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tweet thisGlobal Coverage & Frequency

We have you covered: The imaging capabilities of the RapidEye constellation are unrivaled, and our coverage and frequency maps are reflective of that. The RapidEye constellation has the capability to collect 5 million km² of the most current and high-quality imagery every day. BlackBridge has over 5 billion km² of high-resolution RapidEye imagery in its archive, both current and historical.

  • Coverage and Frequency Maps
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Global Coverage and Imaging Frequency


Collection Examples

BlackBridge White Papers

Apparent Cloud Shift in RapidEye Image Data

Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping Using RapidEye Imagery

Spectral Response Curves of the RapidEye Sensor

The RapidEye Red-Edge Band

Scientific Papers & Publications


Ali, M., Montzka, C., Stadler, A., Menz, G., Thonfeld, F., and Vereecken, H. (2015): Estimation and validation of RapidEye-based time-series of Leaf Area Index for winter wheat in the Rur Catchment (Germany). Remote Sensing, 7(3): 2808–2831.

Arnett, J. T., Coops, N. C., Daniels, L. D., and Falls, R. W. (2015): Detecting forest damage after a low-severity fire using remote sensing at multiple scales. International Journal of Applied Earth Observation and Geoinformation 35: 239–246.

Beaumont, B., Bouvy, A., Stephenne, N., Mathoux, P., Bastin, J.-F., and Baudot, Y. (2015): Combining satellite, aerial and ground measurements to assess forest carbon stocks in Democratic Republic of Congo. In EGU General Assembly 2015.

Beyer, F., Jarmer, T., and Siegmann, B. (2015): Identification of agricultural crop types in Northern Israel using multitemporal RapidEye data. Photogrammetrie-Fernerkundung-Geoinformation 2015(1): 21–32.

Blasch, G., Spengler, D., Hohmann, C., Neumann, C., Itzerott, S., and Kaufmann, H. (2015): Multitemporal soil pattern analysis with multispectral remote sensing data at the field-scale. Computers and Electronics in Agriculture 113: 1–13.

Cicerelli, R. E., and de LBT Galo, M. (2015): Sensoriamento remoto multifonte aplicado na detecção do fitoplâncton em águas interiores. R. Bras. Eng. Agric. Ambiental 19(3): 259–265.

Choe, E., Lee, J.-W., and Cheon, S.-U. (2015): Monitoring and modelling of chlorophyll-a concentrations in rivers using a high-resolution satellite image: a case study in the Nakdong River, Korea. International Journal of Remote Sensing 36(6): 1645–1660.

Imukova, K., Ingwersen, J., and Streck, T. (2015): Determining the spatial and temporal dynamics of the green vegetation fraction of croplands using high-resolution RapidEye satellite images. Agricultural and Forest Meteorology 206: 113–123.

Johnson, B. (2015): Remote sensing image fusion at the segment level using a spatially-weighted approach: Applications for land cover spectral analysis and mapping. ISPRS International Journal of Geo-Information 4(1): 172–184.

Kim, H.-O., and Yeom, J.-M. (2015): Sensitivity of vegetation indices to spatial degradation of RapidEye imagery for paddy rice detection: a case study of South Korea. GIScience & Remote Sensing 52(1): 1–17.

Lex, S., Asam, S., Lӧw, F., and Conrad, C. (2015): Comparison of two statistical methods for the derivation of the fraction of absorbed photosynthetic active radiation for cotton. Photogrammetrie-Fernerkundung-Geoinformation 2015(1): 55–67.

Ramoelo, A., Dzikiti, S., van Deventer, H., Maherry, A., Cho, M. A., and Gush, M. (2015): Potential to monitor plant stress using remote sensing tools. Journal of Arid Environments 113: 134–144.

Schuster, C., Schmidt, T., Conrad, C., Kleinschmit, B., and Fӧrster, M. (2015): Grassland habitat mapping by intra-annual time series analysis-Comparison of RapidEye and TerraSAR-X satellite data. International Journal of Applied Earth Observation and Geoinformation 34: 25–34.

Spiekermann, R., Brandt, M., and Samimi, C. (2015): Woody vegetation and land cover changes in the Sahel of Mali (1967-2011). International Journal of Applied Earth Observation and Geoinformation 34: 113–121.

Wallner, A., Elatawneh, A., Schneider, T., and Knoke, T. (2015): Estimation of forest structural information using RapidEye satellite data. Forestry 88(1): 96–107.


Adam, E., Mutanga, O., Odindi, J., and Abdel-Rahman, E. M. (2014): Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing 35(10): 3440–3458.

Adelabu, S., Mutanga, O., and Adam, E. (2014): Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels. ISPRS Journal of Photogrammetry and Remote Sensing 95: 34–41.

Antunes, M. A. H., Debiasi, P., and Siqueira, J. C. dos S. (2014): Avaliação espectral e geométrica das imagens RapidEye e seu potencial para o mapeamento e monitoramento agrícola e ambiental. Revista Brasileira de Cartografia 1(66/1): 105–113.

Arnett, J. T., Coops, N. C., Gergel, S. E., Falls, R. W., and Baker, R. H. (2014):Detecting stand replacing disturbance using RapidEye imagery: a Tasseled Cap transformation and modified Disturbance Index. Canadian Journal of Remote Sensing 40 (just-accepted): 1–14.

Beckschäfer, P., Fehrmann, L., Harrison, R. D., Xu, J., and Kleinn, C. (2014): Mapping Leaf Area Index in subtropical upland ecosystems using RapidEye imagery and the randomForest algorithm. iForest 7: 1–11.

Bu, H. (2014): Yield and quality prediction using satellite passive imagery and ground-based active optical sensors in sugar beet, spring wheat, corn, and sunflower.

Buck, O., Millán, V. E. G., Klink, A., and Pakzad, K. (2014): Using information layers for mapping grassland habitat distribution at local to regional scales. International Journal of Applied Earth Observation and Geoinformation. In press.

Conrad, C., Dech, S., Dubovyk, O., Fritsch, S., Klein, D., Lӧw, F., Schorcht, G., and Zeidler, J. (2014): Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images. Computers and Electronics in Agriculture 103: 63–74.

Coronado Z., M. E. (2014): Análisis de la fragmentación en el Parque Nacional Cerro Azul Meámbar (PANACAM).

Da Silva Costa, A. (2014)Potencial das imagens RapidEye para estudos ambientais. Revista de Ciências da Amazônia 1(2): 9–17.

De Almeida, A. S., and Vieira, I. C. G. (2014): Conflitos no uso da terra em Áreas de Preservação Permanente em um polo de produção de biodiesel no Estado do Pará. Ambiente & Água - An Interdisciplinary Journal of Applied Science 9(3): 476–487.

De Oliveira, D. A., and Rosolen, V. (2014): Os sistemas úmidos na paisagem de chapada, o uso da terra e desafios da preservação ambiental. Revista Brasileira de Geomorfologia 15(2): 221–230.

