ArticleRemote sensing2021
Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine.
Article in Remote sensing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Fine-scale mapping of irrigation suitability in South Africa using ensemble modelling.Scientific reports · 2025Article
- From tradition to smart: A comprehensive review of the evolution and prospects of land use planning tools.Heliyon · 2024Review
- Article
- Why Do Farmers Not Irrigate All the Areas Equipped for Irrigation? Lessons from Southern Africa.Agriculture (Basel, Switzerland) · 2024Article
- Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models.Scientific reports · 2024Article
- Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Article
- Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction.International journal of environmental research and public health · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Improvements in irrigated areas' classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.