Author:
Jain Shagun,Sethia Divyashikha,Tiwari Kailash Chandra
Publisher
Springer Science and Business Media LLC
Reference200 articles.
1. Abdoli, P., et al. (2023). Use of remote sensing data to predict soil organic carbon in some agricultural soils of Iran. Remote Sensing Applications: Society and Environment, 30, 100969. https://doi.org/10.1016/j.rsase.2023.100969
2. Acharya et al. (2015). Exploring Landsat 8. International Journal of IT, Engineering and Applied Sciences Research (IJIEASR), 4(4), 4–10. Retrieved from https://www.researchgate.net/publication/311901147_Exploring_Landsat_8
3. Agilandeeswari, L., et al. (2022). Crop classification for agricultural applications in hyperspectral remote sensing images. Applied Sciences, 12(3), 1670. https://doi.org/10.3390/app12031670
4. Angelopoulou, T., et al. (2019). Remote sensing techniques for soil organic carbon estimation: A review. Remote Sensing, 11(6), 676. https://doi.org/10.3390/rs11060676
5. Angelopoulou, T., et al. (2023). Evaluation of airborne hyspex and spaceborne PRSIMA hyperspectral remote sensing data for soil organic matter and carbonates estimation. Remote Sensing, 15(4), 1106. https://doi.org/10.3390/rs15041106