Identification of salt-affected soils using remote sensing data through random forest technique: a case study from India

Rani, Alka; Kumar, Nirmal Tej; Sinha, Nishant K.; Kumar, Jitendra

doi:10.1007/s12517-022-09682-3

Cited by 6 publications

(5 citation statements)

References 59 publications

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“…Several reports have been published on salt-affected soils and their management from different regions such as Argentina (Imbellone et Hafez et al 2021), Pakistan (Hasan et al 2021;Sheikh et al 2022), Uzbekistan (Devkota et al 2022), the United States of America (Fiedler et al 2021), andLatin America (Taleisnik et al 2021), as well as on the global level (Sharma and Singh 2017;Chhabra 2021). Salt-affected soils can be identified using remote sensing data through the random forest technique (Rani et al 2022).…”

Section: Management Of Salt-affected Soilsmentioning

confidence: 99%

Management of Salt-Affected Soils: A Photographic Mini-Review

El-Ramady

Faizy²,

Amer

et al. 2022

EBSS

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oil is the main source of human food, feed for our animals, fiber, and a major source of fuel. The ability of soil to supply life essentials can be impacted by several obstacles that reduce or prevent plant production. Salt-affected soils are common under arid and semi-arid climates, which often produce soils that have salinity and/or alkalinity problems. Salinity and alkalinity are important stresses, mainly including oxidative and osmotic stress, which threaten crop productivity under such soil conditions and can cause significant yield reductions. Successful production in such soils needs a deep understanding of their formation and productivity challenges. Due to the increased areas of salt-affected soils in the world, the management of these soils is a crucial global issue. This paper is a mini-review on salt-affected soils, which include their characterization and suitable management approaches. Nano-management of salt-affected soils as a promising approach will also be discussed and new perspectives and challenges in reclamation of salt-affected soils will be highlighted.

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Section: Management Of Salt-affected Soilsmentioning

confidence: 99%

Management of Salt-Affected Soils: A Photographic Mini-Review

El-Ramady

Faizy²,

Amer

et al. 2022

EBSS

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“…Rani et al introduced an innovative technique for estimating salt-affected soils, employing the Random Forest (RF) algorithm with NDVI data from MODIS, reflectance data from LANDSAT-8, and elevation data from ALOS [101]. Similarly, Kalambakattu et al [102] and Vibhute et al [103] harnessed the potential of the Hyperion dataset and Support Vector Machine (SVM) algorithm to map soil salinity severity and soil types, respectively. The digital soil maps can help farmers and decision makers in utilizing precision information for farm-scale decision making leading to increased pro-ductivity through optimal nutrient use and ecosystem sustainability, ultimately securing food supplies.…”

Section: Soil Managementmentioning

confidence: 99%

Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

Pokhariyal,

Patel,

Govind

2023

Agronomy

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In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management.

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“…the total number of votes [23]. Random forest algorithms have been shown to perform exceptionally well in classification [24,25]. As a common type of nonparametric machine learning algorithm, they have been successfully applied to a variety of remote sensing data for feature information recognition [26,27], agricultural land use classification [28,29], and land use change monitoring [30,31].…”

Section: Study Areamentioning

confidence: 99%

“…Once the training is complete, each tree assigns class labels to the test data, and, eventually, the results of all the decision trees are combined, and the classification of each land cover is ascertained by the total number of votes [23]. Random forest algorithms have been shown to perform exceptionally well in classification [24,25]. As a common type of nonparametric machine learning algorithm, they have been successfully applied to a variety of remote sensing data for feature information recognition [26,27], agricultural land use classification [28,29], and land use change monitoring [30,31].…”

Section: Introductionmentioning

confidence: 99%

Optimal Time Phase Identification for Apple Orchard Land Recognition and Spatial Analysis Using Multitemporal Sentinel-2 Images and Random Forest Classification

et al. 2023

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The significance of identifying apple orchard land and monitoring its spatial distribution patterns is increasing for precise yield prediction and agricultural sustainable development. This study strived to identify the optimal time phase to efficiently extract apple orchard land and monitor its spatial characteristics based on the random forest (RF) classification method and multitemporal Sentinel-2 images. Firstly, the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI), and Difference Vegetation Index (DVI) between apple orchard land and other green vegetation (other orchards, forest and grassland) during the growing stage were calculated and compared to identify the optimal time phase for apple orchard land extraction; the RF classifier was then constructed using multifeature variables on Google Earth Engine to efficiently identify apple orchard land, and the support vector machine (SVM) classification results were used as a comparison; GIS spatial analysis, a slope calculation model, and Moran’s I and Getis-Ord GI* analysis were employed to further analyze the spatial patterns of the apple orchard land. The results found the following: (1) April, May, and October were the optimal time phases for apple orchard identification. (2) The RF-based method combining coefficients of indexes, the grayscale co-occurrence matrix, and 70% of the ground reference data can precisely classify apple orchards with an overall accuracy of 90% and a Kappa coefficient of 0.88, increasing by 9.2% and 11.4% compared to those using the SVM. (3) The total area of apple orchard land in the study area was 485.8 km2, which is 0.6% less than the government’s statistical results. More than half (55.7%) of the apple orchard land was distributed on the gentle slope (Grade II, 6–15°) and the flat slope (Grade I, 0–5°); SiKou, Songshan, and Shewopo contained more than 50% of the total orchard land area. (4) The distribution of apple orchard land has a positive spatial autocorrelation (0.309, p = 0.000). High–High cluster types occurred mainly in Sikou (60%), High–Low clusters in Songshan (40%), Low–High clusters in Sikou (47.5%), and Low–Low clusters in Taocun and Tingkou (37.4%). The distribution patterns of cold and hot spots converged with those of the Local Moran Index computation results. The findings of this study can provide theoretical and methodological references for orchard land identification and spatial analysis.

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Identification of salt-affected soils using remote sensing data through random forest technique: a case study from India

Cited by 6 publications

References 59 publications

Management of Salt-Affected Soils: A Photographic Mini-Review

Management of Salt-Affected Soils: A Photographic Mini-Review

Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

Optimal Time Phase Identification for Apple Orchard Land Recognition and Spatial Analysis Using Multitemporal Sentinel-2 Images and Random Forest Classification

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