The following 46 publications cited the product LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2.
Year | Citation |
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2023 | BHARTENDU SAJAN, SHRUTI KANGA, SURAJ KUMAR SINGH, VARUN NARAYAN MISHRA, and BOJAN DURIN. 2023. Spatial variations of LST and NDVI in Muzaffarpur district, Bihar using Google earth engine (GEE) during 1990-2020. Journal of Agrometeorology. 25(2):262-267. https://doi.org/10.54386/jam.v25i2.2155 |
2021 | Belenok, V., T. Noszczyk, L. Hebryn-Baidy, and S. Kryachok. 2021. Investigating anthropogenically transformed landscapes with remote sensing. Remote Sensing Applications: Society and Environment. 24:100635. https://doi.org/10.1016/j.rsase.2021.100635 |
2021 | Eneyew, B.G. and W.W. Assefa. 2021. Anthropogenic effect on wetland biodiversity in Lake Tana Region: A case of Infranz Wetland, Northwestern Ethiopia. Environmental and Sustainability Indicators. 12:100158. https://doi.org/10.1016/j.indic.2021.100158 |
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 | Kharuk, V.I., S.T. Im, and I.y.A. Petrov. 2021. Alpine ecotone in the Siberian Mountains: vegetation response to warming. Journal of Mountain Science. 18(12):3099-3108. https://doi.org/10.1007/s11629-021-6876-2 |
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 | Popov, M., S. Stankevich, A. Kozlova, I. Piestova, M. Lubskiy, O. Titarenko, M. Svideniuk, A. Andreiev, A. Lysenko, and S.K. Singh. 2021. Long-Term Satellite Data Time Series Analysis for Land Degradation Mapping to Support Sustainable Land Management in Ukraine. 165-189. https://doi.org/10.1007/978-981-16-4768-0_11 |
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 | Milewski, R., S. Chabrillat, and R. Behling. 2017. Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data. Remote Sensing. 9(2):170. https://doi.org/10.3390/rs9020170 |
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 | Collins, C.D.H., M.A. Kautz, R. Tiller, S. Lohani, G. Ponce-Campos, J. Hottenstein, and L.J. Metz. 2015. Development of an integrated multiplatform approach for assessing brush management conservation efforts in semiarid rangelands. Journal of Applied Remote Sensing. 9(1):096057. https://doi.org/10.1117/1.JRS.9.096057 |
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. |