Article Title :
Remote Sensing Satellites for Land Applications: A Review
Remote sensing satellites , multispectral , hyperspectral , Microwave , LiDAR , UAV platforms , High-definition video from space
Satellite remote sensing offers a unique opportunity in deriving various components of land information by integrating with ground based observation. Currently several remote sensing satellites are providing multispectral, hyperspectral and microwave data to cater the need of various land applications. Several old age remote sensing satellites have been updated with new generation satellites offering high spatial, spectral and temporal resolution. Microwave remote sensing data is now available with high spatial resolution and providing land information in cloudy weather condition that strengthening availability of remote sensing data in all days. Spatial resolution has significantly improved over the decades and temporal resolution has improved from months to daily. Indian Remote Sensing programs are providing state of the art satellite data in optical and microwave wavelength regions to meet large land applications in the country. Today several remote sensing data is available as open data sources. Upcoming satellite remote sensing data will help in precise characterization and quantification of land resources to support in sustainable land development planning to meet future challenges.
Satellite remote sensing offers a unique opportunity in deriving various components of the Earth’s surface.
Several remote sensing satellites provide multispectral, hyperspectral and microwave data for various land applications.
Old age remote sensing satellites have been updated with new generation satellites.
Microwave remote sensing radiometers provide data with high spatial resolution in all weather conditions.
Indian remote sensing program is providing state of the art satellite data in optical and microwave wavelength regions.
Recent progress of mini unmanned airborne vehicles (UAVs) emerged as most promising high resolution remote sensing imagery for land applications.
Upcoming satellite remote sensing data will be helpful in precise characterization and quantification of land resources for sustainable land management.
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 Sens. Environ., 120, 25-36.
Ghosh, R., Padmanabhan, N., Patel, K. C. and Siyolkar, R., 2012. Soil fertility parameter retrieval and mapping using hyperion data. Investigations on Hyperspectral Remote Sensing Applications (Ed. Panigrahy, S. and Manjunath, K. R.), Space Applications Centre (ISRO), Ahmedabad, 29-31.
Han, F., Zhang, Z., Wang, Z., Zhang, S., Liang, Y., and Chen, Y., 2015. Real-time earth observation system based on micro-nano networks. Proceedings of the International Astronautical Congress, IAC, 12-16, October 2015, Jerusalem, Israel, 3271-3278.
Mahoney, S., James, P., Mauger, A. and Heinson, G., 2002. Geologic and regolith mapping for mineral exploration in the Gawler Craton of South Australia using Hyperion and other remote sensing techniques. International Geoscience and Remote Sensing Symposium (IGARSS) 02. Proceedings. 2002 IEEE International, 1779-1781.
Marcucci, E., Saiet, E. and Hatfield, M. C., 2014. A new hyperspectral designed for small UAS tested in real world applications. In: AGU Fall Meeting, 15-19, December, 2014, San Francisco, California, USA.
Starek, M. and Jung, J., 2015. The state of LiDAR for UAS applications. LiDAR’s Next Geospatial Frontier, UAS Special of GIM International, 25-27.
Villien, A., Pedersen, G., Charvet, P., and Maliet, E., 2014. Geostationary observation space surveillance system (GO3S) - Real time video from space, Proceedings of the International Astronautical Congress, IAC, 29 September-3 October, 2014, Toronto, Canada, 2460-2464.
Yang, G., Liu, J., Zhao, C.,Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R., Feng, H., Zhao, H., Li, Z., Li, H. and Yang, H., 2017. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Front. Plant Sci., 8, 26.