De Oliveira Santos, L. T. S., de Jesus, T. B., and Nolasco, M. C. (2014): Influência do uso e ocupação do solo na qualidade das águas superficiais do rio Subaé, Bahia. Geographia Opportuno Tempore 1(1): 68–79.

Dube, T., Mutanga, O., Elhadi, A., and Ismail, R. (2014): Intra- and interpecies biomass prediction in a plantation forest: Testing the utility of high spatial resolution spaceborne multispectral RapidEye sensor and advanced machine learning algorithms. Sensors 14(8): 15348–15370.

Honda, K., Ines, A. V. M., Yui, A., Witayangkurn, A., Teeravech, K., and Chinnachodteeranun, R. (2014): Agriculture information service built on geospatial data infrastructure and crop modeling. In Proceedings of the 2014 International Workshop on Web Intelligence and Smart Sensing. In Proceedings of the 2014 International Workshop on Web Intelligence and Smart Sensing.

Kemec, S., Ok, A., and Kamaci, E. (2014):The effects of 23 October and 9 November 2011 earthquakes on spatial transformation of the Van City. Geodinamica Acta (ahead-of-print): 1–10.

Kim, H.-O., and Yeom, J.-M. (2014): Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data. International Journal of Remote Sensing 35(19): 7046–7068.

Kiyoshi, H., Yui, A., Ines, A. V., Chinnachodteeranun, R., Witayangkurn, A., Matsubara, Y., Nagai, H., and Miyamoto, J. (2014): FieldTouch: an innovative agriculture decision support service based on multi-scale sensor platform. In Global Conference (SRII), 2014 Annual SRII.

Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., Liu, Y., Liu, B., Ustin, S. L., and Chen, X. (2014): Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research 157: 111–123.

Magdon, P., Fischer C., Fuchs H. and Kleinn C. (2014): Translating criteria of international forest definitions into remote sensing image analysis. Remote Sensing of Environment, 149, 252-262

Martins, G. D., and Galo, M. L. B. T. (2014): ) Detecção de áreas infestadas por nematoides e Migdolus fryanus em cultura canavieira a partir de imagens multiespectrais RapidEye. Revista Brasileira de Cartografia 66(2): 285–301.

Ozdemir, I. (2014): Linear transformation to minimize the effects of variability in understory to estimate percent tree canopy cover using RapidEye data. GIScience & Remote Sensing 51(3): 288–300.

Ramoelo, A., Dzikiti, S., van Deventer, H., Maherry, A., Cho, M. A., and Gush, M. (2015): Potential to monitor plant stress using remote sensing tools. Journal of Arid Environments 113: 134–144.

Remelgado, R., Notarnicola, C., and Sonnenschein, R. (2014)Forest damage assessment using SAR and optical data: Evaluating the potential for rapid mapping in mountains. EARSeL eProceedings 13(S1): 76–81.

Ribeiro, C. A. A. S., Lemos, N. de C. M., de Oliveira Barros, K., Soares, V. P., Silva, E., and others (2014): Áreas de preservação permanente em conflito com o uso ea ocupação do solo na bacia hidrográfica do Córrego Sertão, Cajuri, Minas Gerais. Revista Agrogeoambiental 6(2): 1–9.

Rochdi, N., Yang, X., Staenz, K., Patterson, S., and Purdy, B. (2014) Mapping tree species in a boreal forest area using RapidEye and Lidar data. In Ulrich Michel and Karsten Schulz (Ed.), Proc. SPIE 9245, Earth Resources and Environmental Remote Sensing/GIS Applications V.

Roslani, M. A., Mustapha, M. A., Lihan, T., and Wan Juliana, W. A. (2014): Applicability of RapidEye satellite imagery in mapping mangrove vegetation species at Matang Mangrove Forest Reserve, Perak, Malaysia. Journal of Environmental Science and Technology 7: 123–136.

Sang, H., Zhang, J., Zhai, L., Qiu, C., and Sun, X. (2014): Analysis of RapidEye imagery for agricultural land cover and land use mapping. In 3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA).

Schuster, C., Schmidt, T., Conrad, C., Kleinschmit, B., and Fӧrster, M. (2015) Grassland habitat mapping by intra-annual time series analysis-Comparison of RapidEye and TerraSAR-X satellite data." International Journal of Applied Earth Observation and Geoinformation 34: 25–34.

Silva, C. K. D., Pereira, R. S., Nunes, M. M. D. C., Silva, T. P. C. D., and Alvarez, I. A. (2014): Aplicação do algoritmo LEGAL/SPRING na cobertura florestal do munic’\ipio de Ivorá/RS nos anos de 2011 e 2012. In Embrapa Monitoramento Por Satélite-Artigo Em Anais de Congresso (ALICE).

Spiekermann, R., Brandt, M., and Samimi, C. (2015): Woody vegetation and land cover changes in the Sahel of Mali (1967-2011). International Journal of Applied Earth Observation and Geoinformation 34: 113–121.

Theilen-Willige, B.; Malek, H.A.; Charif, A.; El Bchari, F.; Chaïbi, M. (2014): Remote Sensing and GIS Contribution to the Investigation of Karst Landscapes in NW-Morocco. Geosciences 2014, 4, 50-72.

Thiele, M., Anderson, C., and Brunn, A. (2014): Cross-calibration of the RapidEye Multispectral Imager payloads using near simultaneous acquisitions of pseudo-invariant test sites. In Proc. SPIE 9241, Sensors, Systems, and Next-Generation Satellites XVIII, 924114, October 7.

Tillack, A., Clasen, A., Kleinschmit, B., and Fӧrster, M. (2014): Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices. Remote Sensing of Environment 141: 52–63.

Ustuner, M., Sanli, F. B., Abdikan, S., Esetlili, M. T., and Kurucu, Y. (2014): Crop type classification using vegetation indices of RapidEye imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7: 195–198.

Viana, D. R., da Silva, R. R., and Correia, A. H. (2014): Elaboração de base cartográfica digital do estado de Rondônia a partir de imagens RapidEye. XXVI Congresso Brasileiro de Cartografia, Gramado, RS, Brasil, 03-07 August. (494): 1–13.

Wallner, A., Elatawneh, A., Schneider, T., and Knoke, T. (2014): Estimation of forest structural information using RapidEye satellite data. Forestry.

Wen, X., Zhou, Z., Chen, B., Li, Z., and Tang, X. (2014): Research on the features of Chlorophyll-a derived from RapidEye and EOS/MODIS data in Chaohu Lake. In 35th International Symposium on Remote Sensing of Environment. IOP Conference Series: Earth and Environmental Science (Vol. 17).

Zillmann, E., and Weichelt, H. (2014): Crop identification by means of seasonal statistics of RapidEye time series. In Third International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2014).


