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Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition)

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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: 30 December 2025 | Viewed by 11354

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Special Issue Editors

Dr. Quanjun Jiao

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Guest Editor

Chinese Academy of Sciences, Beijing, China
Interests: egetation; chlorophyll content; remote sensing; forest health
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Prof. Dr. Wei Su

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Guest Editor

College of Land Science and Technology, China Agricultural University, Beijing 100081, China
Interests: LiDAR application in vegetation; vegetation parameter retrieval; vegetation monitoring; hyperspectral remote sensing
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Dr. Qiaoyun Xie

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Guest Editor

School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
Interests: ecosystem dynamics monitoring; remote sensing of the environment; vegetation phenology; vegetation response to climate change
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Dr. Xing Li

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Guest Editor

School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, China
Interests: solar‐induced chlorophyll fluorescence; terrestrial carbon cycle; remote sensing of vegetation
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Dr. Bo Liu

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Guest Editor

Department of Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: vegetation parameters estimation; hyperspectral remote sensing; agricultral & ecological remote sensing
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Dear Colleagues,

Vegetation biophysical parameters are important indicators when characterizing the canopy density and structure of vegetation, such as the leaf area index (LAI), fractional vegetation coverage (FVC), biomass, leaf angle distribution (LAD), clumping index (CI), canopy height, etc. The vegetation biophysical parameters estimated through remote-sensing approaches support numerous applications in agriculture, forestry and other vegetation ecosystems.

The current algorithms employed to estimate biophysical parameters of vegetation include statistical regression, radiative transfer models, machine learning and deep learning techniques. Regression models based on a single vegetation spectral index or another spectral feature are simple and easy to use, with relatively stable accuracy; they are therefore the most widely utilized models. In contrast, machine learning has the ability to nonlinearly model the relationships between the biophysical parameters of vegetation and a satellite-derived spectrum. Deep learning, a new machine learning method, was initially used for target recognition and classification, and has gradually become a popular approach for the estimation of the biophysical parameters of vegetation.

Various types of remote sensing data (optical, LiDAR, SAR, etc.) have different advantages for extracting different vegetation biophysical parameters. The synthesis of different remote-sensing data is also important for estimation of certain vegetation parameters. Additionally, the estimation algorithm is closely related to the spatial and temporal resolution of those remote-sensing data.

Due to the complexity of land-surface vegetation, challenges regarding the estimation of vegetation parameters remain in terms of algorithm performance and applications. Advanced remote-sensing techniques and machine learning provide unprecedented opportunities to tackle these challenges. For example, the spectral responses region of different vegetation physical parameters (including biochemical parameters) may overlap, whereby the integration of multi-source remote sensing data offers significant potential in this regard.

This Special Issue aims to address advances and challenges in the remote-sensing estimation of vegetation biophysical variables using various algorithms and satellite data, and to enhance and promote the application of the estimated parameters. Original research articles and reviews are welcome. Research topics may include (but are not limited to) the following:

(1) The applicability of vegetation parameter estimation algorithms (regression, machine learning, deep learning) in various scenarios (regions, vegetation types, etc.);

(2) New methods such as a vegetation spectral index for vegetation biophysical parameter estimation;

(3) The integration and assimilation of multi-source remote sensing data and other data for vegetation biophysical parameter estimation;

(4) A case study of the applications of estimated vegetation parameters in ecosystems such as agriculture, forest and grassland areas (remote sensing of crop growth, vegetation phenology, vegetation degradation and recovery, etc.).

Dr. Quanjun Jiao
Prof. Dr. Wei Su
Dr. Qiaoyun Xie
Dr. Xing Li
Dr. Bo Liu
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

vegetation biophysical parameter estimation
vegetation spectral indices
multispectral/hyperspectral/LiDAR/SAR
satellite/airborne/UAV/tower-based/ground observation
regression/RTM/machine learning/deep learning
agriculture
forest
grassland
vegetation dynamics monitoring

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Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation in Remote Sensing (12 articles)

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Research

24 pages, 12091 KiB

Open AccessArticle

A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat

by Runzhi Yang, Shanshan Li, Bing Zhang, Quanjun Jiao, Dailiang Peng, Songlin Yang and Ruyi Yu

