American Journal of Remote Sensing

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Remote Sensing for Land Resources: A Review on Satellites, Data Availability and Applications

Remote sensing is a technology that offers a unique opportunity of gathering land information by measuring and recording its emitted and reflected energy usually from a satellite or an aircraft. The capabilities of remote sensing satellite data in mapping, monitoring and managing land resources are intensifying with the rapid advancements in satellite technology. In addition, increased users demand in sustainable management of land resources has escalated the need for remote sensing technology. As a result, this article presents an overview of the remote sensing satellites that are best for mapping land resources and monitoring, focusing specifically on the necessary satellites, data availability and key land application areas. Currently, several remote sensing satellites are providing microwave, multispectral and hyperspectral data with a wide array of spatial, temporal and spectral resolutions used on land applications. Microwave remote sensing has seen the development of both active and passive remote sensing systems for remote sensing activities. Consequently, microwave data is now available with high spatial resolution and providing land information in all cloudy weather condition. On the other hand, optical remote sensing is providing space-based remote sensing data in a variety of spatial, spectral and temporal resolutions meeting the needs of many land applications. Similarly, hyperspectral remote sensing is providing digital imagery of earth resources in many narrow contiguous spectral bands. Additionally, other remote sensing techniques like Unmanned Aerial Vehicles (UAV) and Light Detection and Ranging (LiDAR) have helped in deriving detailed information of land resources to support land related studies. Besides having commercial satellites that are providing satellite data at a high cost, today several remote sensing data have been made available from open data sources and users can freely search and download areas of interest.

Land Resources, Remote Sensing Satellites, Data Availability, Land Resource Monitoring

APA Style

Winfred Mbinya Manetu, John Momanyi Mironga, Jackob Haywood Ondiko. (2023). Remote Sensing for Land Resources: A Review on Satellites, Data Availability and Applications. American Journal of Remote Sensing, 10(2), 39-49. https://doi.org/10.11648/j.ajrs.20221002.12

ACS Style

Winfred Mbinya Manetu; John Momanyi Mironga; Jackob Haywood Ondiko. Remote Sensing for Land Resources: A Review on Satellites, Data Availability and Applications. Am. J. Remote Sens. 2023, 10(2), 39-49. doi: 10.11648/j.ajrs.20221002.12

AMA Style

Winfred Mbinya Manetu, John Momanyi Mironga, Jackob Haywood Ondiko. Remote Sensing for Land Resources: A Review on Satellites, Data Availability and Applications. Am J Remote Sens. 2023;10(2):39-49. doi: 10.11648/j.ajrs.20221002.12

