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Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation

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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 (30 June 2023) | Viewed by 23714

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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
Special Issues, Collections and Topics in MDPI journals

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
Special Issues, Collections and Topics in MDPI journals

Dr. Qiaoyun Xie

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Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: vegetation monitoring; ecological forecasting; vegetation parameter retrieval; vegetation phenology; climate variability
Special Issues, Collections and Topics in MDPI journals

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
Special Issues, Collections and Topics in MDPI journals

Dr. Xing Li

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Guest Editor

Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Korea
Interests: solar‐induced chlorophyll fluorescence; terrestrial carbon cycle; remote sensing of vegetation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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

The current algorithms used to estimate vegetation biophysical parameters 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, and therefore are the most widely used. In contrast, machine learning has the advantages of nonlinearity modeling of the relationships between vegetation biophysical parameters and a satellite-derived spectrum. One of the new machine-learning methods, deep learning, was initially used for target recognition and classification, and has gradually become a popular approach for vegetation biophysical parameter estimation.

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, vegetation parameter estimation challenges 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 integration of multi-source remote sensing data has a great potential in this regard.

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

The applicability of vegetation parameter estimation algorithms (regression, machine learning, deep learning) in various scenarios (regions, vegetation types, etc.);
New methods such as a vegetation spectral index for vegetation biophysical parameter estimation;
Integration and assimilation of multi-source remote sensing data and other data for vegetation biophysical parameter estimation;
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.).

We look forward to receiving your contributions.

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

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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

Related Special Issue

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

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17 pages, 6554 KiB

Open AccessArticle

Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms

by Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti, Najmeh Neysani Samany, Saeid Soufizadeh and Saeid Hamzeh

Remote Sens. 2023, 15(14), 3690; https://doi.org/10.3390/rs15143690 - 24 Jul 2023

Viewed by 1038

Abstract

Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)–particle swarm optimization (PSO), GPR–genetic algorithm (GA), GPR–tabu search (TS), and GPR–simulated annealing (SA) hyperparameter-optimized algorithms were developed and compared against kernel-based machine learning regression algorithms and artificial neural network (ANN) and random forest (RF) algorithms. The accuracy of the proposed algorithms was assessed using digital hemispherical photography (DHP) data and destructive measurements performed during the growing season of silage maize in agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019. The results on biophysical variables against validation data showed that the developed GPR-PSO algorithm outperformed other algorithms under study in terms of robustness and accuracy (0.917, 0.931, 0.882 using R² and 0.627, 0.078, and 1.99 using RMSE in LAI, fCover, and biomass of Sentinel-2 20 m, respectively). GPR-PSO also possesses the unique ability to generate pixel-based uncertainty maps (confidence level) for prediction purposes (i.e., estimated uncertainty level <0.7 in LAI, fCover, and biomass, for 96%, 98%, and 71% of the total study area, respectively). Altogether, GPR-PSO appears to be the most suitable option for mapping biophysical variables at the local scale using Sentinel-2 images. Full article

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

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22 pages, 3236 KiB

Open AccessArticle

Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data

by Yanxiang Wang, Minfeng Xing, Hongguo Zhang, Binbin He and Yi Zhang

Remote Sens. 2023, 15(12), 2961; https://doi.org/10.3390/rs15122961 - 06 Jun 2023

Cited by 2 | Viewed by 1201

Abstract

Rice false smut (RFS) is a late-onset fungal disease that primarily affects rice panicle in recent years. Severe RFS can decrease the yield by 20–30% and severely affect rice quality. This research used hyperspectral remote sensing data from unmanned aerial vehicles (UAV). On the basis of genetic algorithm combined with partial least squares to select the feature bands, this paper creates a new method to use the Pearson correlation coefficient method and Instability Index between Classes (ISIC) method to further select characteristic bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of the Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10%, respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the combination of characteristic bands selected by the three band optimization methods, the sensitive band ranges of rice false smut monitoring were determined, which were 698–800 nm and 974–997 nm. This paper provides an effective method of selecting characteristic bands of hyperspectral data and a method of monitoring crop diseases’ using unmanned aerial vehicles. Full article

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

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18 pages, 24549 KiB

Open AccessArticle

Improved Object-Based Mapping of Aboveground Biomass Using Geographic Stratification with GEDI Data and Multi-Sensor Imagery

by Lin Chen, Chunying Ren, Bai Zhang, Zongming Wang, Weidong Man and Mingyue Liu

Remote Sens. 2023, 15(10), 2625; https://doi.org/10.3390/rs15102625 - 18 May 2023

