Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

By Iftikhar Ali, F. Greifeneder, J. Stamenkovic, M. Neumann, C. Notarnicola. Remote Sensing, 2015

441 citations recorded by the source corpus.

The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/a0309aebc917601f10a29b91811a70819a68e6a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Environmental Engineering

Citations in the corpus

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images (2017, methodology): “…applied to estimation of bio-physical forest parameters for multispectral data (Linderman et al., 2004), biomass and soil moisture retrieval (Ali, Greifeneder, Stamenkovic, Neumann, & Notarnicola, 2015), crop monitoring (Gupta et al., 2015) or land cover classification (Petropoulos et al.,…”
Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data (2019, background): “To estimate the biomass of a region, such approaches usually classify the vegetation first and then calculate the number of pixels of each class [30].”
A review of machine learning in processing remote sensing data for mineral exploration (2021, methodology): “ments, has enabled scientists to provide innovative and effective methodologies for data processing (Ali et al., 2015).”
Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy (2020, background): “For these reasons, independent validation sets are indispensable; once kernel models are sensitive to over-fitting (Ali et al., 2015) and potentially do not translate predictive prediction accuracy from the calibration sets to independent test sets or new samples.”