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Publications Citing Model Archive

The following 134 publications cited the Model Archive project.

YearCitationDataset or Project
2024Donmez, C., M. Sahingoz, C. Paul, A. Cilek, C. Hoffmann, S. Berberoglu, H. Webber, and K. Helming. 2024. Climate change causes spatial shifts in the productivity of agricultural long-term field experiments. European Journal of Agronomy. 155:127121. https://doi.org/10.1016/j.eja.2024.127121Biome-BGC: Modeling Effects of Disturbance and Climate (Thornton et al. 2002)
2024Gustafson, E.J., B.R. Sturtevant, B.R. Miranda, and M.J. Duveneck. 2024. Overcoming conceptual hurdles to accurately represent trees as cohorts in forest landscape models. Ecological Modelling. 490:110657. https://doi.org/10.1016/j.ecolmodel.2024.110657LINKAGES: An Individual-based Forest Ecosystem Biogeochemistry Model
2023BHARTENDU 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.v25i2.2155LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2023Bosela, M., Á. Rubio-Cuadrado, P. Marcis, K. Mergani?ová, P. Fleischer, D.I. Forrester, E. Uhl, A. Avdagi?, M. Bellan, K. Bielak, F. Bravo, L. Coll, K. Cseke, M. del Rio, L. Dinca, L. Dobor, S. Drozdowski, F. Giammarchi, E. Gömöryová, A. Ibrahimspahi?, M. Kašanin-Grubin, M. Klop?i?, V. Kurylyak, F. Montes, M. Pach, R. Ruiz-Peinado, J. Skrzyszewski, B. Stajic, D. Stojanovic, M. Svoboda, G. Tonon, S. Versace, S. Mitrovic, T. Zlatanov, H. Pretzsch, and R. Tognetti. 2023. Empirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: A multimodel approach. Science of The Total Environment. 888:164123. https://doi.org/10.1016/j.scitotenv.2023.164123Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2023Sedighi, A., S. Hamzeh, M.K. Firozjaei, H.V. Goodarzi, and A.A. Naseri. 2023. Comparative Analysis of Multispectral and Hyperspectral Imagery for Mapping Sugarcane Varieties. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 91(6):453-470. https://doi.org/10.1007/s41064-023-00255-xLEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2022Park, E., H. Loc Ho, D. Van Binh, S. Kantoush, D. Poh, E. Alcantara, S. Try, and Y.N. Lin. 2022. Impacts of agricultural expansion on floodplain water and sediment budgets in the Mekong River. Journal of Hydrology. 605:127296. https://doi.org/10.1016/j.jhydrol.2021.127296LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2021Belenok, 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.2021.100635LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2021Crockett, E.T.H., S. Vennin, J. Botzas-Coluni, G. Larocque, and E.M. Bennett. 2021. Bright spots of carbon storage in temperate forests. Journal of Applied Ecology. 58(12):3012-3022. https://doi.org/10.1111/1365-2664.14042Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2021Eneyew, 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.2021.100158LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2021Fornacca, 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/WF20072LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2021Kharuk, 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/s11629-021-6876-2LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2021Ostrogovi? Sever, M.Z., Z. Barcza, D. Hidy, A. Kern, D. Dimoski, S. Miko, O. Hasan, B. Grahovac, and H. Marjanovi?. 2021. Evaluation of the Terrestrial Ecosystem Model Biome-BGCMuSo for Modelling Soil Organic Carbon under Different Land Uses. Land. 10(9):968. https://doi.org/10.3390/land10090968Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2021Phan, 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/s41598-021-89034-5LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2021Popov, 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_11LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2021Swetnam, 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/rs13081448LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2020Chen, 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.2020.100003LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2020Hislop, 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.2019.102034LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2020Pereira, 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/rs12071090LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2020Reeves, M.C., B.B. Hanberry, H. Wilmer, N.E. Kaplan, and W.K. Lauenroth. 2020. An Assessment of Production Trends on the Great Plains from 1984 to 2017. Rangeland Ecology & Management. https://doi.org/10.1016/j.rama.2020.01.011CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)