Copyright © 2022 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. P. S. Roy, M. D. Behera, and S. K. Srivastav, “Satellite Remote Sensing: Sensors, Applications and Techniques,” Proc. Natl. Acad. Sci. India Sect. A - Phys. Sci., vol. 87, no. 4, pp. 465–472, 2017.
2. B. H. M. Miller, L. Richardson, S. R. Koontz, J. Loomis, L. Koontz, and S. Jewell, “Users, Uses, and Value of Landsat Satellite Imagery — Results from the 2012 Survey of Users,” p. 51, 2013.
3. D. S. Peter Backlund, Anthony Janetos, “The Effects of Climate Change on Agriculture, Land Resources, Water Resources, and Biodiversity in the United States,” no. May, 2008.
4. S. K. Seelan, S. Laguette, G. M. Casady, and G. A. Seielstad, “Remote sensing applications for precision agriculture: A learning community approach,” Remote Sens. Environ., vol. 88, no. 1–2, pp. 157–169, 2003.
5. Q. Wu, “GIS and Remote Sensing Applications in Wetland Mapping and Monitoring,” Compr. Geogr. Inf. Syst., vol. 3, no. September 2017, pp. 140–157, 2017.
6. P. K. Kingra, D. Majumder, and S. P. Singh, “Application of Remote Sensing and Gis in Agriculture and Natural Resource Management Under Changing Climatic Conditions,” Agric. Res. J., vol. 53, no. 3, p. 295, 2016.
7. P. M. Treitz, P. J. Howarth, and Peng Gong, “Application of satellite and GIS technologies for land-cover and land-use mapping at the rural-urban fringe: a case study,” Photogrammetric Engineering & Remote Sensing, vol. 58, no. 4. pp. 439–448, 1992.
8. X. Jin et al., “A review of data assimilation of remote sensing and crop models,” Eur. J. Agron., vol. 92, no. May 2017, pp. 141–152, 2018.
9. M. Abdi, “Integrating open access geospatial data to map the habitat suitability of the declining corn bunting (Miliaria calandra),” ISPRS Int. J. Geo-Information, vol. 2, no. 4, pp. 935–954, 2013.
10. R. Harris and I. Baumann, “Open data policies and satellite Earth observation,” Space Policy, vol. 32, pp. 44–53, 2015.
11. W. Turner et al., “Free and open-access satellite data are key to biodiversity conservation,” Biol. Conserv., vol. 182, pp. 173–176, 2015.
12. Castillo, J. Estrada, P. Perez, and S. Soto, “High-Speed VLSI Architecture Based on Massively Parallel Processor Arrays for Real-Time Remote Sensing Applications,” Appl. Digit. Signal Process., 2011.
13. D. B. Lobell, G. P. Asner, J. I. Ortiz-Monasterio, and T. L. Benning, “Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties,” Agric. Ecosyst. Environ., vol. 94, no. 2, pp. 205–220, 2003.
14. K. Prasad, L. Chai, R. P. Singh, and M. Kafatos, “Crop yield estimation model for Iowa using remote sensing and surface parameters,” Int. J. Appl. Earth Obs. Geoinf., vol. 8, no. 1, pp. 26–33, 2006.
15. D. Lu, Q. Chen, G. Wang, L. Liu, G. Li, and E. Moran, “A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems,” Int. J. Digit. Earth, vol. 9, no. 1, pp. 63–105, 2016.
16. W. Abera, L. Tamene, A. Abegaz, and D. Solomon, “Understanding climate and land surface changes impact on water resources using Budyko framework and remote sensing data in Ethiopia,” J. Arid Environ., vol. 167, no. April, pp. 56–64, 2019.
17. Singh, “Managing the salinization and drainage problems of irrigated areas through remote sensing and GIS techniques,” Ecol. Indic., vol. 89, no. February, pp. 584–589, 2018.
18. D. Kim et al., “Monitoring river basin development and variation in water resources in transboundary Imjin River in North and South Korea using remote sensing,” Remote Sens., vol. 12, no. 1, 2020.
19. C. Grinand, G. Le Maire, G. Vieilledent, H. Razakamanarivo, T. Razafimbelo, and M. Bernoux, “Estimating temporal changes in soil carbon stocks at ecoregional scale in Madagascar using remote-sensing,” Int. J. Appl. Earth Obs. Geoinf., vol. 54, pp. 1–14, 2017.