Anderson, C., M. Thiele, A. Brunn (2013): Calibration and validation of the RapidEye constellation. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Antunes, M. and J. Siqueira (2013): Características das imagens RapidEye para mapeamento e monitoramento e agrícola e Ambiental. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Cattani, C., E. Mercante, C. Wachholz de Souza, S. Wrublack (2013): Desempenho de algoritmos de classificação supervisionada para imagens dos satélites RapidEye. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Cronemberger, F., R. Vicens, M. Pimenta (2013): Detecção de Mudanças Utilizando Analise Orientada a Objeto em Imagens Rapideye – Caso COMPERJ e Eventos extremos. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE


DRZEWIECKI, W., WEZYK, P., PIERZCHALSKI, M. and B. SZAFRANSKA (2013): Quantitative and Qualitative Assessment of Soil Erosion Risk in Małopolska (Poland), Supported by an Object-Based Analysis of High-Resolution Satellite Images. In: Pure and Applied Geophysics. DOI 10.1007/s00024-013-0669-7

Eduardo, B., A. Machado e Silva (2013): Avaliação da influência da correção atmosférica no cálculo do índice de vegetação NDVI em imagens Landsat 5 e RapidEye. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Gleriani, J., P. Ferreira, V. Soares (2013): Comparação de Modelo Linear de Mistura Espectral (MLME) e Modelo Não Linear de Mistura Espectral (MNLME) aplicados à dados TM/Landsat-5 com dados subpixel do sensor RapidEye. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

de Oliveira, G., G. da Silva, H. da Silva, M. Santos, U Lima (2013): Mapeamento de índices de cobertura vegetal dos bairros de Salvador-BA com uso de imagens do sensor RapidEye para o ano de 2009. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Gomes, M. and P. Maillard (2013): O uso de feições de textura em imagens RapidEye para estimativas da idade e de parâmetros estruturais da vegetação do cerrado. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Krahwinkler, P. and J. Rossmann (2013): Tree Species Classification and Input Data Evaluation. In: European Journal of Remote Sensing - 2013, 46: 535-549.

Machado e Silva, A., B. Eduardo, A. Fazan (2013): Avaliação da Qualidade Geométrica das Imagens RapidEye Ortorretificadas. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Makinde E. O and Salami A. (2013): REMOTE SENSING OF VEGETATION STRESS AND INDICATORS. In: Proceedings of Global Geospatial Conference 2013
Addis Ababa, Ethiopia, 4-8 November 2013.

Moura, A., C. Sepúlveda, M. Resende, S. Ribeiro (2013): Uso de imagens RapidEye como apoio à tomada de decisões no planejamento e gestão da paisagem do município de Bom Sucesso – MG. In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Nguyen-Thanh Son and Chi-Farn Chen (2013): Remote sensing of mangrove forests in Central America. In: SPIE Newsroom

Oliveira, F.; E. Filho; V. Soares; A. Lopes de Souza (2013): Mapeamento de fragmentos florestais com monodominância de aroeira a partir da classificação supervisionada de imagens Rapideye. Rev. Árvore vol.37 no.1 Viçosa Jan./Feb. 2013

Ortiz, S., J. Breidenbach and G. Kändler (2013): Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data. In: Remote Sens. 2013, 5, 1912-1931

Rodrigues, S., P. Souza-Filho (2013): Índice de sensibilidade ambiental (ISA) a partir do processamento de imagens RapidEye para o litoral paraense (Soure,Curuçá e Bragança). In: Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE

Tigges, J., Lakes, T., Hostert, P. (2013). Urban Vegetation Classification: Benefits of Multitemporal RapidEye Satellite Data. Remote Sensing of Environment, 136, 66-75.


ANDERSON, C., BRUNN, A., THIELE, M. (2012): Combining Imaging Statistics and Side Slither Imagery to Estimate Detector Gains. CALCON - Conference on Characterization and Radiometric Calibration for Remote Sensing. August 27, 2012 - Utah State University, Logan, Utah

BRUNN, A., ANDERSON, C., THIELE, M. and ROLOFF, C. (2012): Calibration and Validation activities at RapidEye. JACIE 2012 - Civil Commercial Imagery Evaluation Workshop. April 17, 2012 - Fair Oaks Marriott Hotel, Fairfax, Virginia

CAMPO, F. and V. DE LUCA (2012): RapidEye e la banda Red Edge per la creazione di Mappe di Clorofilla. In: GEOmedia n°4-2012

Dupuy, Stéphane; Barbe, Eric; Balestrat, Maud (2012): An Object-Based Image Analysis Method for Monitoring Land Conversion by Artificial Sprawl Use of RapidEye and IRS Data. Remote Sens. 4, no. 2: 404-423.

FRITSCH, S., MACHWITZ, M. EHAMMER, A. CONRAD, C., DECH, S. (2012): Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery. International Journal of Remote Sensing. Vol. 33, Iss. 21, 2012

HESE, S. (2012): Change Signature Analysis for Rapid Mapping Concepts of Tsunami Flooded Coastal Areas. In: BORG, E., H. DAEDELOW & R. JOHNSON (Hrsg.): RapidEye Science Archive (RESA): Vom Algorithmus zum Produkt. 4. RESA Workshop. Berlin: GITO, 345-357.

HESE, S. (2012): Contextual and Infrastructure linked Spectral Oilspill Mapping in West Siberia, RESA ID 472 – Extended Summary. In: BORG, E., H. DAEDELOW & R. JOHNSON (Hrsg.): RapidEye Science Archive (RESA): Vom Algorithmus zum Produkt. 4. RESA Workshop. Berlin: GITO, 335-344.

HESE, S., M. VOLTERSEN & M. URBAN (2012): High Resolution Land Cover Mapping and Arctic Thermokarst Lake Change Monitoring in the DUE Permafrost Project. In: BORG, E., H. DAEDELOW & R. JOHNSON (Hrsg.): RapidEye Science Archive (RESA): Vom Algorithmus zum Produkt. 4. RESA Workshop. Berlin: GITO, 257-274.

HESE, S. & M. URBAN (2012): Water Body and Tree Line Change Mapping. In: BORG, E., H. DAEDELOW & R. JOHNSON (Hrsg.): RapidEye Science Archive (RESA): Vom Algorithmus zum Produkt. 4. RESA Workshop. Berlin: GITO, 311-319.

