, J. Dong, Y. Qin, and G. Xie. 2018. Mapping Forest and Their Spatial-Temporal Changes From 2007 to 2015 in Tropical Hainan Island by Integrating ALOS/ALOS-2 L-Band SAR and Landsat Optical Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(3):852-867. https://doi.org/10.1109/JSTARS.2018.2795595
2018 Cissell, J.R. and M.K. Steinberg. 2018. Mapping forty years of mangrove cover trends and their implications for flats fisheries in Cienaga de Zapata, Cuba. Environmental Biology of Fishes. https://doi.org/10.1007/s10641-018-0809-0
2018 Hislop, S., S. Jones, M. Soto-Berelov, A. Skidmore, A. Haywood, and T. Nguyen. 2018. Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery. Remote Sensing. 10(3):460. https://doi.org/10.3390/rs10030460
2018 Umar, M., B.L. Rhoads, and J.A. Greenberg. 2018. Use of multispectral satellite remote sensing to assess mixing of suspended sediment downstream of large river confluences. Journal of Hydrology. 556:325-338. https://doi.org/10.1016/j.jhydrol.2017.11.026
2018 Van doninck, J. and H. Tuomisto. 2018. A Landsat composite covering all Amazonia for applications in ecology and conservation. Remote Sensing in Ecology and Conservation. 4(3):197-210. https://doi.org/10.1002/rse2.77
2018 Weber, D., G. Schaepman-Strub, and K. Ecker. 2018. Predicting habitat quality of protected dry grasslands using Landsat NDVI phenology. Ecological Indicators. 91:447-460. https://doi.org/10.1016/j.ecolind.2018.03.081
2017 Adhikari, P. and K.M. de Beurs. 2017. Growth in urban extent and allometric analysis of West African cities. Journal of Land Use Science. 12(2-3):105-124. https://doi.org/10.1080/1747423X.2017.1280550
2017 Botella-Martinez, M.A. and A. Fernandez-Manso. 2017. Estudio de la severidad post-incendio en la Comunidad Valenciana comparando los indices dNBR, RdNBR y RBR a partir de imagenes Landsat 8. Revista de Teledeteccion. 33. https://doi.org/10.4995/raet.2017.7095
2017 Franch-Gras, L., E.M. Garcia-Roger, B. Franch, M.J. Carmona, and M. Serra. 2017. Quantifying unpredictability: A multiple-model approach based on satellite imagery data from Mediterranean ponds. PLOS ONE. 12(11):e0187958. https://doi.org/10.1371/journal.pone.0187958
2017 Fuchs, M., A.A. Awan, S.S. Akhtar, I. Ahmad, S. Sadiq, A. Razzak, and N. Haider. 2017. Lithological mapping with multispectral data - setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan. Journal of Mountain Science. 14(5):948-963. https://doi.org/10.1007/s11629-016-4101-5
2017 Murillo-Sandoval, P., J. Van Den Hoek, and T. Hilker. 2017. Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes. Remote Sensing. 9(2):179. https://doi.org/10.3390/rs9020179
2017 Santos, F., O. Dubovyk, and G. Menz. 2017. Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms. Remote Sensing. 9(1):68. https://doi.org/10.3390/rs9010068
2017 Schaffer-Smith, D., J.J. Swenson, B. Barbaree, and M.E. Reiter. 2017. Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds. Remote Sensing of Environment. 193:180-192. https://doi.org/10.1016/j.rse.2017.02.016
2016 Dvorett, D., C. Davis, and M. Papes. 2016. Mapping and Hydrologic Attribution of Temporary Wetlands Using Recurrent Landsat Imagery. Wetlands. 36(3):431-443. https://doi.org/10.1007/s13157-016-0752-9
2016 Gizachew, B., S. Solberg, E. Naesset, T. Gobakken, O.M. Bollandsas, J. Breidenbach, E. Zahabu, and E.W. Mauya. 2016. Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data. Carbon Balance and Management. 11(1): https://doi.org/10.1186/s13021-016-0055-8
2016 Mantas, V.M., J.C. Marques, and A.J.S.C. Pereira. 2016. A geospatial approach to monitoring impervious surfaces in watersheds using Landsat data (the Mondego Basin, Portugal as a case study). Ecological Indicators. 71:449-466. https://doi.org/10.1016/j.ecolind.2016.07.013
2016 Mitraka, Z., F. Del Frate, and F. Carbone. 2016. Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(7):3340-3350. https://doi.org/10.1109/JSTARS.2016.2522181
2015 DANELICHEN, V.H.M., M.S. BIUDES, M.C.S. VELASQUE, N.G. MACHADO, R.S.R. GOMES, G.L. VOURLITIS, and J.S. NOGUEIRA. 2015. Estimating of gross primary production in an Amazon-Cerrado transitional forest using MODIS and Landsat imagery. Anais da Academia Brasileira de Ciencias. 87(3):1545-1564. https://doi.org/10.1590/0001-3765201520140457
2015 Fetene, A., T. Hilker, K. Yeshitela, R. Prasse, W. Cohen, and Z. Yang. 2015. Detecting Trends in Landuse and Landcover Change of Nech Sar National Park, Ethiopia. Environmental Management. 57(1):137-147. https://doi.org/10.1007/s00267-015-0603-0
2015 Mitraka, Z., F. Del Frate, and F. Carbone. 2015. Spectral unmixing of urban Landsat imagery by means of neural networks. 1-4. https://doi.org/10.1109/JURSE.2015.7120463
2015 Zhao, Y., X. Chen, Z. Cui, and D.B. Lobell. 2015. Using satellite remote sensing to understand maize yield gaps in the North China Plain. Field Crops Research. 183:31-42. https://doi.org/10.1016/j.fcr.2015.07.004
2014 Byrd, K.B., J.L. O'Connell, S. Di Tommaso, and M. Kelly. 2014. Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation. Remote Sensing of Environment. 149:166-180. https://doi.org/10.1016/j.rse.2014.04.003
2014 Hajj, M., N. Baghdadi, G. Belaud, M. Zribi, B. Cheviron, D. Courault, O. Hagolle, and F. Charron. 2014. Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data. Remote Sensing. 6(10):10002-10032. https://doi.org/10.3390/rs61010002
2014 Wilson, C.R. and D.G. Brown. 2014. Change in visible impervious surface area in southeastern Michigan before and after the "Great Recession:" spatial differentiation in remotely sensed land-cover dynamics. Population and Environment. 36(3):331-355. https://doi.org/10.1007/s11111-014-0219-y
2013 Parece, T. and J. Campbell. 2013. Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography. Remote Sensing. 5(10):4942-4960. https://doi.org/10.3390/rs5104942
2013 Wilson C.R. (2013) Evaluating Satellite-Observed Changes in Impervious Surface Cover in Relation to Economic Changes and Spatially Variable Socioeconomic Conditions in Census Data in Southeastern Michigan. University of Michigan, Department of Natural Resources and Environment.