Viewed by 1373

Abstract

Aboveground biomass (AGB) mapping using spaceborne LiDAR data and multi-sensor images is essential for efficient carbon monitoring and climate change mitigation actions in heterogeneous forests. The optimal predictors of remote sensing-based AGB vary greatly with geographic stratification, such as topography and forest type, while the way in which geographic stratification influences the contributions of predictor variables in object-based AGB mapping is insufficiently studied. To address the improvement of mapping forest AGB by geographic stratification in heterogeneous forests, satellite multisensory data from global ecosystem dynamics investigation (GEDI) and series of advanced land observing satellite (ALOS) and Sentinel were integrated. Multi-sensor predictors for the AGB modeling of different types of forests were selected using a correlation analysis of variables calculated from topographically stratified objects. Random forests models were built with GEDI-based AGB and geographically stratified predictors to acquire wall-to-wall biomass values. It was illustrated that the mapped biomass had a similar distribution and was approximate to the sampled forest AGB. Through an accuracy comparison using independent validation samples, it was determined that the geographic stratification approach improved the accuracy by 34.79% compared to the unstratified process. Stratification of forest type further increased the mapped AGB accuracy compared to that of topography. Topographical stratification greatly influenced the predictors’ contributions to AGB mapping in mixed broadleaf–conifer and broad-leaved forests, but only slightly impacted coniferous forests. Optical variables were predominant for deciduous forests, while for evergreen forests, SAR indices outweighed the other predictors. As a pioneering estimation of forest AGB with geographic stratification using satellite multisensory data, this study offers optimal predictors and an advanced method for obtaining carbon maps in heterogeneous regional landscapes. Full article

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

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20 pages, 4790 KiB

Open AccessArticle

Machine Learning Algorithms for the Retrieval of Canopy Chlorophyll Content and Leaf Area Index of Crops Using the PROSAIL-D Model with the Adjusted Average Leaf Angle

by Qi Sun, Quanjun Jiao, Xidong Chen, Huimin Xing, Wenjiang Huang and Bing Zhang

Remote Sens. 2023, 15(9), 2264; https://doi.org/10.3390/rs15092264 - 25 Apr 2023

Cited by 6 | Viewed by 1797

Abstract

The canopy chlorophyll content (CCC) and leaf area index (LAI) are both essential indicators for crop growth monitoring and yield estimation. The PROSAIL model, which couples the properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAIL) radiative transfer models, is commonly used for the quantitative retrieval of crop parameters; however, its homogeneous canopy assumption limits its accuracy, especially in the case of multiple crop categories. The adjusted average leaf angle (ALA_adj), which can be parameterized for a specific crop type, increases the applicability of the PROSAIL model for specific crop types with a non-uniform canopy and has the potential to enhance the performance of PROSAIL-coupled hybrid methods. In this study, the PROSAIL-D model was used to generate the ALA_adj values of wheat, soybean, and maize crops based on ground-measured spectra, the LAI, and the leaf chlorophyll content (LCC). The results revealed ALA_adj values of 62 degrees for wheat, 45 degrees for soybean, and 60 degrees for maize. Support vector regression (SVR), random forest regression (RFR), extremely randomized trees regression (ETR), the gradient boosting regression tree (GBRT), and stacking learning (STL) were applied to simulated data of the ALA_adj in 50-band data to retrieve the CCC and LAI of the crops. The results demonstrated that the estimation accuracy of singular crop parameters, particularly the crop LAI, was greatly enhanced by the five machine learning methods on the basis of data simulated with the ALA_adj. Regarding the estimation results of mixed crops, the machine learning algorithms using ALA_adj datasets resulted in estimations of CCC (RMSE: RFR = 51.1 μg cm⁻², ETR = 54.7 μg cm⁻², GBRT = 54.9 μg cm⁻², STL = 48.3 μg cm⁻²) and LAI (RMSE: SVR = 0.91, RFR = 1.03, ETR = 1.05, GBRT = 1.05, STL = 0.97), that outperformed the estimations without using the ALA_adj (namely CCC RMSE: RFR = 93.0 μg cm⁻², ETR = 60.1 μg cm⁻², GBRT = 60.0 μg cm⁻², STL = 68.5 μg cm⁻² and LAI RMSE: SVR = 2.10, RFR = 2.28, ETR = 1.67, GBRT = 1.66, STL = 1.51). Similar findings were obtained using the suggested method in conjunction with 19-band data, demonstrating the promising potential of this method to estimate the CCC and LAI of crops at the satellite scale. Full article

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

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20 pages, 12959 KiB

Open AccessArticle

Modeling the Corn Residue Coverage after Harvesting and before Sowing in Northeast China by Random Forest and Soil Texture Zoning

by Yi Dong, Fu Xuan, Ziqian Li, Wei Su, Hui Guo, Xianda Huang, Xuecao Li and Jianxi Huang

Remote Sens. 2023, 15(8), 2179; https://doi.org/10.3390/rs15082179 - 20 Apr 2023

Cited by 3 | Viewed by 1247

Abstract

Crop residue cover is vital for reducing soil erosion and improving soil fertility, which is an important way of conserving tillage to protect the black soil in Northeast China. How much the crop residue covers on cropland is of significance for black soil protection. Landsat-8 and Sentinel-2 images were used to estimate corn residue coverage (CRC) in Northeast China in this study. The estimation model of CRC was established for improving CRC estimation accuracy by the optimal combination of spectral indices and textural features, based on soil texture zoning, using the random forest regression method. Our results revealed that (1) the optimization C5 of spectral indices and textural features improves the CRC estimation accuracy after harvesting and before sowing with determination coefficients (R²) of 0.78 and 0.73, respectively; (2) the random forest improves the CRC estimation accuracy after harvesting and before sowing with R² of 0.81 and 0.77, respectively; (3) considering the spatial heterogeneity of the soil background and the usage of soil texture zoning models increase the accuracy of CRC estimation after harvesting and before sowing with R² of 0.84 and 0.81, respectively. In general, the CRC estimation accuracy after harvesting was better than that before sowing. The results revealed that the corn residue coverage in most of the study area was 0.3 to 0.6 and was mainly distributed in the Songnen Plain. By the estimated corn residue coverage results, the implementation of conservation tillage practices is identified, which is vital for protecting the black soil in Northeast China. Full article

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

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16 pages, 11338 KiB

Open AccessArticle

Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

by Anting Guo, Huichun Ye, Guoqing Li, Bing Zhang, Wenjiang Huang, Quanjun Jiao, Binxiang Qian and Peilei Luo

Remote Sens. 2023, 15(7), 1784; https://doi.org/10.3390/rs15071784 - 27 Mar 2023

Cited by 3 | Viewed by 1315

Abstract

Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model’s accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R² of 0.567 and an RMSE of 5.598, and the model with AL had an R² of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R² = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model’s robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R² = 0.792, RMSE = 2.8; CCC: R² = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth. Full article

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

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19 pages, 8089 KiB

Open AccessArticle

Cotton Fiber Quality Estimation Based on Machine Learning Using Time Series UAV Remote Sensing Data

by Weicheng Xu, Weiguang Yang, Pengchao Chen, Yilong Zhan, Lei Zhang and Yubin Lan

Remote Sens. 2023, 15(3), 586; https://doi.org/10.3390/rs15030586 - 18 Jan 2023

Cited by 3 | Viewed by 1890

Abstract

As an important factor determining the competitiveness of raw cotton, cotton fiber quality has received more and more attention. The results of traditional detection methods are accurate, but the sampling cost is high and has a hysteresis, which makes it difficult to measure cotton fiber quality parameters in real time and at a large scale. The purpose of this study is to use time-series UAV (Unmanned Aerial Vehicle) multispectral and RGB remote sensing images combined with machine learning to model four main quality indicators of cotton fibers. A deep learning algorithm is used to identify and extract cotton boll pixels in remote sensing images and improve the accuracy of quantitative extraction of spectral features. In order to simplify the input parameters of the model, the stepwise sensitivity analysis method is used to eliminate redundant variables and obtain the optimal input feature set. The results of this study show that the R² of the prediction model established by a neural network is improved by 29.67% compared with the model established by linear regression. When the spectral index is calculated after removing the soil pixels used for prediction, R² is improved by 4.01% compared with the ordinary method. The prediction model can well predict the average length, uniformity index, and micronaire value of the upper half. R² is 0.8250, 0.8014, and 0.7722, respectively. This study provides a method to predict the cotton fiber quality in a large area without manual sampling, which provides a new idea for variety breeding and commercial decision-making in the cotton industry. Full article

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

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17 pages, 3632 KiB

Open AccessArticle

Multisensor Assessment of Leaf Area Index across Ecoregions of Ardabil Province, Northwestern Iran

by Lida Andalibi, Ardavan Ghorbani, Roshanak Darvishzadeh, Mehdi Moameri, Zeinab Hazbavi, Reza Jafari and Farid Dadjou

Remote Sens. 2022, 14(22), 5731; https://doi.org/10.3390/rs14225731 - 13 Nov 2022

Cited by 2 | Viewed by 1423

Abstract

Leaf area index (LAI), one of the most crucial vegetation biophysical variables, is required to evaluate the structural characteristic of plant communities. This study, therefore, aimed to evaluate the LAI of ecoregions in Iran obtained using Sentinel-2B, Landsat 8 (OLI), MODIS, and AVHRR data in June and July 2020. A field survey was performed in different ecoregions throughout Ardabil Province during June and July 2020 under the satellite image dates. A Laipen LP 100 (LP 100) field-portable device was used to measure the LAI in 822 samples with different plant functional types (PFTs) of shrubs, bushes, and trees. The LAI was estimated using the SNAPv7.0.4 (Sentinel Application Platform) software for Sentinel-2B data and Google Earth Engine (GEE) system–based EVI for Landsat 8. At the same time, for MODIS and AVHRR, the LAI products of GEE were considered. The results of all satellite-based methods verified the LAI variations in space and time for every PFT. Based on Sentinel-2B, Landsat 8, MODIS, and AVHRR application, the minimum and maximum LAIs were respectively obtained at 0.14–1.78, 0.09–3.74, 0.82–4.69, and 0.35–2.73 for shrubs; 0.17–5.17, 0.3–2.3, 0.59–3.84, and 0.63–3.47 for bushes; and 0.3–4.4, 0.3–4.5, 0.7–4.3, and 0.5–3.3 for trees. These estimated values were lower than the LAI values of LP 100 (i.e., 0.4–4.10 for shrubs, 1.6–7.7 for bushes, and 3.1–6.8 for trees). A significant correlation (p < 0.05) for almost all studied PFTs between LP 100-LAI and estimated LAI from sensors was also observed in Sentinel-2B (|r| > 0.63 and R² > 0.89), Landsat 8 (|r| > 0.50 and R² > 0.72), MODIS (|r| > 0.65 and R² > 0.88), and AVHRR (|r| > 0.59 and R² > 0.68). Due to its high spatial resolution and relatively significant correlation with terrestrial data, Sentinel-2B was more suitable for calculating the LAI. The results obtained from this study can be used in future studies on sustainable rangeland management and conservation. Full article

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

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18 pages, 20053 KiB

Open AccessArticle

Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data

by Peilei Luo, Huichun Ye, Wenjiang Huang, Jingjuan Liao, Quanjun Jiao, Anting Guo and Binxiang Qian

Remote Sens. 2022, 14(21), 5624; https://doi.org/10.3390/rs14215624 - 07 Nov 2022

Viewed by 1499

Abstract

Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimation. However, these models and methods mostly rely on handcrafted features and theoretical formulas under idealized assumptions, which limits their accuracy. Deep neural networks have demonstrated great superiority in automatic feature extraction and complicated nonlinear approximation, but their application to LAI and biomass estimation has been hindered by the shortage of in situ data. Therefore, bridging the gap of data shortage and making it possible to leverage deep neural networks to estimate maize LAI and biomass is of great significance. Optical data cannot provide information in the lower canopy due to the limited penetrability, but synthetic aperture radar (SAR) data can do this, so the integration of optical and SAR data is necessary. In this paper, 158 samples from the jointing, trumpet, flowering, and filling stages of maize were collected for investigation. First, we propose an improved version of the mixup training method, which is termed mixup⁺, to augment the sample amount. We then constructed a novel gated Siamese deep neural network (GSDNN) based on a gating mechanism and a Siamese architecture to integrate optical and SAR data for the estimation of the LAI and biomass. We compared the accuracy of the GSDNN with those of other machine learning methods, i.e., multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and a multilayer perceptron (MLP). The experimental results show that without the use of mixup

^{+}

, the GSDNN achieved a similar accuracy to that of the simple neural network MLP in terms of

R^{2}

and RMSE, and this was slightly lower than those of MLR, SVR, and RFR. However, with the help of mixup

^{+}

, the GSDNN achieved state-of-the-art performance (

R^{2}

= 0.71, 0.78, and 0.86 and RMSE = 0.58, 871.83, and 150.76 g/m

^{2}

, for LAI, Biomass_wet, and Biomass_dry, respectively), exceeding the accuracies of MLR, SVR, RFR, and MLP. In addition, through the integration of optical and SAR data, the GSDNN achieved better accuracy in LAI and biomass estimation than when optical or SAR data alone were used. We found that the most appropriate amount of synthetic data from mixup

^{+}

was five times the amount of original data. Overall, this study demonstrates that the GSDNN + mixup

^{+}

has great potential for the integration of optical and SAR data with the aim of improving the estimation accuracy of the maize LAI and biomass with limited in situ data. Full article

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

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16 pages, 5499 KiB

Open AccessArticle

An Investigation of Winter Wheat Leaf Area Index Fitting Model Using Spectral and Canopy Height Model Data from Unmanned Aerial Vehicle Imagery

by Xuewei Zhang, Kefei Zhang, Suqin Wu, Hongtao Shi, Yaqin Sun, Yindi Zhao, Erjiang Fu, Shuo Chen, Chaofa Bian and Wei Ban

Remote Sens. 2022, 14(20), 5087; https://doi.org/10.3390/rs14205087 - 12 Oct 2022

Cited by 12 | Viewed by 1543

Abstract

The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R² for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring. Full article

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

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23 pages, 12978 KiB

Open AccessArticle

Mapping Shrub Coverage in Xilin Gol Grassland with Multi-Temporal Sentinel-2 Imagery

by Liqin Gan, Xin Cao, Xuehong Chen, Qian He, Xihong Cui and Chenchen Zhao

Remote Sens. 2022, 14(14), 3266; https://doi.org/10.3390/rs14143266 - 07 Jul 2022

Cited by 5 | Viewed by 2177

Abstract

In recent decades, shrubs dominated by the genus Caragana have expanded in a large area in Xilin Gol grassland, Inner Mongolia, China. This study comprehensively evaluated the performances of multiple factors for mapping shrub coverage across the Xilin Gol grassland based on the spectral and temporal signatures of Sentinel-2 imagery, and for the first time produced a large-scale shrub coverage mapping result in this region. Considering the regional differences and gradients in the types and sizes of shrub in the study area, the study area was divided into three subregions based on precipitation data, i.e., west, middle and east regions. The shrub coverage estimation accuracy from dry- and wet-year data, different types of vegetation indices (VIs) and multiple regression methods were compared in each subregion, and the key phenological periods were selected. We also compared the accuracy of four mapping strategies, which were pairwise combinations of zoning (i.e., subregions divided by precipitation) and non-zoning, and full time series of VIs and key phenological period. Results show that the mapping accuracy in a dry year (2017) is higher than that in a wet year (2018). The optimal VIs and key phenological periods show high spatial variability. In terms of mapping strategies, the accuracy of zoning is higher than that of non-zoning. The root mean square error (RMSE), overall accuracy (OA) and recall for ‘zoning + full time series (or + key phenological period)’ strategy were 0.052 (0.055), 76.4% (79.7%) and 91.7% (94.6%), respectively, while for ‘non-zoning + full time series (or + key phenological period)’ strategy were 0.057 (0.060), 75.5% (74.6%) and 91.7% (88.6%), respectively. The mapping using VIs in key phenological periods is better than that of using full time series in the low-value prediction of shrub cover. Based on the strategy of ‘zoning + key phenological period’, the shrub coverage map of the whole region was generated with a RMSE of 0.055, OA of 80% and recall of 95%. This study not only provides the first large-scale mapping data of shrub coverage, but also provides reference for shrub dynamic monitoring in this area. Full article

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

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Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity—A Review

by Patrick Kacic and Claudia Kuenzer

Remote Sens. 2022, 14(21), 5363; https://doi.org/10.3390/rs14215363 - 26 Oct 2022

Cited by 14 | Viewed by 5638

Abstract

Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction [...] Read more.

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scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts “vegetation indices”, “spectral information content”, and “spectral species” for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future. Full article

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

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