HYUN OK KIM and JONG MIN YEOM (2012): Multi-Temporal Spectral Analysis of Rice Fields in South Korea Using MODIS and RapidEye Satellite Imagery. Journal of Astronomy and Space Science 29(4), 407-411 (2012)

KLONUS, S. and M. EHLERS (2012): Using TerraSar-X and RapidEye data for change detection in Wadden Sea areas. In: Proceedings of 1st EARSeL Workshop on Temporal Analysis of Satellite Images Mykonos, Greece, 23rd – 25th May, 2012

RAMOELO, A., SKIDMORE, K., CHOA, M.A., SCHLERF, M., MATHIEUA, R. and I.M.A. HEITKÖNIG (2012): Regional estimation of savanna grass nitrogen using the red-edge band of the RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation Volume 19, October 2012, Pages 151–162

SCHUSTER, C., FÖRSTER, M. & B. KLEINSCHMITT (2012): Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data. International Journal of Remote Sensing, 33, 17, 5583-5599. ISSN 1366-5901.

THEILEN-WILLIGE, B. and WENZEL, H. (2012):  Remote Sensing and GIS Contribution to the Inventory of Infrastructure susceptible to Earthquake and Tsunami Hazards - demonstrated by Case Studies in Japan and Chile. Proceedings of the Fifth International Tsunami Symposium (ISPRA-2012) Tsunami Society International

THIELE, M., ANDERSON, C., and BRUNN, A.: Cross-Calibration of the RapidEye Multispectral Imager Payloads using Pseudo-Invariant Test Sites. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B1, 167-171, doi:10.5194/isprsarchives-XXXIX-B1-167-2012, 2012. 

Ribeiro, M., T. Vieira, M. Volpato, H. Alve, R. Marujo and L. Silva (2012): CLASSIFICAÇÃO DE MAGENS RAPIDEYE PARA ÁREAS CAFEEIRAS NO MUNICIPIO DE CAMPANHA-MG. 3ª Jornada Científica da Geografia.


Santos, L., T. de Jesus, J.Chaves, M. Noslaco (2012): INFLUÊNCIA DO USO E OCUPAÇÃO DO SOLO NA BACIA DO RIO SUBAÉ, BAHIA-BRASIL. In: Proceedings of the XV Symposium SELPER, 19-23 November, Cayenne, French Guyana.

Rempel, C., R. Eckhardt, G. Schultz, E. Périco, C. da Silva Cyrne (2012): ZONEAMENTO ECOLÓGICO-ECONÔMICO - ZEE - PARA SISTEMAS ORGÂNICOS DE
. TECNO-LÓGICA, Santa Cruz do Sul, v. 16, n. 2, p. 90-97, jul./dez. 2012


Anderson, C., Naughton, D., Brunn, A., Thiele, M. (2011): Radiometric correction of RapidEye imagery using the on-orbit side-slither method. SPIE Proc. 8180, (2011).

Bindel, M., Hese, S., Berger, C. & Schmullius, C. (2011): Evaluation of red-edge spectral information for biotope mapping using RapidEye. Proceedings of SPIE 8174 (2011).

Conrad, C., Machwitz, M., Schorcht, G., Löw, F., Fritsch, S. & Dech, S. (2011): Potentials of RapidEye time series for improved classification of croprotations in heterogeneous agricultural landscapes: Experiences fromirrigation systems in Central Asia. Proceedings of SPIE 8174 (2011).

Förster, M., Frick, A., Kleinschmit, B. (2011): Application of a phenological library to detect grassland-habitats with a RapidEye intra-annual time-series. 4th. Workshop of EARSeL SIG LULC, Prag, Poster

Förster, M., Schuster, C., Sonnenschein, R., Bahls, A., Kleinschmit, B. (2011): Möglichkeiten der Erfassung von Landbedeckung und Vegetationsgesellschaften mittels RapidEye-Daten. In: Borg, E. & Daedelow, H. (Eds.): RapidEye Science Archive (RESA) - Erste Ergebnisse. Gito, Berlin, pp. 3-17, ISBN: 978-3-942183-38-3.

Fritsch, S,, Conrad, C., Manschadi, A., Machwitz, M., Rücker, G. & Dech, S. (2011): Estimating regional crop yield at field scale using multitemporal RapidEye data. Poster

Hoberg, T.; Müller, S. (2011): Multitemporal Crop Type Classification using Conditional Random Fields and RapidEye Data. In: ISPRS Proceedings Volume XXXVIII-4/W19, 2011

Hyun Ok Kim; Jong Min Yeom; Youn Soo Kim (2011): Agricultural land cover classification using rapideye satellite imagery in South Korea - first result.Proceedings of SPIE 8174 (2011). Fritsch, S,, Conrad, C., Manschadi, A., Machwitz, M., Rücker, G. & Dech, S. (2011): Estimating regional crop yield at field scale using multitemporal RapidEye data. Poster

Naughton, D., et al. (2011): Absolute radiometric calibration of the RapidEye Multispectral Imager using the reflectance-based vicarious calibration method.  SPIE Journal of Applied Remote Sensing 5, (2011).

Peng, G., G. Gao, D. Feng, Y. Xiong (2011): Pegmatite remote sensing extraction and metallogenic prediction in Azubai area, Xinjiang. In: The Transactions of Nonferrous Metals Society of China 21 (2011), 543 - 548

Recio, J.A.,, P. Helmholz, S. Müller (2011): Potential Evaluation of Different Types Of Images and Their Combination for the Classification of GIS Objects Cropland and Grassland. In: ISPRS Proceedings Volume XXXVIII-4/W19, 2011

Schuster, C., Ali, I., Schmidt, T., Lohmann, P., Frick, A., Förster, M., Kleinschmit, B. (2011): Synergistic analysis of multi-temporal RapidEye and TerraSAR-X data for monitoring NATURA 2000 grassland habitats. 4th. Workshop of EARSeL SIG LULC, Prague, Poster.

Stoll, E. ; Schulze, R. ; Oxfort, M. (2011):  The Impact of Collision Avoidance Maneuvers on RapidEye Constellation Management. European Space Surveillance Conference 7-9 June 2011, Madrid, Spain

Theilen-Willige,B. and Wenzel, H. (2011):  Remote Sensing and GIS Contribution to Earthquake Disaster Preparedness in Hungary.  Gi4DM Geoinformation for Disaster Management, 3.-8.May 2011 in Antalya, Turkey, 7.May 2011, OP62

Watt, P. and Watt, M. (2011): Applying Satellite Imagery for Forest Planning. NZ Journal of Forestry. May 2011.


Brunn, A., Naughton, D., Weichelt, H., Douglass, S., Thiele, M., Oxfort, M., Beckett, K. (2010): The calibration procedure of the multispectral imaging instruments on board the RapidEye Remote Sensing Satellites. Proceedings of International Calibration and Orientation Workshop EuroCOW 2010, Castelldefels, Spain, 2010.

Beckett, K., Robertson, B., Steyn, J. (2010): MTF Characterization and Deconvolution of RapidEye Imagery. Proceedings of IGARSS 2010, Honolulu, Hawaii, 2010.

