Remote Sensing and Machine Learning for Identification of Salt-affected Soils-Reference-Cited by-同舟云学术

Remote Sensing and Machine Learning for Identification of Salt-affected Soils

Published:2021-10-12 Issue: Volume: Page:267-287
ISSN:2197-6503
Container-title:Studies in Big Data
language:
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Author:

Kumar Nirmal,Reddy G. P. Obi,Nagaraju M. S. S.,Naitam R. K.

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-5847-1_13

Reference69 articles.

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3. Allbed A, Kumar L (2013) Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Adv Remote Sens 2:373–385

4. Asfaw E, Suryabhagavan KV, Argaw M (2018) Soil salinity modeling and mapping using remote sensing and GIS: the case of Wonji sugar cane irrigation farm. Ethiopia J Saudi Soc Agric Sci 17:250–258

5. Bivand R, Keitt T, Rowlingson B (2019) rgdal: Bindings for the geospatial data abstraction library, R Package Version 1.1–10. R Foundation for Statistical Computing, Austria

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1. IDENTIFICATION OF SALT-AFFECTED SOILS IN THE COASTAL AREA OF KRISHNA DISTRICT, ANDHRA PRADESH, USING REMOTE SENSING DATA AND MACHINE LEARNING TECHNIQUES;Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska;2024-06-30

2. Temporal remote sensing based soil salinity mapping in Indo-Gangetic plain employing machine-learning techniques;Proceedings of the Indian National Science Academy;2023-03-20

3. Geological Remote Sensing Interpretation Using Deep Learning Feature and an Adaptive Multisource Data Fusion Network;IEEE Transactions on Geoscience and Remote Sensing;2022