2019Castelli, 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.2019.02.002LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2019Chamberlain, C.J., B.I. Cook, I. Garcia de Cortazar-Atauri, and E.M. Wolkovich. 2019. Rethinking false spring risk. Global Change Biology. https://doi.org/10.1111/gcb.14642Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2019Farinosi, F., M.E. Arias, E. Lee, M. Longo, F.F. Pereira, A. Livino, P.R. Moorcroft, and J. Briscoe. 2019. Future Climate and Land Use Change Impacts on River Flows in the Tapajós Basin in the Brazilian Amazon. Earth's Future. 7(8):993-1017. https://doi.org/10.1029/2019EF001198Ecosystem Demography Model: Scaling Vegetation Dynamics Across South America
2019Hillson, 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/s12942-019-0180-1LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2019Hislop, 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.2018.11.025LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2019Keller, A.A. and J. Fox. 2019. Giving credit to reforestation for water quality benefits. PLOS ONE. 14(6):e0217756. https://doi.org/10.1371/journal.pone.0217756Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2019Li, S., W. Yuan, P. Ciais, N. Viovy, A. Ito, B. Jia, and D. Zhu. 2019. Benchmark estimates for aboveground litterfall data derived from ecosystem models. Environmental Research Letters. 14(8):084020. https://doi.org/10.1088/1748-9326/ab2ee4Integrated Biosphere Simulator Model (IBIS), Version 2.5
2019Mergani?ová, K., J. Mergani?, A. Lehtonen, G. Vacchiano, M.Z.O. Sever, A.L.D. Augustynczik, R. Grote, I. Kyselová, A. Mäkelä, R. Yousefpour, J. Krejza, A. Collalti, and C.P.O. Reyer. 2019. Forest carbon allocation modelling under climate change. Tree Physiology. 39(12):1937-1960. https://doi.org/10.1093/treephys/tpz105Ecosystem Demography Model: Scaling Vegetation Dynamics Across South America
2019Rasool, Q.Z., J.O. Bash, and D.S. Cohan. 2019. Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1. Geoscientific Model Development. 12(2):849-878. https://doi.org/10.5194/gmd-12-849-2019BDSNP Module for Improved Soil NO Emission Estimates for CMAQ Model, Conterminous USA
2019Rasool, Q.Z., J.O. Bash, and D.S. Cohan. 2019. Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1. Geoscientific Model Development. 12(2):849-878. https://doi.org/10.5194/gmd-12-849-2019Mechanistic Module for Soil Nitrogen Emissions for CMAQ Model, North America, 2011
2019Sanchez-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.2019.101913LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2019Sandera, 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.2019.03.379-394LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2019Sultanov, 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/s41064-019-00062-3LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2019Zanotta, 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.2019.8898606LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2018Chen, 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.2018.2795595LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2018Chen, 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.2018.2795595LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2018Cissell, 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/s10641-018-0809-0LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2018Heimhuber, V., M.G. Tulbure, and M. Broich. 2018. Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics. Remote Sensing of Environment. 211:307-320. https://doi.org/10.1016/j.rse.2018.04.016LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2018Hislop, 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/rs10030460LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2018Umar, 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.2017.11.026LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2018Van 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/rse2.77LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2018Wang, Y., B. Jia, and Z. Xie. 2018. The Effects of Dynamic Root Distribution on Land-Atmosphere Carbon and Water Fluxes in the Community Earth System Model (CESM1.2.0). Forests. 9(4):172. https://doi.org/10.3390/f9040172Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies
2018Weber, 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.2018.03.081LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Adhikari, 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.2017.1280550LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Botella-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.2017.7095LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Bullock, E.L., S. Fagherazzi, W. Nardin, P. Vo-Luong, P. Nguyen, and C.E. Woodcock. 2017. Temporal patterns in species zonation in a mangrove forest in the Mekong Delta, Vietnam, using a time series of Landsat imagery. Continental Shelf Research. 147:144-154. https://doi.org/10.1016/j.csr.2017.07.007LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2017Chernetskiy, M., N. Gobron, J. Gmez?Dans, P. Lewis and C.C. Schmullius2017. Earth Observation Land Data Assimilation System (EO?LDAS) Regularization Constraints over Barrax Site. Earth Observation for Land and Emergency Monitoring. LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2017Franch-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.0187958LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Fuchs, 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/s11629-016-4101-5LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Hanan, E.J., C.N. Tague, and J.P. Schimel. 2017. Nitrogen cycling and export in California chaparral: the role of climate in shaping ecosystem responses to fire. Ecological Monographs. 87(1):76-90. https://doi.org/10.1002/ecm.1234Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2017Hofmann, S., J. Everaars, O. Schweiger, M. Frenzel, L. Bannehr, and A.F. Cord. 2017. Modelling patterns of pollinator species richness and diversity using satellite image texture. PLOS ONE. 12(10):e0185591. https://doi.org/10.1371/journal.pone.0185591LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2017Huang, C., H.S. He, T.J. Hawbaker, Y. Liang, P. Gong, Z. Wu, and Z. Zhu. 2017. A coupled modeling framework for predicting ecosystem carbon dynamics in boreal forests. Environmental Modelling & Software. 93:332-343. https://doi.org/10.1016/j.envsoft.2017.03.009LINKAGES: An Individual-based Forest Ecosystem Biogeochemistry Model
2017Khare, S., S.K. Ghosh, H. Latifi, S. Vijay, and T. Dahms. 2017. Seasonal-based analysis of vegetation response to environmental variables in the mountainous forests of Western Himalaya using Landsat 8 data. International Journal of Remote Sensing. 38(15):4418-4442. https://doi.org/10.1080/01431161.2017.1320450LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2017Martinez, S., E. Chuvieco, I. Aguado, and J. Salas. 2017. Burn severity and regeneration in large forest fires: an analysis from Landsat time series. Revista de Teledeteccion. 17. https://doi.org/10.4995/raet.2017.7182LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2017Milewski, 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/rs9020170LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Murillo-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/rs9020179LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Pereira, F.F., F. Farinosi, M.E. Arias, E. Lee, J. Briscoe, and P.R. Moorcroft. 2017. Technical note: A hydrological routing scheme for the Ecosystem Demography model (ED2+R) tested in the Tapajos River basin in the Brazilian Amazon. Hydrology and Earth System Sciences. 21(9):4629-4648. https://doi.org/10.5194/hess-21-4629-2017Ecosystem Demography Model: Scaling Vegetation Dynamics Across South America
2017Samal, N.R., W.M. Wollheim, S. Zuidema, R.J. Stewart, Z. Zhou, M.M. Mineau, M.E. Borsuk, K.H. Gardner, S. Glidden, T. Huang, D.A. Lutz, G. Mavrommati, A.M. Thorn, C.P. Wake, and M. Huber. 2017. A coupled terrestrial and aquatic biogeophysical model of the Upper Merrimack River watershed, New Hampshire, to inform ecosystem services evaluation and management under climate and land-cover change. Ecology and Society. 22(4):. https://doi.org/10.5751/ES-09662-220418PnET Models: Carbon, Nitrogen, Water Dynamics in Forest Ecosystems (Vers. 4 and 5)