Förster, M. Frick, A. Schuster, C. & Kleinschmit, B. (2010): Object-based Change Detection Analysis for the Monitoring of Habitats in the Framework of the NATURA 2000 Directive with Mulit-temporal Satellite Data. In: Addink, E.A. and F.M.B. Van Coillie (Eds). GEOBIA 2010-Geographic Object-Based Image Analysis. Ghent University, Ghent, Belgium, 29 June – 2 July. ISPRS Vol.No. XXXVIII-4/C7. ISSN: 1682-1777

Förster, M.,  Schuster, C. & Kleinschmit, B. (2010): Significance Analysis of Multi-temporal RapidEye Satellite Images in a Land-cover Classification. In: Tate, N.J. & Fisher, P.F. (Eds.) Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Leicester, UK, 20 - 23 July. 273-276

HESE, S., G. GROSSE & S. PÖCKING (2010): Object based thermokarst lake change mapping as part of the ESA Data User Element (DUE) Permafrost. – OBIA Conference 2010, Genth/Belgium.

Nielsen, A.A., Hecheltjen, A.,  Thonfeld, F., Canty, M.J. (2010): Automatic change detection in RapidEye data using the combined MAD and kernel MAF methods. Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International

Schuster, C., Förster, M. & Kleinschmit, B. (2010): Evaluation of the RapidEye red edge channel for improving land-use classifications. In: Kohlhofer, G., Franzen, M. (Hrsg.). 2010. Tagungsband Dreiländertagung OVG, DGPF und SGPF. 30. Wissenschaftlich-Technische Jahrestagung der DGPF. Technische Universiät Wien, Wien, Österreich, 1.-3. Juli. Band 19, S. 119-126

Tapsall, B.., Pavel, M., Kadim, T. (2010). Analysis of rapideye imagery for annual landcover mapping as an aid to European Union (EU) common agricultural policy. In: ISPRS Technical Commission VII Symposium - 100 Years ISPRS Advancing Remote Sensing Science; 05 July 2010; Vienna (Austria). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII (7B) p. 568-573. JRC59234

Vuolo, F., Atzberger, C., Richter, K. & J Dash (2010): RETRIEVAL OF BIOPHYSICAL VEGETATION PRODUCTS FROM RAPIDEYE IMAGERY. Symposium A Quarterly Journal In Modern Foreign Literatures (2010) Volume: XXXVIII, Pages: 281-286


Brunn, A., Douglass, S., Weichelt, H., Beckett, K. (2009): The RapidEye Calibration Approach and Current Results. Proceedings of JACIE 2009, Washington, DC, 2009.


Robertson, B., Beckett, K., Rampersad, C., Putih (200): Quantitative Geometric Calibration & Validation of the RapidEye Constellation. Proceedings of IGARSS 2009, Cape Town, South Africa, 2009.

Steyn, J., Tyc, G., Beckett, K., Hashida, Y. (2009): RapidEye Constellation Relative Radiometric Accuracy Measurement Using Lunar Images. Proceedings of SPIE 2009, Berlin, Germany, 2009.

tweet thisSatellite Image Poster Gallery (PDF)

Earth. It's a beautiful planet. The BlackBridge system captures over a billion km² of it every year. Some of our most beautiful imagery is highlighted below as a collection of posters in PDF format..

RapidEye Mosaics
RapidEye Mosaics
RapidEye Mosaics
North Korea
RapidEye Mosaics
RapidEye Mosaics
RapidEye Mosaics
Afghanistan Abu Dhabi Abu Dhabi Albania Algeria Anguilla & St. Martin
Argentina Australia Australia Australia Bahamas Bangladesh
Bolivia Bolivia Brazil Brazil Brazil Canada
Canada Canada Chad Chile China China
Croatia Equador France France Germany Germany
Guatemala Iceland Iceland Indonesia Iraq Ireland
Israel Italy Italy Japan Jordan Kenya
Maldives Mauretania Mexico Mongolia Morocco Mozambique
Namibia Namibia Nepal New Zealand Nicaragua Norway
Norway Oman Pakistan Philippines Qatar Russia
Saudi Arabia Saudi Arabia Senegal South Africa South Africa Spain
Spain Sudan Tanzania Turkey Turkey Turkmenistan
United Kingdom USA USA USA USA USA


Sample Data

Full-resolution RapidEye Free Sample Data products are available to download and view. We have a collection of Free Sample Data of various product types and geographies. Over time, we will be providing additional products for your review.

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and more...


BlackBridge offers answers to frequently asked questions about our products, satellites and constellation, purchasing options and more. Should you have any further questions, please consult our Contact Us page.

  • Product Information
  • Satellite Tasking
  • Product Viewing
  • Purchasing Products
  • Deliveries
  • Distributor Information

Product Information

Q: Can you supply me with customized demonstration products over a specified area?
A: We can provide you with data that holds similar characteristics to your area of interest and will do our best to provide you with data from the same region, however we will not produce customized test data. For the latest list of demonstration products go here

Q: Does BlackBridge supply multispectral data?
A: Yes, the RapidEye sensors provide five band multispectral images with native resolution of 6.5m. The following table lists the spectral range of the five BlackBridge bands:

Band # Name Spectral Range (nm)
1 Blue 440 – 510
2 Green 520 – 590
3 Red 630 – 685
4 Red Edge 690 – 730
5 Near-Infrared 760 – 850

All RapidEye Satellite Image products are offered with all five spectral bands. The 1B Basic Product is offered at the native sensor resolution of 6.5m and the 3A Ortho Product is offered at a resampled resolution of 5m.

Q: Does BlackBridge supply panchromatic data?
A: No, the RapidEye sensors are not equipped with a panchromatic band, so we cannot supply panchromatic data.

Q: What is the positional accuracy of RapidEye products?
A: The positional accuracy of the RapidEye products varies between countries and regions, depending on available ground control data. Ground control points (GCPs) are used during the cataloging process to refine the locational accuracy of all products. The positional accuracy of the 1B Basic Product can be as accurate as 11.0m 1-sigma (23.6m CE90). Our RapidEye 3A Ortho product can be as accurate as 6m 1-sigma (12.7m CE90) depending upon the GCPs and DEM used.

Q: What is the quality of RapidEye radiometry?
A: BlackBridge continuously monitors acquired image data to ensure long term stability and intercomparability among all five sensors. In addition, image data is frequently acquired from a number of calibration sites spread worldwide. These statistics are also used to ensure that each band stays within a range of +/- 2.5% from the band mean value across the constellation.

A reflectance-based vicarious calibration campaign was conducted between April 2009 and May 2010 at Railroad Valley Playa, Nevada, USA and Ivanpah Playa, Nevada, USA to determine the on-orbit radiometric accuracy of the RapidEye sensor. In-situ surface spectralreflectance measurements of known ground targets and an assessment of the atmospheric conditions above the sites were taken during spacecraft overpasses. The ground data were used as input to a radiative transfer code to compute a band specific top-of-atmosphere (TOA) spectral radiance. A comparison of these predicted values based on absolute physical data to the measured at-sensor spectral radiance provided the absolute calibration of the sensor. Initial assessments showed that the RapidEye sensor response was within 7% of the predicted values. Outcomes from this campaign were then used to update the calibration parameters in the ground segment processing system. Subsequent verification events confirmed that the measured RapidEye response was improved to within 4% of the predictions based on the vicarious calibration method.

