ArticlePeerJ2018
Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Prediction of Soil Properties Using Vis-NIR Spectroscopy Combined with Machine Learning: A Review.Sensors (Basel, Switzerland) · 2025Review
- Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Prediction of soil organic carbon and total nitrogen affected by mine using Vis-NIR spectroscopy coupled with machine learning algorithms in calcareous soils.Scientific reports · 2024Article
- Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices.Plant methods · 2024Article
- Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments.Scientific reports · 2021Article
- An empirical investigation of deviations from the Beer-Lambert law in optical estimation of lactate.Scientific reports · 2021Article
- Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method.Sensors (Basel, Switzerland) · 2021Article
- Article
- Article
- Article
- Article
- 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350-2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745-910 nm and 1,911-2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of
Indexed as
Identifiers
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.