2017Santos, 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/rs9010068LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Schaffer-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.2017.02.016LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2017Verma, S., J. Marshall, C. Gerbig, C. Rodenbeck, and K.U. Totsche. 2017. The constraint of CO<sub>2</sub> measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing. Atmospheric Chemistry and Physics. 17(9):5665-5675. https://doi.org/10.5194/acp-17-5665-2017Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2016Dijak, W.D., B.B. Hanberry, J.S. Fraser, H.S. He, W.J. Wang, and F.R. Thompson. 2016. Revision and application of the LINKAGES model to simulate forest growth in central hardwood landscapes in response to climate change. Landscape Ecology. 32(7):1365-1384. https://doi.org/10.1007/s10980-016-0473-8LINKAGES: An Individual-based Forest Ecosystem Biogeochemistry Model
2016Dvorett, 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/s13157-016-0752-9LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2016Gizachew, 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/s13021-016-0055-8LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2016Hamzeh, S., A.A. Naseri, S.K. AlaviPanah, H. Bartholomeus, and M. Herold. 2016. Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields. International Journal of Applied Earth Observation and Geoinformation. 52:412-421. https://doi.org/10.1016/j.jag.2016.06.024LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2016Hidy, D., Z. Barcza, H. Marjanovic, M.Z. Ostrogovic Sever, L. Dobor, G. Gelybo, N. Fodor, K. Pinter, G. Churkina, S. Running, P. Thornton, G. Bellocchi, L. Haszpra, F. Horvath, A. Suyker, and Z. Nagy. 2016. Terrestrial ecosystem process model Biome-BGCMuSo v4.0: summary of improvements and new modeling possibilities. Geoscientific Model Development. 9(12):4405-4437. https://doi.org/10.5194/gmd-9-4405-2016Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2016Jepsen, S.M., T.C. Harmon, and Y. Shi. 2016. Watershed model calibration to the base flow recession curve with and without evapotranspiration effects. Water Resources Research. 52(4):2919-2933. https://doi.org/10.1002/2015WR017827Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies
2016Jin, W., H.S. He, and F.R. Thompson. 2016. Are more complex physiological models of forest ecosystems better choices for plot and regional predictions?. Environmental Modelling & Software. 75:1-14. https://doi.org/10.1016/j.envsoft.2015.10.004LINKAGES: An Individual-based Forest Ecosystem Biogeochemistry Model
2016Mantas, 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.2016.07.013LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2016MILES, E.S., I.C. WILLIS, N.S. ARNOLD, J. STEINER, and F. PELLICCIOTTI. 2016. Spatial, seasonal and interannual variability of supraglacial ponds in the Langtang Valley of Nepal, 1999-2013. Journal of Glaciology. 63(237):88-105. https://doi.org/10.1017/jog.2016.120LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2016Mitraka, 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.2016.2522181LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2016Molina, J., P. Lugo, J. Arias, J. Guaje, H. Castro, C. Costa, E. Cabrera, and M. Sanabria. 2016. Atmospheric correction matching to theoretical forest signature applied to different colombian regions. 1-7. https://doi.org/10.1109/STSIVA.2016.7743313LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2016Rasool, Q.Z., R. Zhang, B. Lash, D.S. Cohan, E.J. Cooter, J.O. Bash, and L.N. Lamsal. 2016. Enhanced representation of soil NO emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2. Geoscientific Model Development. 9(9):3177-3197. https://doi.org/10.5194/gmd-9-3177-2016BDSNP Module for Improved Soil NO Emission Estimates for CMAQ Model, Conterminous USA
2016Sandor, R., Z. Barcza, D. Hidy, E. Lellei-Kovacs, S. Ma, and G. Bellocchi. 2016. Modelling of grassland fluxes in Europe: Evaluation of two biogeochemical models. Agriculture, Ecosystems & Environment. 215:1-19. https://doi.org/10.1016/j.agee.2015.09.001Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2016Valencia, G.M., J.A. Anaya, and F.J. Caro-Lopera. 2016. Implementacion y evaluacion del modelo Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS): estudio de caso en los Andes colombianos. Revista de Teledeteccion. 83. https://doi.org/10.4995/raet.2016.3582LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2016Wang, W.J., H.S. He, F.R. Thompson, J.S. Fraser, and W.D. Dijak. 2016. Changes in forest biomass and tree species distribution under climate change in the northeastern United States. Landscape Ecology. 32(7):1399-1413. https://doi.org/10.1007/s10980-016-0429-zLINKAGES: An Individual-based Forest Ecosystem Biogeochemistry Model