Q: What is your band to band registration and multi temporal pixel to pixel registration accuracy?
A: For areas where the terrain slope is below 10°, the band to band co-registration should be within 0.2 pixels or less (1-sigma). For areas with a slope angle of more than 10° and/or areas with a very poor image structure (e.g. sand dunes, water bodies, areas with significant snow cover) the co-registration accuracy may not be met. The co-registration accuracy of two products from different dates (multi temporal) is directly related to the accuracy of the products being used. Since each product is produced independently, their geolocation accuracy will vary.

Q: What is blackfill? Why does my order contain 3A tiles with blackfill and some without blackfill?
A: Blackfill within an image product are areas that do not contain valid imagery for that area. Blackfill in 3A orthorectified tiles is due to the fact that the RapidEye image data, collected in 77 km wide Image Takes at a certain inclination, is processed for the 3A products in tiles, that are based on a fixed grid. This results in tiles not always filled with imaged data. Blackfill within the Area Of Interest (AOI) specified for an order indicates that not all the tiles within the AOI could be collected in a single imaging attempt. In most cases, for tiles with blackfill, BlackBridge will provide complementary tile(s) that will contain the missing data, thus providing full coverage. An example of complementary tiles is shown below:

Blackfill outside of the AOI may or may not occur, and no attempt will be made to provide valid imagery for those areas.

Q: What is the RapidEye tiling system? How does this correspond to the UTM grid?
A: The RapidEye tiling system divides the world between +/- 84º into zones based on the UTM grid. Each tile is defined by a respective UTM zone, and then by a BlackBridge defined row and column number. For example a tile with the ID number of 3354105 is located in UTM zone 33, row 541 and column 05. The tile grid defines 24 km by 24 km tiles with a 1 km overlap, resulting in 25 km by 25 km tiles.

A ESRI Shapefile of the RapidEye Level 3A tile grid can be downloaded here

Q: How are RapidEye 3A products processed?
A: The RapidEye 3A is an orthorectified product. Orthorectification is the process used to remove distortions to an image coming from topographic relief (the Earth's surface) or the camera used to take the image. To remove the surface distortions or topographic relief, Digital Elevation Models (DEMs) are used, along with Ground Control Points (GCPs). Orthorectified images can be used to measure true distances on the ground, because they are an accurate representation of the surface of the Earth, like a map.

RapidEye 3A products are orthorectified as 25 km by 25 km image tiles, with each tile produced independently during the production process. During the orthorectification processing, extra imagery outside of the boundary of the tiles is used in marking GCPs. This marking process is used to accurately locate the imagery to known ground features. Since each tile is produced independently and each tile's extent is different, the GCPs and number of GCPs will vary from tile to tile. After the marking process, a DEM is used to remove the terrain distortions from the image. The accuracy of both the GCPs and DEM affect the accuracy of the final image product. Please consult the Satellite Image Product Specification document for further details of the accuracy of the 3A product and the GCPs and DEMs used.

Q: Which areas cannot be acquired by RapidEye satellites and why?
A: All land areas of the world between 84 degrees North and 84 degrees South can be acquired by the RapidEye satellites in normal operational mode. We do exclude water and ice regions, because they do not contain identifiable landmarks. Landmarks (features or points with well known geo-locations) are needed to band align the images and are also used to improve the georeferencing of the image data. There are, however, seasonal restrictions on imaging, driven by the need for sufficient illumination levels. In higher latitudes, imaging is restricted during the hemisphere's winter to areas with a sun elevation angle of 30 degrees or more. The low sun elevation negatively affects our image quality and not the capability to acquire images. Generally speaking, the lower the sun elevation, the darker the image becomes. This affects Central and Northern Europe, Northern Asia and Northern U.S./ Canada in the northern hemisphere's winter and Southern Argentina and Chili, Tasmania and New Zealand in southern hemisphere's winter. Please contact BlackBridge if your needs involve imaging areas during low illumination conditions. We will consider every request and advise you on the to be expected image quality.

Q: What factors are considered when acquiring imagery for the BlackBridge Archive? I have noticed that some areas are acquired several times a month and other areas haven't been acquired at all!
A: The BlackBridge Archive / EyeFind contains all the image data acquired by the RapidEye satellites, as long as it meets the quality standards outlined in the RapidEye Product Specification document and is not restricted by special agreement. This includes all the data acquired in response to customer orders, as long as it meets the two criteria above.

Q: How can I purchase DEMs from BlackBridge?
A: BlackBridge does not currently sell digital elevation models (DEMs) derived from RapidEye satellite images as a standard product.

Q: How can I translate the radiance values of a RapidEye image product into reflectance values?
A: The digital numbers of the RapidEye image pixels represent

  • absolute calibrated radiance values for non atmospheric corrected images
  • reflectance values for atmospheric corrected images (currently not offered for delivery)

To convert the Digital Number (DN) of a pixel to radiance it is necessary to multiply the DN value by the radiometric scale factor, as follows: RAD(i) = DN(i) * radiometricScaleFactor(i)

The resulting value is the Top of Atmosphere (TOA) radiance of that pixel in watts per steradian per square meter (W/m2 sr μm). The radiometric scale factor for each band can be found in the image XML metadata file under the band specific metadata. Reflectance is generally the ratio of the reflected radiance divided by the incoming radiance. Note, that this ratio has a directional aspect. To turn radiances into a reflectance it is necessary to relate the radiance values (i.e. the pixel DNs) to the radiance the object is illuminated with. This is often done by applying an atmospheric correction software to the image, because this way the impact of the atmosphere to the radiance values is eliminated at the same time. But it would also be possible to neglect the influence of the atmosphere by calculating the Top Of Atmosphere (TOA) reflectance taking into consideration only the sun distance and the geometry of the incoming solar radiation.

