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Novel Approaches for Mapping and Monitoring of Vegetation Properties from Earth Observation (EO) Data in an Agricultural Context

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 8533

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Dr. Katja Berger

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1. Laboratory for Earth Observation, Image Processing Laboratory - Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
2. Mantle Labs GmbH, Vienna, Austria
Interests: agriculture; hybrid retrieval; hyperspectral remote sensing; machine learning methods; active learning
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Dr. Stefan Livens

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Flemish Institute for Technological Research, Center for Remote Sensing and Earth Observation Processes (VITO-TAP), 2400 Mol, Belgium
Interests: pattern recognition; image processing; computer vision; image analysis; feature selection; wavelet; calibration; classification; hyperspectral image analysis; hyperspectral remote sensing
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Prof. Dr. Clement Atzberger

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Institute of Geomatics, University of Natural Resources and Life Sciences, 1090 Vienna, Austria
Interests: remote sensing of vegetation with focus on time series analysis and use of physically based radiative transfer models for mapping biochemical and biophysical traits
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Dr. Uta Heiden

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German Aerospace Center (DLR), Remote Sensing Technology Institute, Photogrammetry and Image Analysis, Oberpfaffenhofen, 82234 Weßling, Germany
Interests: imaging spectroscopy with a focus on urban surface materials; spaceborne imaging spectroscopy missions; EnMAP; DESIS; earth observation for soil information; applied spectroscopy
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Dr. Stephanie Delalieux

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Remote Sensing Department, Flemish Institute for Technological Research (VITO-TAP), 2400 Mol, Belgium
Interests: remote sensing; spectral imaging; image processing; precision agriculture; horticulture; disease detection
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Special Issue Information

Dear Colleagues,

The use of digital information for more efficient cultivation of large areas has become a part of agricultural practices worldwide. In this context, the use of satellite data allows for the regular and cost-effective quantification of biophysical and biochemical variables with increasingly fine spatial and spectral resolutions. Such monitoring is essential for understanding the state and dynamics of the cropped surface which, in turn, facilitates consideration of the environmental impacts of agricultural practices. Classical canopy traits involve leaf area index (LAI), chlorophyll content (Cab), fractional vegetation cover (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), aboveground biomass (AGB), as well as leaf/canopy water content (EWT/CWC). However, the estimation of more subtle or non-state variables, such as the contents of protein, nitrogen, leaf carotenoid, and anthocyanin, or non-photosynthetically active vegetation (lignin + cellulose), is also of great interest to agricultural service providers. Given the vast data streams of upcoming spaceborne imaging spectroscopy missions, fast and efficient retrieval techniques—exploiting the full spectral range—should be developed. Techniques which can be implemented onboard are particularly valuable as they reduce the data downlink bottleneck faced in hyperspectral satellite imaging. This can be realized, for instance, by machine learning regression algorithms (MLRAs) able to handle the strong nonlinearity between observed reflectance signals and biophysical/biochemical traits. Those MLRAs also offer an interesting linkage to radiative transfer models (RTMs), where the latter provide the necessary training databases without the requirement for in situ data. These so-called “hybrid approaches” combine the generic properties of RTMs with the flexibility and efficiency of nonparametric methods and, therefore, can be used to exploit the vast superspectral and imaging spectrometer data streams for application in agricultural areas.

This Special Issue strongly encourages contributions aimed at estimating the biochemical and biophysical quantities in an agricultural context using the full range of available optical remote sensing data, and that capitalize upon statistical nonparametric, physically based, or innovative hybrid methods.

This Special Issue was also established to collect contributions for the related Special Session “Novel Approaches for Agricultural Monitoring” of the Whispers conference, taking place in Amsterdam, the Netherlands, on 24–26 September 2019.

Dr. Katja Berger
Prof. Dr. Clement Atzberger
Dr. Uta Heiden
Dr. Stephanie Delalieux
Dr. Stefan Livens
Guest Editors

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Keywords

Cropland status and dynamics
Crop biophysical and biochemical variables
Precision agriculture
Imaging spectrometer missions
Superspectral missions
Machine learning regression algorithms
Radiative transfer modeling
Onboard techniques
Hybrid inversion techniques

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Research

20 pages, 3934 KiB

Open AccessArticle

A hybrid OSVM-OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery

by Huapeng Li, Ce Zhang, Shuqing Zhang and Peter M. Atkinson

Remote Sens. 2019, 11(20), 2370; https://doi.org/10.3390/rs11202370 - 12 Oct 2019

Cited by 20 | Viewed by 3716

Abstract

Accurate information on crop distribution is of great importance for a range of applications including crop yield estimation, greenhouse gas emission measurement and management policy formulation. Fine spatial resolution (FSR) remotely sensed imagery provides new opportunities for crop mapping at a detailed level. However, crop classification from FSR imagery is known to be challenging due to the great intra-class variability and low inter-class disparity in the data. In this research, a novel hybrid method (OSVM-OCNN) was proposed for crop classification from FSR imagery, which combines a shallow-structured object-based support vector machine (OSVM) with a deep-structured object-based convolutional neural network (OCNN). Unlike pixel-wise classification methods, the OSVM-OCNN method operates on objects as the basic units of analysis and, thus, classifies remotely sensed images at the object level. The proposed OSVM-OCNN harvests the complementary characteristics of the two sub-models, the OSVM with effective extraction of low-level within-object features and the OCNN with capture and utilization of high-level between-object information. By using a rule-based fusion strategy based primarily on the OCNN’s prediction probability, the two sub-models were fused in a concise and effective manner. We investigated the effectiveness of the proposed method over two test sites (i.e., S1 and S2) that have distinctive and heterogeneous patterns of different crops in the Sacramento Valley, California, using FSR Synthetic Aperture Radar (SAR) and FSR multispectral data, respectively. Experimental results illustrated that the new proposed OSVM-OCNN approach increased markedly the classification accuracy for most of crop types in S1 and all crop types in S2, and it consistently achieved the most accurate accuracy in comparison with its two object-based sub-models (OSVM and OCNN) as well as the pixel-wise SVM (PSVM) and CNN (PCNN) methods. Our findings, thus, suggest that the proposed method is as an effective and efficient approach to solve the challenging problem of crop classification using FSR imagery (including from different remotely sensed platforms). More importantly, the OSVM-OCNN method is readily generalisable to other landscape classes and, thus, should provide a general solution to solve the complex FSR image classification problem. Full article

(This article belongs to the Special Issue Novel Approaches for Mapping and Monitoring of Vegetation Properties from Earth Observation (EO) Data in an Agricultural Context)

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26 pages, 4021 KiB

Open AccessArticle

Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data

by Jochem Verrelst, Jorge Vicent, Juan Pablo Rivera-Caicedo, Maria Lumbierres, Pablo Morcillo-Pallarés and José Moreno

Remote Sens. 2019, 11(16), 1923; https://doi.org/10.3390/rs11161923 - 17 Aug 2019

Cited by 41 | Viewed by 4025

Abstract

Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN’s computational burden and GSA’s demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400–2500 nm spectral range at 15 cm

^{- 1}

(between 0.3–9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction. Full article

(This article belongs to the Special Issue Novel Approaches for Mapping and Monitoring of Vegetation Properties from Earth Observation (EO) Data in an Agricultural Context)

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Novel Approaches for Mapping and Monitoring of Vegetation Properties from Earth Observation (EO) Data in an Agricultural Context

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