Remote Sens. 2024, 16(23), 4441; https://doi.org/10.3390/rs16234441 - 27 Nov 2024

Viewed by 841

Abstract

Spectral information plays a crucial role in fractional vegetation cover (FVC) estimation, and selecting the appropriate spectral information is essential for improving the accuracy of FVC estimation. Traditionally, spectral feature selection is primarily guided by physical mechanisms or empirical statistical models. This has led to the use of multispectral and hyperspectral images, which often result in missing or redundant information, thereby decreasing the efficiency and accuracy of FVC estimation. This study proposes a novel dual-attention network to select the feature bands of Sentinel-2 multispectral images for the accurate FVC estimation of winter wheat. In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. Finally, the selected features were assessed based on their performance in FVC estimation using a CNN model with real data. The experimental results indicate that during the key growth period of winter wheat, the combination of red, green, and red-edge bands significantly influences the FVC estimation accuracy. Band 3 (Green), band 4 (Red), band 5 (Red-edge 1), and band 6 (Red-edge 2) of Sentinel-2 satellite images contribute most significantly to winter wheat FVC estimation, achieving an accuracy comparable to that obtained using all bands, while reducing the training time by 19.1%, as confirmed by field survey data. Full article

(This article belongs to the Special Issue Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition))

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27 pages, 7620 KiB

Open AccessArticle

Maturity Prediction in Soybean Breeding Using Aerial Images and the Random Forest Machine Learning Algorithm

by Osvaldo Pérez, Brian Diers and Nicolas Martin

Remote Sens. 2024, 16(23), 4343; https://doi.org/10.3390/rs16234343 - 21 Nov 2024

Viewed by 1200

Abstract

Several studies have used aerial images to predict physiological maturity (R8 stage) in soybeans (Glycine max (L.) Merr.). However, information for making predictions in the current growing season using models fitted in previous years is still necessary. Using the Random Forest machine learning algorithm and time series of RGB (red, green, blue) and multispectral images taken from a drone, this work aimed to study, in three breeding experiments of plant rows, how maturity predictions are impacted by a number of factors. These include the type of camera used, the number and time between flights, and whether models fitted with data obtained in one or more environments can be used to make accurate predictions in an independent environment. Applying principal component analysis (PCA), it was found that compared to the full set of 8–10 flights (R² = 0.91–0.94; RMSE = 1.8–1.3 days), using data from three to five fights before harvest had almost no effect on the prediction error (RMSE increase ~0.1 days). Similar prediction accuracy was achieved using either a multispectral or an affordable RGB camera, and the excess green index (ExG) was found to be the important feature in making predictions. Using a model trained with data from two previous years and using fielding notes from check cultivars planted in the test season, the R8 stage was predicted, in 2020, with an error of 2.1 days. Periodically adjusted models could help soybean breeding programs save time when characterizing the cycle length of thousands of plant rows each season. Full article

(This article belongs to the Special Issue Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition))

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19 pages, 5207 KiB

Open AccessArticle

Enhancing the Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data

by Temitope Olaoluwa Omoniyi and Allan Sims

Remote Sens. 2024, 16(20), 3794; https://doi.org/10.3390/rs16203794 - 12 Oct 2024

Cited by 1 | Viewed by 1348

Abstract

Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, random forest (RF), support vector regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia’s geographical regions, RF consistently delivered the best performance. In the northwest (NW), the RF model achieved the best performance with the CO3 combination, having an R² of 0.63 and an RMSE of 125.39 m³/plot. In the southwest (SW), the RF model also performed exceptionally well, achieving an R² of 0.73 and an RMSE of 128.86 m³/plot with the CO4 variable combination. In the northeast (NE), the RF model outperformed other ML models, achieving an R² of 0.64 and an RMSE of 133.77 m³/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R² of 0.70 and an RMSE of 21,120.72 m³/plot. These results underscore RF’s precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy. Full article

(This article belongs to the Special Issue Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition))

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24 pages, 11990 KiB

Open AccessArticle

Plant Species Classification and Biodiversity Estimation from UAV Images with Deep Learning

by Marco Conciatori, Nhung Thi Cam Tran, Yago Diez, Alessandro Valletta, Andrea Segalini and Maximo Larry Lopez Caceres

Remote Sens. 2024, 16(19), 3654; https://doi.org/10.3390/rs16193654 - 30 Sep 2024

Viewed by 2415

Abstract

Biodiversity is a characteristic of ecosystems that plays a crucial role in the study of their evolution, and to estimate it, the species of all plants need to be determined. In this study, we used Unmanned Aerial Vehicles to gather RGB images of mid-to-high-altitude ecosystems in the Zao mountains (Japan). All the data-collection missions took place in autumn so the plants present distinctive seasonal coloration. Patches from single trees and bushes were manually extracted from the collected orthomosaics. Subsequently, Deep Learning image-classification networks were used to automatically determine the species of each tree or bush and estimate biodiversity. Both Convolutional Neural Networks (CNNs) and Transformer-based models were considered (ResNet, RegNet, ConvNeXt, and SwinTransformer). To measure and estimate biodiversity, we relied on the Gini–Simpson Index, the Shannon–Wiener Index, and Species Richness. We present two separate scenarios for evaluating the readiness of the technology for practical use: the first scenario uses a subset of the data with five species and a testing set that has a very similar percentage of each species to those present in the training set. The models studied reach very high performances with over 99 Accuracy and 98 F₁ Score (the harmonic mean of Precision and Recall) for image classification and biodiversity estimates under 1% error. The second scenario uses the full dataset with nine species and large variations in class balance between the training and testing datasets, which is often the case in practical use situations. The results in this case remained fairly high for Accuracy at 90.64% but dropped to 51.77% for F₁ Score. The relatively low F₁ Score value is partly due to a small number of misclassifications having a disproportionate impact in the final measure, but still, the large difference between the Accuracy and F₁ Score highlights the complexity of finely evaluating the classification results of Deep Learning Networks. Even in this very challenging scenario, the biodiversity estimation remained with relatively small (6–14%) errors for the most detailed indices, showcasing the readiness of the technology for practical use. Full article

(This article belongs to the Special Issue Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition))

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25 pages, 3397 KiB

Open AccessArticle

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

by Frank Gyan Okyere, Daniel Kingsley Cudjoe, Nicolas Virlet, March Castle, Andrew Bernard Riche, Latifa Greche, Fady Mohareb, Daniel Simms, Manal Mhada and Malcolm John Hawkesford

Remote Sens. 2024, 16(18), 3446; https://doi.org/10.3390/rs16183446 - 17 Sep 2024

Cited by 6 | Viewed by 3583

Abstract

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (P_n) and stomatal conductance (g_s) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast g_s and P_n and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions. Full article

(This article belongs to the Special Issue Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition))

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17 pages, 7349 KiB

Open AccessArticle

Temporal Patterns of Vegetation Greenness for the Main Forest-Forming Tree Species in the European Temperate Zone

by Kinga Kulesza and Agata Hościło

Remote Sens. 2024, 16(15), 2844; https://doi.org/10.3390/rs16152844 - 2 Aug 2024

Cited by 2 | Viewed by 1092

Abstract

In light of recently accelerating global warming, the changes in vegetation trends are vital for the monitoring of the dynamics of both whole ecosystems and individual species. Detecting changes within the time series of specific forest ecosystems or species is very important in the context of assessing their vulnerability to climate change and other negative phenomena. Hence, the aim of this paper was to identify the trend change points and periods of greening and browning in multi-annual time series of the normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) of four main forest-forming tree species in the temperate zone: pine, spruce, oak and beech. The research was conducted over the last two decades (2002–2022), and was based on vegetation indices data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). To this end, several research approaches, including calculating the linear trends in the moving periods and BEAST algorithm, were adapted. A pattern of browning then greening then constant was detected for coniferous species, mostly pine. In turn, for broadleaved species, namely oak and beech, a pattern of greening then constant was identified, without the initial phase of browning. The main trend change points seem to be ca. 2006 and ca. 2015 for coniferous species and solely around 2015 for deciduous ones. Full article

(This article belongs to the Special Issue Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition))

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