The formula to calculate the TOA reflectance not taking into account any atmospheric influence is as follows:


  • i: Number of the spectral band
  • REF: reflectance value
  • RAD: Radiance value
  • SunDist: Earth-Sun Distance at the day of acquisition in Astronomical Units (Note: This value is not fix, it varies between 0.983 289 8912 AU and 1.016 710 3335 AU and has to be calculated for the image acquisition point in time.
  • EAI: Exo-Atmospheric Irradiance
  • SolarZenit: Solar Zenith angle in degrees (= 90° – sun elevation)

For RapidEye the EAI values for the 5 bands are:

Band Chance Kurucz 1997 Thuillier 1997 SIRS WRC Kurucz 1997 New Kurucz 2005
Blue 1950 W/m²μm 2003 W/m²μm 1989 W/m²μm 1969 W/m²μm 2003 W/m²μm 1998 W/m²μm
Green 1815 W/m²μm 1824 W/m²μm 1848 W/m²μm 1853 W/m²μm 1816 W/m²μm 1863 W/m²μm
Red 1566 W/m²μm 1541 W/m²μm 1531 W/m²μm 1562 W/m²μm 1573 W/m²μm 1560 W/m²μm
Red-Edge 1352 W/m²μm 1399 W/m²μm 1362 W/m²μm 1387 W/m²μm 1392 W/m²μm 1395 W/m²μm
NIR 1121 W/m²μm 1117 W/m²μm 1100 W/m²μm 1127 W/m²μm 1121 W/m²μm 1124 W/m²μm


Q: What are the gain and offset values to convert RapidEye DNs into Radiances?
A: BlackBridge already performs the full radiometric calibration chain with the radiometric correction of the image products. The DN Values of all RapidEye products are already converted to radiance. To convert the DNs into the common W/(m^2*µm*str)  scale just the constant scale factor given in the metadata file
(<re:radiometricScaleFactor>9.999999776482582e-03</re:radiometricScaleFactor>) needs to be applied. This factor is kept constant during the whole mission lifetime.

Satellite Tasking

Q: How do I request satellite tasking for new imagery acquisition?
A: Satellite tasking is made easy by contacting our Customer Support Team (CST) by sending an email to or dialing +49-30-6098300-555. You will be asked to define your area of interest (AOI), time of interest (TOI), end-user license type (please refer to our End-User License Agreement) and cloud cover (CC) threshold. Our CST will provide you with a feasibility report considering your specified acquisition criteria. Additionally you will be provided with an estimated imagery acquisition time frame and a quotation outlining the associated costs.

Q: What is the minimum order size?
A: The minimum order size for new acquisitions is a contiguous AOI of 3,500 km². However, for corridor-like AOIs, an in-depth feasibility analysis will be required.

Q: How can I define my AOI?
A: You can provide your AOI in the following format:

  • Map Projection: Geographic (lat/long)
  • Datum: WGS84
  • File Format: ESRI Shapefile (.shp) or Keyhole Markup Language (.kmz /.kml)

If you are unable to provide an AOI in shapefile or kml format, please provide:

  • If the AOI is rectangular or square, you can define a bounding box by defining the corner coordinates of the order polygon in decimal degrees (lat/long)
  • If the AOI is round, you can define the center point in Lat/long and specify the radius or buffer zone.

Q: What are the (un)acceptable polygon shapes?
A: It is advised to avoid very narrow areas as well as area-around-area shapes (doughnut form). In some cases, for example pipeline monitoring projects, the AOI is often very narrow but lengthy. For such projects it is best to allow a pipeline width of at least 10 km. The best way to check whether your AOI is feasible or not is to contact your CST.

Q: Can I place a tasking order for all 5 satellites over my AOI? Can I define which satellite(s) I would like to collect imagery over my AOI?
A: Unfortunately, this is not possible due to the complexity of pre-acquisition planning. This specific procedure takes into consideration the number of running projects and their specific criteria for every single satellite. Moreover, our 5 satellites are identical in regard to their technical specifications and there would be no difference in imagery taken by different satellites.

Q: The weather forecast over my AOI within the next couple of days is promising cloud- free and sunny weather. Can you program the satellites to acquire my AOI accordingly?
A: Our planning system runs in a way that it cannot be influenced by "external" weather forecast information. Take in mind that the system has to consider not only your AOI(s) but all of the AOIs which lay on the same orbit. Yet, you can be sure that we pay the greatest attention to your orders and that we do our best in order to acquire the needed imagery within the contractual terms.

Q: How much does satellite tasking cost?
A: Our base price for new acquisitions is 0.95 EUR/km². Yet, the actual costs will depend on the size of an AOI, the weather forecast for the AOI within the specified TOI, license type, the number of coverages and preferred delivery options. Our CST will provide you with the tasking fee(s) before the acquisitions begin. For more details on our pricing policy for satellite tasking please contact our CST directly at

Q: How long does it take to acquire new data?
A: Acquisitions of smaller AOIs with good weather forecast can be collected within a couple of days. Larger AOIs may take up to a few weeks or even months to fully acquire, depending on the specified tasking requirements and the weather conditions. Please note that BlackBridge can only commit to collecting new imagery on a best effort basis, esp. for AOIs with unfavorable weather forecasts (e.g. tropical regions).

Q: How do I know when my data has been acquired?
A: Your CST is constantly monitoring your imagery acquisitions. You will be informed within 48 hours after successful completion of your imaging. When collecting a large area over a long acquisition window, your CST is able to provide you with a collection status report upon request.

Q: Does the cloud cover threshold indicated in the quality requirements refer to the total AOI or to every single image delivered?
A: The CC threshold always refers to the total AOI and not to the single images delivered. Some of the products could in fact exceed the value stated into the contract if the overall average is respected. However, we always do our best to deliver top quality data, avoiding large and compact cloud areas.

Q: Does the cloud cover calculation of an area include the shadows produced by the clouds and the haze?
A: The shadows produced by the clouds and light haze are not computed as unusable data.

Q: Do I have to pay if my AOI has only been partially covered within the specified TOI?
A: In case of a partial AOI coverage within the given TOI, you have the option to extend the TOI to allow for further acquisitions of your AOI until fully covered. Otherwise the partial coverages are invoiced according to the AOI size acquired that fits your specified requirements. For example, if only 85% of your AOI could be imaged withing the given TOI but only 60% of the acquired imagery fits the total defined requirements, only 60% of your AOI will be invoiced.

Q: Is an extension of the TOI possible without additional costs?
A: It depends on the weather forecast for the extended TOI. By bad weather conditions, uplifts may apply. In such cases, please contact your CST for clarifications.

Q: Can RapidEye satellites image areas on the night side of the Earth?
A: The imaging instruments of the RapidEye satellites are designed to acquire high quality and high performance image data on the day side of the Earth. The instrument’s optics and its light sensitive sensors are optimized for the bright illumination conditions on the Earth’s surface at around 11:00 local time, close to local noon. The sensitivity of the RapidEye imaging instruments cannot be adjusted. During night time the illumination of the Earth’s surface is magnitudes lower than during day time. Under such dark conditions, the imaging instruments will not acquire a sufficient amount of light to provide any useful data information. Images taken on the night side, even over strongly illuminated urban regions, just appear black, without any recognizable feature.

