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Remote Sensing of Vegetation Biochemical and Biophysical Parameters

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 33624

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Special Issue Editors

Dr. Bing Lu

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Guest Editor

Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
Interests: remote sensing; environmental change; grassland-wetland; ecosystems; precision agriculture; estuarine and coastal dynamics; remote sensing big data
Special Issues, Collections and Topics in MDPI journals

Dr. Dameng Yin

E-Mail Website
Guest Editor

Center for Crop Management & Farming System, Institute of Crop Sciences, CAAS, No. 12 Zhongguancun South Street, Beijing 100081, China
Interests: LiDAR remote sensing for crop phenotyping
Special Issues, Collections and Topics in MDPI journals

Dr. Holly Croft

E-Mail Website
Guest Editor

School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
Interests: leaf chlorophyll; vegetation remote sensing; ecophysiology; radiative transfer modelling; plant phenology; plant photosynthetic traits
Special Issues, Collections and Topics in MDPI journals

Dr. Katja Berger

E-Mail Website
Guest Editor

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
Special Issues, Collections and Topics in MDPI journals

Dr. Tao Liu

E-Mail Website
Guest Editor

College of Forest Resources and Environmental Science, Michigan Tech, Houghton, MI 49931, USA
Interests: LiDAR point processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The biochemical and biophysical variables of vegetation, such as leaf area index, chlorophyll content, and species composition, are essential vegetation characteristics that influence plant physiological status, vegetation productivity, and ecosystem health. The investigation of vegetation biochemical and biophysical properties is critical for understanding vegetation growth conditions, evaluating ecosystem services, and supporting resource management. Compared to field measurements of vegetation properties that are costly, labor-intensive, and limited to small areas, remote sensing is a more efficient and powerful tool for estimating vegetation properties and investigating their spatio-temporal variations over large areas.

Multi-type remote sensing data (e.g., optical reflective, LiDAR, Radar, and thermal) are capable of capturing different vegetation information. For instance, optical images can record the surface or top-layer features of vegetation (e.g., chlorophyll content), LiDAR and Radar signals are more sensitive to the 3D structure of vegetation (e.g., canopy height, leaf area index), while thermal data can reflect vegetation stresses in early stages (e.g., water shortage). These different types of remote sensing images have become more widely available in recent years for mapping various vegetation biochemical and biophysical properties.

Different remote sensing platforms, including satellites, airplanes, helicopters, and unmanned aerial vehicles (UAVs), have been commonly used in recent years for mapping ground features at different altitudes and thus providing data with different spatial and temporal resolutions. The higher availability of different platforms provides unprecedented opportunities for investigating vegetation properties at different spatio-temporal scales, which will facilitate a more solid understanding of the physiological status of vegetation and of ecosystem processes.

A range of data analysis methods are available for processing remote sensing data into meaningful vegetation properties, such as empirical regressions, machine learning and deep learning, physical modelling, and hybrids of these methods. They have different advantages and disadvantages in terms of accuracy, transferability, and complexity and are thus appropriate for different application scenarios. Understanding features of these methods is critical for retrieving vegetation properties from remote sensing data efficiently and accurately.

To better understand the challenges and opportunities in mapping the biochemical and biophysical properties of vegetation with different types of sensors, platforms, and analytical methods, together with related applications in ecosystem monitoring and modelling, this Special Issues invites contributions in a range of research areas, including, but not limited to, the following:

Vegetation mapping with different types of sensors (e.g., optical, LiDAR, Radar, and thermal);
Fusion of multi-type data;
Recent hyperspectral sensors and techniques (e.g., EnMAP or PRISMA);
Applications of different platforms (e.g., satellites, UAVs) for vegetation mapping;
Innovative analytical methods for estimating vegetation properties;
Machine learning and deep learning;
Radiative transfer modeling;
Hybrid of different analytical methods;
Cloud computing and remote sensing big data;
Vegetation classification and biodiversity mapping;
Ecosystem and habitat modelling;
Impacts of environmental factors on vegetation health.