20. S. Jerie, “Site Suitability Analysis for Solid Waste Landfill Site Location Using Geographic Information Systems and Remote Sensing: a Case Study of Banket Town Board, Zimbabwe,” Rev. Soc. Sci., vol. 2, no. 4, pp. 19–31, 2017.
21. Dubovyk, “The role of Remote Sensing in land degradation assessments: opportunities and challenges,” Eur. J. Remote Sens., vol. 50, no. 1, pp. 601–613, 2017.
22. H. Y. Li et al., “Remote sensing investigation of anthropogenic land cover expansion in the low-elevation coastal zone of Liaoning Province, China,” Ocean Coast. Manag., vol. 148, no. November, pp. 245–259, 2017.
23. D. Gilvear and R. Bryant, Analysis of remotely sensed data for fluvial geomorphology and river science, no. April. 2016.
24. H. van der Werff and F. van der Meer, “Sentinel-2A MSI and Landsat 8 OLI provide data continuity for geological remote sensing,” Remote Sens., vol. 8, no. 11, 2016.
25. S. Xu, L. Qing, L. Han, M. Liu, Y. Peng, and L. Shen, “A new remote sensing images and point-of-interest fused (RPF) model for sensing urban functional regions,” Remote Sens., vol. 12, no. 6, 2020.
26. B. Gokaraju, R. A. A. Nobrega, D. A. Doss, A. C. Turlapaty, and R. C. Tesiero, “Data fusion of multi-source satellite data sets for cost-effective disaster management studies,” Conf. Proc. - IEEE SOUTHEASTCON, no. February 2018, 2017.
27. R. Padmanaban, A. K. Bhowmik, and P. Cabral, “A remote sensing approach to environmental monitoring in a reclaimed mine area,” ISPRS Int. J. Geo-Information, vol. 6, no. 12, 2017.
28. T. Misra, “Indian Remote Sensing Sensor System: Current and Future Perspective,” Proc. Natl. Acad. Sci. India Sect. A - Phys. Sci., vol. 87, no. 4, pp. 473–486, 2017.
29. Al-Yaari et al., “The AQUI soil moisture network for satellite microwave remote sensing validation in South-Western France,” Remote Sens., vol. 10, no. 11, pp. 1–22, 2018.
30. D. K. M. and P. V. N. Rao, “Surface Soil Moisture Estimation Using Passive Microwave Radiometer Data,” no. January 2014, 2014.
31. M. Owe, R. de Jeu, and T. Holmes, “Multisensor historical climatology of satellite-derived global land surface moisture,” J. Geophys. Res. Earth Surf., vol. 113, no. 1, pp. 1–17, 2008.
32. Y. H. Kerr, P. Waldteufel, J. P. Wigneron, J. M. Martinuzzi, J. Font, and M. Berger, “Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 8, pp. 1729–1735, 2001.
33. D. Entekhabi et al., “The soil moisture active passive (SMAP) mission,” Proc. IEEE, vol. 98, no. 5, pp. 704–716, 2010.
34. R. L. Ray, A. Fares, Y. He, and M. Temimi, “Evaluation and inter-comparison of satellite soil moisture products using in situ observations over Texas, U.S.,” Water (Switzerland), vol. 9, no. 6, 2017.
35. J. Peng and A. Loew, “Recent advances in soil moisture estimation from remote sensing,” Water (Switzerland), vol. 9, no. 7, pp. 1–5, 2017.
36. H. S. Solberg, “Remote sensing of ocean oil-spill pollution,” Proc. IEEE, vol. 100, no. 10, pp. 2931–2945, 2012.
37. S. Kuntz, F. Von Poncet, M. Schlund, A. K. Steffen Kuntz Foster Mensah, F. Poncet, and M. Köhl, “Supporting Ghana in forest monitoring based on German remote sensing technology Supporting Ghana in forest monitoring based on German remote sensing technology Final Report,” no. November, 2013.
38. M. Yang and Z. Jian, “Bridge detection in high-resolution X-band SAR images by combined statistical and topological features,” Prog.
39. L. Zhu, J. P. Walker, N. Ye, and C. Rüdiger, “Roughness and vegetation change detection: A pre-processing for soil moisture retrieval from multi-temporal SAR imagery,” Remote Sens. Environ., vol. 225, no. February, pp. 93–106, 2019.
40. O. Onojeghuo, G. A. Blackburn, J. Huang, D. Kindred, and W. Huang, “Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges and opportunities,” Int. J. Appl. Earth Obs. Geoinf., vol. 64, no. January 2017, pp. 62–86, 2018.
41. F. Tian et al., “Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel,” Remote Sens. Environ., vol. 177, pp. 265–276, 2016.