Product Viewing

Q: Which commercial GIS software packages support the import and processing of RapidEye image products?
A: All commercially available GIS software should support the import and processing of RapidEye imagery using generic routines for the different image products, GeoTiFF for the 3A products and NITF 2.0 for the 1B products. In some cases the software providers may charge a separate license fee for the handling of NITF files. In addition to the generic import routines, BlackBridge has worked with a number of software vendors to support the specific use of RapidEye imagery and its metadata. In most cases this work has been focused on the importation and handling of the 1B Basic image product. To date the following software packages support the specific use of RapidEye image products:
  • BAE SocetSet v5.6 and later
  • ENVI v4.7 or later
  • ESRI ArcGIS v10.0 or later
  • PCI Geomatica v10.2.1 or later

Q: What can I do if I do not have a suitable GIS/remote sensing software package?
A: Most of the software companies provide basic viewer programs that are free of charge. Most can be downloaded from the Internet, e.g. for the PCI free viewer go to:
Q: Can I view the products with a common image viewer because I do not want to download or install a special GIS/remote sensing software package?
A: The GIS/remote sensing programs referred to above are the best approach. Most common basic image processing viewers cannot handle our products with their five bands and 16 bit data. It may happen that these image viewers will only display a black square. In this case, the one remaining option is to view the reduced resolution browse image that accompanies all products. These browse images do not fully represent all the attributes of the parent images. Note also that few if any of the common viewers will open the NITF files of the 1B product.
Q: When I view the products with my GIS/remote sensing software the colors I see are very pale and/or dark. What can I do?
A: For proper viewing please use the image enhancement tools of your image viewer package and adjust the contrast of the imagery as necessary.
Q: When I view the products with an image processing software like Adobe Photoshop I see the colors very pale and/or dark and the bands seem to be mixed up. What can I do?
A: Advanced image software programs, like newer versions of Adobe Photoshop, will be able to read and view the first three bands of the RapidEye products. Adjusting the levels and changing the channel combination using the channel mixer might be necessary to achieve a natural look for the imagery. Furthermore, try adjusting the contrast of the imagery for proper viewing.

Purchasing Products

Q: Do you grant research/scientific use discounts?
A: BlackBridge does not offer a standard research discount. BlackBridge is involved in a DLR RESA program that offers scientific use imagery grants on a case by case basis for researchers associated with German institutions. For more information see: For researchers not associated with a German institution, please contact BlackBridge directly.
Q: Can you provide me with both 1B and 3A data over my AOI?
A: Yes, we can provide you with both 1B and 3A data for the same AOI, according to the price list. Please note that each product is priced separately.
Q: How do I buy data if there is no distributor for my area?
A: You can purchase data directly from BlackBridge if your region does not have a designated distributor. You can email your request and additional questions to:
Q: What license types does BlackBridge offer?
BlackBridge offers multiple different license types. For more details of licenses, please contact your local distributor.


Q: Is an RPC file available with each (1B) delivery?
A: The RPC information is found in the NITF file header of each 1B – Basic Product image. At the current time it is not included as a separate file.
Q: Do you provide a Camera model with your deliveries?
A: No. The RapidEye 1B- Basic Product images are reformatted to an “idealized” camera model. These parameters are provided within the XML metadata file. At this time, most major GIS software packages have automatic import routines for RapidEye data and this information is not explicitly needed by the customer to successfully use the image product.
Q: If I order tasking of L3A with an imaging window of 45 days, do I receive my data A) just after the end of TOI, B) within TOI if the acquisition is successfully completed or C) can I get instant (e.g. 48+ hours) access to all the already acquired data during TOI?
A: BlackBridge starts delivering data as soon as it has been collected and processed. That means that you do not have to wait until the acquisition has been completed or the acquisition window is over to start getting data.
Q: If the likelihood of not covering my AOI during the TOI increases, does BlackBridge proactively inform me about a potential necessary extension of TOI? And when? Just after end of TOI?
A: BlackBridge is continuously monitoring your orders. If our experts foresee any risk to complete the acquisition over your AOI, they will get in contact with you before the end of the TOI to inform you and discuss different possibilities (e.g. extension of the time window, use archive imagery).
Q: Do the names of the folders refer to the acquisition or to the delivery date?
A: The names of the folders (delivery folders) at top level of the delivery link refer to the date of delivery. If you want to know the acquisition date of the data, you will find it in the product folder name. Product folders are located inside the delivery folders.
Q: What's the utility of the .md5 file?
A: The .md5 file is a checksum file. It can be used in conjunction with certain software packages to help indicate if all the expected files for a delivery have been properly downloaded. It allows the user to be sure that have all the file information and nothing critical is missing.
Q: Why is the AOI shapefile file slightly different from the one I submitted?
A: The AOI shapefile present in the delivery is derived from the AOI shapefile used to put the order. This file may have been slightly modified by removing un-necessary points within the AOI that are not needed to cover the area of the order.
Q: What useful information can I find in the attribute table of the delivery shapefile?
A: Each delivery shapefile contains polygons for all the images delivered. For each polygon there is information related to each image. This information includes:
  • Name -
  • Tile ID (only for 3A – Ortho products) -
  • Order ID -
  • Acquisition date of the image -
  • View angle of the image -
  • Unusable data percentage (UDP) -
  • Cloud cover percentage -
  • Catalog ID for the image -
  • Product level of the image

Q: What's the meaning of UDP (attribute of the shapefile)?
A: UDP stands for Unusable Data Percentage and is the percentage of the image which contains unusable imagery such as clouds, blackfill or missing lines of data (if present).
Q: What's the meaning of CCP (attribute of the shapefile)?
A: CCP stands for Cloud Cover Percentage and is the percent of the image that is covered by clouds.
Q: Why is the catalogue ID of the product I received different from the one that I ordered through EyeFind?
A: The catalog IDs available in EyeFind are the unique IDs that our system gives to the images once they are downloaded from the satellites and archived in our catalog. These images can be then ordered and processed at whatever processing level. After the processing, the images are RapidEye products and thus receive a new catalog ID. This image product catalog ID is readable in the attribute table of the delivery shape? file.
Q: How long will the data be available for download?
A: The data is available in the FTP folder for 14 days after having been uploaded.
Q: Is it possible to extend the availability time of an ftp delivery link?
A: Yes, it is possible. If you require it, please contact us well in advance.
Q: When I purchase a mosaic, do I get the original data with it?
A: A standard mosaic delivery only contains mosaic data. If you also want to purchase the data that were used to produce it, please specify it at the time of requesting a quote.
Q: Do you deliver via WMS or WCS?
A: Currently, RapidEye products are only delivered via FTP or hard drive.
Q: What is the best way to download the data from your FTP server?
A: To make it fast and efficient, involving as little manual effort as possible, we recommend to use an FTP or download client. There are many of them available on the internet at no cost.

Distributor Information

Q: How do I become a distributor?
A: Contact us at: for further information.

Q: How do I find out who the distributor is in my area?
A: Navigate here to find out who the distributor is for your region. If a distributor for your region does not exist, please contact BlackBridge directly at: