ArticleRemote sensing2021
A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data.
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- DFS: A Feature-Sample Collaborative Optimization Framework for Machine Learning-Based Forest Aboveground Biomass Estimation Using Multi-Source Remote Sensing.Plants (Basel, Switzerland) · 2026Article
- Sensitive Spectral and Temporal-Spatial Characteristic Analysis of Leaf SPAD in Maize Under Variety and Nitrogen Coupling Effects.Food science & nutrition · 2026Article
- PyEOGPR: A Python package for vegetation trait mapping with Gaussian Process Regression on Earth observation cloud platforms.Ecological informatics · 2025Article
- Enhanced Whale Optimization Algorithm with Novel Strategies for 3D TSP Problem.Biomimetics (Basel, Switzerland) · 2025Article
- Tower-to-global upscaling of terrestrial carbon fluxes driven by MODIS-LAI, Sentinel-3-LAI and ERA5-Land data.Ecological indicators · 2025Article
- Retrieval of carbon content and biomass from hyperspectral imagery over cultivated areas.ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) · 2022Article
- Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review.Remote sensing of environment · 2022Article
- Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery.Remote sensing · 2022Article
- Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning.International journal of remote sensing · 2022Article
- Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data.Remote sensing · 2022Article
- Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.Remote sensing of environment · 2022Article
- Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery.ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) · 2022Article
- Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission.Remote sensing · 2022Article
- Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine.Remote sensing · 2022Article
- Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression.Remote sensing · 2021Article
- Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery.Remote sensing · 2021Article
- Mapping landscape canopy nitrogen content from space using PRISMA data.ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) · 2021Article
- Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow.Remote sensing · 2021Article
- Bridging the Gap Between Remote Sensing and Plant Phenotyping-Challenges and Opportunities for the Next Generation of Sustainable Agriculture.Frontiers in plant science · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
The current exponential increase of spatiotemporally explicit data streams from satellitebased Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (
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