42. B. Franch et al., “A 30+ year AVHRR land surface reflectance climate data record and its application to wheat yield monitoring,” Remote Sens., vol. 9, no. 3, pp. 1–14, 2017.
43. J. A. Sobrino, M. Gómez, J. C. Jiménez-Muñoz, and A. Olioso, “Application of a simple algorithm to estimate daily evapotranspiration from NOAA-AVHRR images for the Iberian Peninsula,” Remote Sens. Environ., vol. 110, no. 2, pp. 139–148, 2007.
44. F. Maignan, F. M. Bréon, C. Bacour, J. Demarty, and A. Poirson, “Interannual vegetation phenology estimates from global AVHRR measurements. Comparison with in situ data and applications,” Remote Sens. Environ., vol. 112, no. 2, pp. 496–505, 2008.
45. R. J. W. Brewin et al., “Evaluating operational AVHRR sea surface temperature data at the coastline using benthic temperature loggers,” Remote Sens., vol. 10, no. 6, 2018.
46. S. Kirtiloglu, O. Orhan, and S. Ekercin, “A map Mash-Up application: Investigation the temporal effects of climate change on Salt Lake Basin,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 41, no. July, pp. 221–226, 2016.
47. D. Mukaneza, L. Qiao, W. Pengxin, L. Yan, and C. Yingyi, “Study on Changes of Land Use impacting the Process of Urbanization, by Using Landsat Data in African Regions : A Case Study in Kigali, Rwanda,” Int. J. Environ. Ecol. Eng., vol. 11, no. 9, pp. 784–788, 2017.
48. B. Zhang, L. Zhang, D. Xie, X. Yin, C. Liu, and G. Liu, “Application of synthetic NDVI time series blended from landsat and MODIS data for grassland biomass estimation,” Remote Sens., vol. 8, no. 1, pp. 1–21, 2016.
49. J. H. Meddens, C. A. Kolden, and J. A. Lutz, “Detecting unburned areas within wildfire perimeters using Landsat and ancillary data across the northwestern United States,” Remote Sens. Environ., vol. 186, pp. 275–285, 2016.
50. F. Hu, X. M. Gao, G. Y. Li, and M. Li, “DEM extraction from worldview-3 stereo-images and accuracy evaluation,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 2016-January, no. July, pp. 327–332, 2016.
51. B. Ye, S. Tian, J. Ge, and Y. Sun, “Assessment of WorldView-3 data for lithological mapping,” Remote Sens., vol. 9, no. 11, pp. 1–19, 2017.
52. H. Rizeei and B. Pradhan, “Urban Mapping Accuracy Enhancement in High-Rise Built-Up Areas Deployed by 3D-Orthorectification Correction from WorldView-3 and LiDAR Imageries,” Remote Sens., vol. 11, no. 6, p. 692, 2019.
53. P. S. Thenkabail, M. K. Gumma, P. Teluguntla, and A. Irshad, “Hyperspectral Hyperion images and spectral libraries of agricultural crops,” vol. 80, no. 8, 2014.
54. L. H. Filchev, “Satellite Hyperspectral Earth Observation Missions – A Review SATELLITE HYPERSPECTRAL EARTH OBSERVATION MISSIONS – A REVIEW Lachezar Filchev Space Research and Technology Institute – Bulgarian Academy of Sciences which were developed and operational since,” no. January 2014, 2016.
55. S. Veraverbeke et al., “Hyperspectral remote sensing of fire: State-of-the-art and future perspectives,” Remote Sens. Environ., vol. 216, no. June, pp. 105–121, 2018.
56. B. Bruning, B. Berger, M. Lewis, H. Liu, and T. Garnett, “Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat,” Plant Phenome J., vol. 3, no. 1, pp. 1–22, 2020.
57. G. Krishna et al., “Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring,” Geocarto Int., vol. 0, no. 0, pp. 1–18, 2019.
58. L. Liu, M. Ji, and M. Buchroithner, “Transfer learning for soil spectroscopy based on convolutional neural networks and its application in soil clay content mapping using hyperspectral imagery,” Sensors (Switzerland), vol. 18, no. 9, 2018.
59. S. Mishra, S. L. Chattoraj, A. Benny, R. U. Sharma, and P. K. C. Ray, “AVIRIS-NG Data for Geological Applications in Southeastern Parts of Aravalli Fold Belt, Rajasthan,” Proceedings, vol. 24, no. 1, p. 16, 2019.