2016Zipper, S.C., J. Schatz, A. Singh, C.J. Kucharik, P.A. Townsend, and S.P. Loheide. 2016. Urban heat island impacts on plant phenology: intra-urban variability and response to land cover. Environmental Research Letters. 11(5):054023. https://doi.org/10.1088/1748-9326/11/5/054023LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2015Camarero, J., M. Franquesa, and G. Sanguesa-Barreda. 2015. Timing of Drought Triggers Distinct Growth Responses in Holm Oak: Implications to Predict Warming-Induced Forest Defoliation and Growth Decline. Forests. 6(12):1576-1597. https://doi.org/10.3390/f6051576LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2015Collins, 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.9.096057LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2015DANELICHEN, 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/10.1590/0001-3765201520140457LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2015Dihkan, M., F. Karsli, A. Guneroglu, and N. Guneroglu. 2015. Evaluation of surface urban heat island (SUHI) effect on coastal zone: The case of Istanbul Megacity. Ocean & Coastal Management. 118:309-316. https://doi.org/10.1016/j.ocecoaman.2015.03.008LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2015Fetene, 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/s00267-015-0603-0LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2015Gartner, P. and B. Kleinschmit. 2015. Monitoring forest recovery with change metrics derived from Landsat time series stacks. 1-3. https://doi.org/10.1109/Multi-Temp.2015.7245807LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2015Gebhardt, S., P. Maeda, T. Wehrmann, J. Argumedo Espinoza, and M. Schmidt. 2015. A proper Land Cover and Forest Type Classification Scheme for Mexico. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XL-7/W3:383-390. https://doi.org/10.5194/isprsarchives-XL-7-W3-383-2015LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2015Guneroglu, A. 2015. Coastal changes and land use alteration on Northeastern part of Turkey. Ocean & Coastal Management. 118:225-233. https://doi.org/10.1016/j.ocecoaman.2015.06.019LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2015Mitraka, 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.2015.7120463LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2015Wang, W.J., H.S. He, F.R. Thompson, J.S. Fraser, and W.D. Dijak. 2015. Landscape- and regional-scale shifts in forest composition under climate change in the Central Hardwood Region of the United States. Landscape Ecology. 31(1):149-163. https://doi.org/10.1007/s10980-015-0294-1LINKAGES: An Individual-based Forest Ecosystem Biogeochemistry Model
2015Zhao, 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.2015.07.004LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2014Byrd, 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.2014.04.003LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2014Hajj, 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/rs61010002LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2014Jiang, D., M. Hao, J. Fu, Q. Wang, Y. Huang, and X. Fu. 2014. Assessment of the GHG Reduction Potential from Energy Crops Using a Combined LCA and Biogeochemical Process Models: A Review. The Scientific World Journal. 2014:1-10. https://doi.org/10.1155/2014/537826PnET Models: Carbon, Nitrogen, Water Dynamics in Forest Ecosystems (Vers. 4 and 5)
2014Keenan, T.F., J. Gray, M.A. Friedl, M. Toomey, G. Bohrer, D.Y. Hollinger, J.W. Munger, J. O'Keefe, H.P. Schmid, I.S. Wing, B. Yang, and A.D. Richardson. 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change. 4(7):598-604. https://doi.org/10.1038/nclimate2253Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2014Lee, Z.M. and T.M. Schmidt. 2014. Bacterial growth efficiency varies in soils under different land management practices. Soil Biology and Biochemistry. 69:282-290. https://doi.org/10.1016/j.soilbio.2013.11.012CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)
2014Liu, Y., S. Goodrick, and W. Heilman. 2014. Wildland fire emissions, carbon, and climate: Wildfire–climate interactions. Forest Ecology and Management. 317:80-96. https://doi.org/10.1016/j.foreco.2013.02.020Integrated Biosphere Simulator Model (IBIS), Version 2.5
