STARS Project
STARS Project

Refine your search

Knowledge Portal


Amorós-López, J., Gómez-Chova, L., Alonso, L., Guanter, L., Zurita-Milla, R., Moreno, J., and Camps-Valls, G., 2013. Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring. International Journal of Applied Earth Observation and Geoinformation, 23, 132–141.

Chavez, P.S., Sides, S.C., and Anderson, J.A., 1991. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogrammetric Engineering & Remote Sensing, 57 (3), 295–303.

Choi, M., Kim, R.Y., Nam, M.-R., and Kim, H.O., 2005. Fusion of Multispectral and Panchromatic Satellite Images Using the Curvelet Transform. IEEE Geoscience and Remote Sensing Letters, 2 (2), 136–140.

Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P., 2012. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25–36.

Forkuor, G., 2014. Agricultural Land Use Mapping in West Africa Using Multi-sensor Satellite Imagery. THES. University of Wuerzburg.

Forkuor, G., Conrad, C., Thiel, M., Ullmann, T., and Zoungrana, E., 2014. Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa. Remote Sensing, 6 (7), 6472–6499.

Fu, D., Chen, B., Wang, J., Zhu, X., and Hilker, T., 2013. An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model. Remote Sensing, 5 (12), 6346–6360.

Gao, F., Masek, J., Schwaller, M., and Hall, F., 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. Geoscience and Remote Sensing, IEEE Transactions on.

Hazaymeh, K. and Hassan, Q.K., 2015. Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images. Journal of Applied Remote Sensing, 9 (1), 96095.

Hilker, T., Wulder, M.A., Coops, N.C., Linke, J., McDermid, G., Masek, J.G., Gao, F., and White, J.C., 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113 (8), 1613–1627.

Inglada, J., Arias, M., Tardy, B., Hagolle, O., Valero, S., Morin, D., Dedieu, G., Sepulcre, G., Bontemps, S., Defourny, P., and Koetz, B., 2015. Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery. Remote Sensing, 7 (9), 12356–12379.

Irons, J.R., Dwyer, J.L., and Barsi, J.A., 2012. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment, 122, 11–21.

Lobell, D.B. and Asner, G.P., 2004. Cropland distributions from temporal unmixing of MODIS data. Remote Sensing of Environment, 93 (3), 412–422.

Malenovský, Z., Rott, H., Cihlar, J., Schaepman, M.E., García-Santos, G., and Fernandes, R., 2012. Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sensing of Environment, 120, 91–101.

Markham, B.L., Haque, M.O., Barsi, J.A., Micijevic, E., Helder, D.L., Thome, K.J., Aaron, D., and Czapla-Myers, J.S., 2012. Landsat-7 ETM+: 12 Years On-Orbit Reflective-Band Radiometric Performance. IEEE Transactions on Geoscience and Remote Sensing, 50 (5), 2056–2062.

Markham, B.L. and Helder, D.L., 2012. Forty-year calibrated record of earth-reflected radiance from Landsat: A review. Remote Sensing of Environment, 122, 30–40.

McNairn, H., Champagne, C., Shang, J., Holmstrom, D., and Reichert, G., 2009. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 64 (5), 434–449.

Ozdarici-Ok, A., Ok, A., and Schindler, K., 2015. Mapping of Agricultural Crops from Single High-Resolution Multispectral Images—Data-Driven Smoothing vs. Parcel-Based Smoothing. Remote Sensing, 7 (5), 5611–5638.

Paloscia, S., Pettinato, S., Santi, E., Notarnicola, C., Pasolli, L., and Reppucci, A., 2013. Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation. Remote Sensing of Environment, 134, 234–248.

Paré, S., Söderberg, U., Sandewall, M., and Ouadba, J.M., 2008. Land use analysis from spatial and field data capture in southern Burkina Faso, West Africa. Agriculture, Ecosystems & Environment, 127 (3–4), 277–285.

Ramoelo, A., Cho, M., Mathieu, R., and Skidmore, A.K., 2015. Potential of Sentinel-2 spectral configuration to assess rangeland quality. Journal of Applied Remote Sensing, 9 (1), 94096.

Ranchin, T. and Wald, L., 2000. Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation. Photogrammetric engineering and remote sensing, 66 (1), 49–61.

Richter, K., Hank, T.B., Vuolo, F., Mauser, W., and D’Urso, G., 2012. Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping. Remote Sensing, 4 (12), 561–582.

Roy, D.P., Wulder, M.A., Loveland, T.R., C.E., W., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hipple, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z.P., Lymburner, L., Masek, J.G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H., and Zhu, Z., 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172.

Shah, V.P., Younan, N.H., and King, R.L., 2008. An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets. IEEE Transactions on Geoscience and Remote Sensing, 46 (5), 1323–1335.

Shi, W., Zhu, C., Tian, Y., and Nichol, J., 2005. Wavelet-based image fusion and quality assessment. International Journal of Applied Earth Observation and Geoinformation, 6 (3), 241–251.

Song, H., Huang, B., Liu, Q., and Zhang, K., 2015. Improving the Spatial Resolution of Landsat TM/ETM+ Through Fusion With SPOT5 Images via Learning-Based Super-Resolution. IEEE Transactions on Geoscience and Remote Sensing, 53 (3), 1195–1204.

Tappan, G.G., Hadj, A., Wood, E.C., and Lletzow, R.W., 2000. Use of Argon, Corona, and Landsat Imagery to Assess 30 Years of Land Resource Changes in West-Central Senegal. Photogrammetric engineering and remote sensing, 66 (6), 727–735.

Thenkabail, P.S. and Wu, Z., 2012. An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data. Remote Sensing, 4 (12), 2890–2918.

Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I.N., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L’Abbate, M., Croci, R., Pietropaolo, A., Huchler, M., and Rostan, F., 2012. GMES Sentinel-1 mission. Remote Sensing of Environment, 120, 9–24.

Upadhyay, P., Kumar, A., Roy, P.S., Ghosh, S.K., and Gilbert, I., 2012. Effect on specific crop mapping using WorldView-2 multispectral add-on bands: soft classification approach. Journal of Applied Remote Sensing, 6 (1), 63524–1.

Upla, K.P., Joshi, S., Joshi, M. V., and Gajjar, P.P., 2015. Multiresolution image fusion using edge-preserving filters. Journal of Applied Remote Sensing, 9 (1), 96025.

Verrelst, J., Muñoz, J., Alonso, L., Delegido, J., Rivera, J.P., Camps-Valls, G., and Moreno, J., 2012. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3. Remote Sensing of Environment, 118, 127–139.

Vittek, M., Brink, A., Donnay, F., Simonetti, D., and Desclée, B., 2014. Land Cover Change Monitoring Using Landsat MSS/TM Satellite Image Data over West Africa between 1975 and 1990. Remote Sensing, 6 (1), 658–676.

Watts, J.D., Powell, S.L., Lawrence, R.L., and Hilker, T., 2011. Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery. Remote Sensing of Environment, 115 (1), 66–75.

Wilson, T.A., Rogers, S.K., and Kabrisky, M., 1997. Perceptual-based image fusion for hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 35 (4), 1007–1017.

Zhu, X.L., Chen, J., Gao, F., Chen, X.H., and Masek, J.., 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114, 2610–2623.