60. J. C. Pyo et al., “An integrative remote sensing application of stacked autoencoder for atmospheric correction and cyanobacteria estimation using hyperspectral imagery,” Remote Sens., vol. 12, no. 7, 2020.
61. Ibrahim et al., “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ., vol. 204, pp. 60–75, 2018.
62. J. Transon, R. d’Andrimont, A. Maugnard, and P. Defourny, “Survey of hyperspectral Earth Observation applications from space in the Sentinel-2 context,” Remote Sens., vol. 10, no. 2, pp. 1–32, 2018.
63. S. Qian, Optical Payloads for Space Missions. 2016.
64. M. C. L. Patterson and A. Brescia, “Operation of small sensor payloads on tactical sized unmanned air vehicles,” Aeronaut. J., vol. 114, no. 1157, pp. 427–436, 2010.
65. Bhardwaj, L. Sam, Akanksha, F. J. Martín-Torres, and R. Kumar, “UAVs as remote sensing platform in glaciology: Present applications and future prospects,” Remote Sens. Environ., vol. 175, pp. 196–204, 2016.
66. S. Nebiker, N. Lack, M. Abächerli, and S. Läderach, “Light-weight multispectral uav sensors and their capabilities for predicting grain yield and detecting plant diseases,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 2016-Janua, no. June, pp. 963–970, 2016.
67. Malehmir et al., “The potential of rotary-wing UAV-based magnetic surveys for mineral exploration: A case study from central Sweden,” Lead. Edge, vol. 36, no. 7, pp. 552–557, 2017.
68. M. Erdelj and E. Natalizio, “UAV-assisted disaster management: Applications and open issues,” 2016 Int. Conf. Comput. Netw. Commun. ICNC 2016, 2016.
69. G. Yang et al., “Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives,” Front. Plant Sci., vol. 8, no. June, 2017.
70. Matese et al., “Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture,” Remote Sens., vol. 7, no. 3, pp. 2971–2990, 2015.
71. Li et al., “Lidar aboveground vegetation biomass estimates in shrublands: Prediction, uncertainties and application to coarser scales,” Remote Sens., vol. 9, no. 9, 2017.
72. M. El Hajj, N. Baghdadi, I. Fayad, G. Vieilledent, J. S. Bailly, and D. H. Tong Minh, “Interest of integrating spaceborne LiDAR data to improve the estimation of biomass in high biomass forested areas,” Remote Sens., vol. 9, no. 3, 2017.
73. T. Inomata et al., “Archaeological application of Airborne LiDAR with object-based vegetation classification and visualization techniques at the lowland Maya Site of Ceibal, Guatemala,” Remote Sens., vol. 9, no. 6, pp. 1–27, 2017.
74. M. D. Alexandrov and M. I. Mishchenko, “Information content of bistatic lidar observations of aerosols from space,” Opt. Express, vol. 25, no. 4, p. A134, 2017.
75. J. D. Loftis, H. V. Wang, R. J. DeYoung, and W. B. Ball, “Using Lidar Elevation Data to Develop a Topobathymetric Digital Elevation Model for Sub-Grid Inundation Modeling at Langley Research Center,” J. Coast. Res., vol. 76, pp. 134–148, 2016.
76. Guyot, L. Hubert-Moy, and T. Lorho, “Detecting Neolithic burial mounds from LiDAR-derived elevation data using a multi-scale approach and machine learning techniques,” Remote Sens., vol. 10, no. 2, 2018.
77. F. B. Sullivan, M. J. Ducey, D. A. Orwig, B. Cook, and M. W. Palace, “Comparison of lidar- and allometry-derived canopy height models in an eastern deciduous forest,” For. Ecol. Manage., vol. 406, no. October, pp. 83–94, 2017.
78. J. R. Roussel, J. Caspersen, M. Béland, S. Thomas, and A. Achim, “Removing bias from LiDAR-based estimates of canopy height: Accounting for the effects of pulse density and footprint size,” Remote Sens. Environ., vol. 198, pp. 1–16, 2017.
79. Q. Ma, Y. Su, and Q. Guo, “Comparison of Canopy Cover Estimations from Airborne LiDAR, Aerial Imagery, and Satellite Imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 10, no. 9, pp. 4225–4236, 2017.
80. Kumar, S., & Bhagat, V. S. (2018). Remote Sensing Satellites for Land Applications: A Review.