2014Murdoch, P.S., M. McHale, and J. Baron. 2014. Reflections on a Vision for Integrated Research and Monitoring After 15 Years. Aquatic Geochemistry. 20(2-3):363-380. https://doi.org/10.1007/s10498-013-9222-7Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2014Wilson, 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-yLEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2013Badawy, B., C. Rodenbeck, M. Reichstein, N. Carvalhais, and M. Heimann. 2013. Technical Note: The Simple Diagnostic Photosynthesis and Respiration Model (SDPRM). Biogeosciences. 10(10):6485-6508. https://doi.org/10.5194/bg-10-6485-2013Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2013Choi, H.I., X.Z. Liang, and P. Kumar. 2013. A Conjunctive Surface-Subsurface Flow Representation for Mesoscale Land Surface Models. Journal of Hydrometeorology. 14(5):1421-1442. https://doi.org/10.1175/JHM-D-12-0168.1Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies
2013Cunha, A.P.M.A., R.C.S. Alvala, G. Sampaio, M.H. Shimizu, and M.H. Costa. 2013. Calibration and Validation of the Integrated Biosphere Simulator (IBIS) for a Brazilian Semiarid Region. Journal of Applied Meteorology and Climatology. 52(12):2753-2770. https://doi.org/10.1175/JAMC-D-12-0190.1Integrated Biosphere Simulator Model (IBIS), Version 2.5
2013Keenan, T.F., D.Y. Hollinger, G. Bohrer, D. Dragoni, J.W. Munger, H.P. Schmid, and A.D. Richardson. 2013. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature. 499(7458):324-327. https://doi.org/10.1038/nature12291Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2013Laurent, O., J. Wu, L. Li, and C. Milesi. 2013. Green spaces and pregnancy outcomes in Southern California. Health & Place. 24:190-195. https://doi.org/10.1016/j.healthplace.2013.09.016LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2013Parece, 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/rs5104942LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2013Seidl, R., C.S. Eastaugh, K. Kramer, M. Maroschek, C. Reyer, J. Socha, G. Vacchiano, T. Zlatanov, and H. Hasenauer. 2013. Scaling issues in forest ecosystem management and how to address them with models. European Journal of Forest Research. 132(5-6):653-666. https://doi.org/10.1007/s10342-013-0725-yBiome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2013Stoy, P.C., M.C. Dietze, A.D. Richardson, R. Vargas, A.G. Barr, R.S. Anderson, M.A. Arain, I.T. Baker, T.A. Black, J.M. Chen, R.B. Cook, C.M. Gough, R.F. Grant, D.Y. Hollinger, R.C. Izaurralde, C.J. Kucharik, P. Lafleur, B.E. Law, S. Liu, E. Lokupitiya, Y. Luo, J.W. Munger, C. Peng, B. Poulter, D.T. Price, D.M. Ricciuto, W.J. Riley, A.K. Sahoo, K. Schaefer, C.R. Schwalm, H. Tian, H. Verbeeck, and E. Weng. 2013. Evaluating the agreement between measurements and models of net ecosystem exchange at different times and timescales using wavelet coherence: an example using data from the North American Carbon Program Site-Level Interim Synthesis. Biogeosciences. 10(11):6893-6909. https://doi.org/10.5194/bg-10-6893-2013Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2013Wilson 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.LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code
2013Wilson 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.LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2
2013Zhang, C., C. Li, G. Luo, and X. Chen. 2013. Modeling plant structure and its impacts on carbon and water cycles of the Central Asian arid ecosystem in the context of climate change. Ecological Modelling. 267:158-179. https://doi.org/10.1016/j.ecolmodel.2013.06.008Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2012Badawy, B., C. Rodenbeck, M. Reichstein, N. Carvalhais, and M. Heimann. 2012. Technical Note: The Simple Diagnostic Photosynthesis and Respiration Model (SDPRM). Biogeosciences Discussions. 9(10):15127-15174. https://doi.org/10.5194/bgd-9-15127-2012Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2012Blackledge, J., Coyle, E., McCoy, N., Kearney, D., Sunderland, K., & Woolmington, T.; (2012). Analysis of Wind Velocity and the Quantification of Wind Turbulence in Rural and Urban Environments using the L_vy Index and Fractal Dimension. ISAST Transactions on Computers and Intelligent Systems. 4 (0). Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies
2012Hidy, D., Z. Barcza, L. Haszpra, G. Churkina, K. Pinter, and Z. Nagy. 2012. Development of the Biome-BGC model for simulation of managed herbaceous ecosystems. Ecological Modelling. 226:99-119. https://doi.org/10.1016/j.ecolmodel.2011.11.008Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2012Keenan, T.F., I. Baker, A. Barr, P. Ciais, K. Davis, M. Dietze, D. Dragoni, C.M. Gough, R. Grant, D. Hollinger, K. Hufkens, B. Poulter, H. McCaughey, B. Raczka, Y. Ryu, K. Schaefer, H. Tian, H. Verbeeck, M. Zhao, and A.D. Richardson. 2012. Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange. Global Change Biology. 18(6):1971-1987. https://doi.org/10.1111/j.1365-2486.2012.02678.xBiome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2012Muraoka, H., H.M. Noda, S. Nagai, T. Motohka, T.M. Saitoh, K.N. Nasahara, and N. Saigusa. 2012. Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest. Journal of Plant Ecology. 6(5):393-407. https://doi.org/10.1093/jpe/rts037Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies
2012Schaefer, K., C.R. Schwalm, C. Williams, M.A. Arain, A. Barr, J.M. Chen, K.J. Davis, D. Dimitrov, T.W. Hilton, D.Y. Hollinger, E. Humphreys, B. Poulter, B.M. Raczka, A.D. Richardson, A. Sahoo, P. Thornton, R. Vargas, H. Verbeeck, R. Anderson, I. Baker, T.A. Black, P. Bolstad, J. Chen, P.S. Curtis, A.R. Desai, M. Dietze, D. Dragoni, C. Gough, R.F. Grant, L. Gu, A. Jain, C. Kucharik, B. Law, S. Liu, E. Lokipitiya, H.A. Margolis, R. Matamala, J.H. McCaughey, R. Monson, J.W. Munger, W. Oechel, C. Peng, D.T. Price, D. Ricciuto, W.J. Riley, N. Roulet, H. Tian, C. Tonitto, M. Torn, E. Weng, and X. Zhou. 2012. A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis. Journal of Geophysical Research: Biogeosciences. 117(G3):n/a-n/a. https://doi.org/10.1029/2012JG001960Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2012Zhang, C., C. Li, X. Chen, G. Luo, L. Li, X. Li, Y. Yan, and H. Shao. 2012. A spatial-explicit dynamic vegetation model that couples carbon, water, and nitrogen processes for arid and semiarid ecosystems. Journal of Arid Land. 5(1):102-117. https://doi.org/10.1007/s40333-013-0146-2Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2011Badawy, B.; (2011). Quantifying carbon processes of the terrestrial biosphere in a global atmospheric inversion based on atmospheric mixing ratio, remote sensing and meteorological data. International Max Planck Research School on Earth System Modelling. Ph.D. (Technical Report 21). Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2011HU Bo,,SUN Rui,CHEN Yong-jun,FENG Li-chao,SUN Liang; (2011). Estimation of the Net Ecosystem Productivity in Huang-Huai Hai Region Combining with Biome-BGC Model and Remote Sensing Data. Journal of Natural Resources. Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2011Rueda Restrepo, J.A., Ciencias Forestales, (2011). Modelacion de la din_mica del carbono en la biomasa del bosque del norte de los Andes usando el modelo CENTURY. Thesis. http://www.bdigital.unal.edu.co/4609/1/98491011._2011.pdfCENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)
2011Wang, Yeqiao, ed. (2011) Remoting Sensing of Protected Lands.CRC Press.. ISBN:143984187X,9781439841877Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2010Fang, X., T. Chua, K. Schmidt-Rohr, and M.L. Thompson. 2010. Quantitative 13C NMR of whole and fractionated Iowa Mollisols for assessment of organic matter composition. Geochimica et Cosmochimica Acta. 74(2):584-598. https://doi.org/10.1016/j.gca.2009.10.008CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)
2010Schwalm, C.R., C.A. Williams, K. Schaefer, R. Anderson, M.A. Arain, I. Baker, A. Barr, T.A. Black, G. Chen, J.M. Chen, P. Ciais, K.J. Davis, A. Desai, M. Dietze, D. Dragoni, M.L. Fischer, L.B. Flanagan, R. Grant, L. Gu, D. Hollinger, R.C. Izaurralde, C. Kucharik, P. Lafleur, B.E. Law, L. Li, Z. Li, S. Liu, E. Lokupitiya, Y. Luo, S. Ma, H. Margolis, R. Matamala, H. McCaughey, R.K. Monson, W.C. Oechel, C. Peng, B. Poulter, D.T. Price, D.M. Riciutto, W. Riley, A.K. Sahoo, M. Sprintsin, J. Sun, H. Tian, C. Tonitto, H. Verbeeck, and S.B. Verma. 2010. A model-data intercomparison of CO2exchange across North America: Results from the North American Carbon Program site synthesis. Journal of Geophysical Research. 115:. https://doi.org/10.1029/2009jg001229Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2009Gu, L.; (2009). Aqueous and gaseous nitrogen losses induced by fertilizer application. Lawrence Berkeley Technical Report.http://www.escholarship.org/uc/item/1p49x2bjBiome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2009Scott Goetz, Forrest Melton, Weile Wang, Cristina Milesi, David Theobald; (2009) Modeling Strategies for Adaption to Coupled Climate and Land Use Change in the United States. Proc/ of the World Bank 2009 Marseille Cities and Climate Change Urban Research Symposium, World Bank.. https://www.researchgate.net/publication/228480954_Modeling_Strategies_for_Adaptation_to_Coupled_Climate_and_Land_Use_Change_in_the_United_StatesBiome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2009Tague, C., K. Heyn, and L. Christensen. 2009. Topographic controls on spatial patterns of conifer transpiration and net primary productivity under climate warming in mountain ecosystems. Ecohydrology. 2(4):541-554. https://doi.org/10.1002/eco.88Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2008Bai, Z. G., Dent, D. L., Olsson, L., Schaepman, M. E.; (2008). Global assessment of land degradation and improvement 1: identification by remote sensing. ISBN: Report 2008.01, ISRIC - World Soil Information.Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2008Bruno Locatelli, Hety Herawati, Maria Brockhaus, Monica Idinoba, Markku Kanninen (2008) Methods and Tools for Assessing the Vulnerability of Forests and People to Climate Change.Center for International Forestery Research (CIFOR) and Agricultral Research Center for International Development (CIRAD). Working Paper No. 43. http://www.ibcperu.org/doc/isis/10815.pdfIntegrated Biosphere Simulator Model (IBIS), Version 2.5
2008ZHANG, Y., Q. YU, J. JIANG, and Y. TANG. 2008. Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau. Global Change Biology. 14(4):757-767. https://doi.org/10.1111/j.1365-2486.2008.01538.xIntegrated Biosphere Simulator Model (IBIS), Version 2.5
2008Zhang, Y., S. Wang, A.G. Barr, and T.A. Black. 2008. Impact of snow cover on soil temperature and its simulation in a boreal aspen forest. Cold Regions Science and Technology. 52(3):355-370. https://doi.org/10.1016/j.coldregions.2007.07.001Integrated Biosphere Simulator Model (IBIS), Version 2.5
2007Rokityanskiy, D., P.C. Benitez, F. Kraxner, I. McCallum, M. Obersteiner, E. Rametsteiner, and Y. Yamagata. 2007. Geographically explicit global modeling of land-use change, carbon sequestration, and biomass supply. Technological Forecasting and Social Change. 74(7):1057-1082. https://doi.org/10.1016/j.techfore.2006.05.022CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)
2006Barkley, M.P.; (2006). Comparisons between SCIAMACHY atmospheric CO 2 retrieved using (FSI) WFM-DOAS to ground based FTIR data and the TM3 chemistry transport model. Atmospheric chemistry and physics. 6 (0): 4483-4498. Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2006Barkley, M.P.; (2006). Comparisons between SCIAMACHY atmospheric CO 2 retrieved using (FSI) WFM-DOAS to ground based FTIR data and the TM3 chemistry transport model. Atmospheric chemistry and physics. 6 (0): 4483-4498. Biome-BGC: Modeling Effects of Disturbance and Climate (Thornton et al. 2002)
2006Cienciala, E. and F.A. Tatarinov. 2006. Application of BIOME-BGC model to managed forests. Forest Ecology and Management. 237(1-3):252-266. https://doi.org/10.1016/j.foreco.2006.09.086Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2006Tatarinov, F.A. and E. Cienciala. 2006. Application of BIOME-BGC model to managed forests. Forest Ecology and Management. 237(1-3):267-279. https://doi.org/10.1016/j.foreco.2006.09.085Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
2001Bugmann, H.K.M., S.D. Wullschleger, D.T. Price, K. Ogle, D.F. Clark, and A.M. Solomon. 2001. Climatic Change. 51(3/4):349-388. https://doi.org/10.1023/A:1012537914881LINKAGES: An Individual-based Forest Ecosystem Biogeochemistry Model
2001Lu, L., R.A. Pielke, G.E. Liston, W.J. Parton, D. Ojima, and M. Hartman. 2001. Implementation of a Two-Way Interactive Atmospheric and Ecological Model and Its Application to the Central United States. Journal of Climate. 14(5):900-919. https://doi.org/10.1175/1520-0442(2001)014<0900:IOATWI>2.0.CO;2CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)
2008Soulis, E.D. and F.R. Seglenieks. 2008. The MAGS Integrated Modeling System. 445-473. https://doi.org/10.1007/978-3-540-75136-6_24Model Archive