Dr. Bing Lu
Dr. Dameng Yin
Dr. Holly Croft
Dr. Katja Berger
Dr. Tao Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 biochemical and biophysical parameters
multispectral and hyperspectral
remote sensing platforms
unmanned aerial vehicle
empirical regression
machine learning and deep learning
radiative transfer modelling
species classification
ecosystem health
data fusion

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Research

22 pages, 10747 KiB

Open AccessArticle

Using Satellite Data to Characterize Land Surface Processes in Morocco

by Mohammed Thaiki, Lahouari Bounoua and Hinde Cherkaoui Dekkaki

Remote Sens. 2023, 15(22), 5389; https://doi.org/10.3390/rs15225389 - 17 Nov 2023

Cited by 1 | Viewed by 2311

Abstract

This study endeavors to produce a comprehensive land cover map for Morocco, addressing the absence of such a detailed map in the country. Our research encompasses ecological and climatic aspects specific to Morocco, while the methods used can be adapted to various regions and countries, considering their unique climatic conditions and land cover types. A combination of MODIS and Landsat datasets was employed to create a 5 km resolution Land Use and Land Cover (LULC) map for the entire nation. The process involved the aggregation and advanced processing of these datasets using surface processes algorithms. The resulting LULC map is the first of its kind for Morocco, shedding light on land cover distribution nationwide. It shows that approximately 13.5% of the country is covered by forests, predominantly in the Atlas and Rif mountains, Rabat–Sale, and the southern regions. Grasslands occupy over 16% of the study area, mainly in the north-east and west. Urban areas, including major cities like Casablanca, Rabat, and Marrakech, span nearly 3400 km². Moreover, large areas of shrublands and bare lands are evident across the country, while agricultural lands account for almost 20% of the national territory, mainly in the interior plains and north-western Atlantic coast. This study forms a crucial basis for ecological and climatic research in Morocco and serves as a valuable reference for various disciplines such as agriculture, natural resource management, and climate modeling. The mapping of biophysical parameters for each land cover class is a key feature of our research, and these parameters will be instrumental in a subsequent study examining the impact of urban development on surface climate in Morocco. Overall, our study underscores the importance of understanding biophysical parameters in addressing environmental and societal challenges. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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16 pages, 9853 KiB

Open AccessArticle

Investigating the Relationship between Topographic Factors and Vegetation Spatial Patterns in the Alpine Plateau: A Case Study in the Southwestern Tibetan Plateau

by Yan Li, Jie Gong and Yunxia Zhang

Remote Sens. 2023, 15(22), 5356; https://doi.org/10.3390/rs15225356 - 14 Nov 2023

Cited by 6 | Viewed by 1654

Abstract

Vegetation on the Southwestern Tibetan Plateau (SWTP) is critical to ensuring ecological security and promoting regional economic and social development. Here, we explored the relationship between topographic factors (elevation, slope, and aspect) and the spatial patterns in the normalized difference vegetation index (NDVI) in the SWTP over the past 20 years. The results found that the NDVI in the SWTP was primarily influenced by elevation and slope. The regions with significant variations in NDVI were concentrated between 4500 m to 5500 m and slopes ranging from 0° to 15°. Although the influence of aspect on NDVI was small, there was a decreasing trend in NDVI on sunny slopes and an increasing trend on shady slopes. Dominant topographic conditions were identified by considering 230 different combinations of elevation, slope, and aspect. The combination of topographic parameters indicated stronger patterns in NDVI variability, notably within sections of 0°–25°slopes and below 5000 m elevation. These findings highlight the relevance of topography, notably slope and aspect, for vegetation in alpine settings. The information gathered from this study about the prevailing topographic distribution and vegetation growth state in the SWTP can help with future ecological restoration and conservation efforts in the Tibetan Plateau and other comparable regions worldwide. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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29 pages, 44178 KiB

Open AccessEditor’s ChoiceArticle

Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine

by Dávid D. Kovács, Pablo Reyes-Muñoz, Matías Salinero-Delgado, Viktor Ixion Mészáros, Katja Berger and Jochem Verrelst

Remote Sens. 2023, 15(13), 3404; https://doi.org/10.3390/rs15133404 - 5 Jul 2023

Cited by 13 | Viewed by 5419

Abstract

Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. Although significant efforts have been made, there is still a lack of global and consistently derived multi-temporal trait maps that are cloud-free. Here we present the processing chain for the spatiotemporally continuous production of four EVTs at a global scale: (1) fraction of absorbed photosynthetically active radiation (FAPAR), (2) leaf area index (LAI), (3) fractional vegetation cover (FVC), and (4) leaf chlorophyll content (LCC). The proposed workflow presents a scalable processing approach to the global cloud-free mapping of the EVTs. Hybrid retrieval models, named S3-TOA-GPR-1.0-WS, were implemented into Google Earth Engine (GEE) using Sentinel-3 Ocean and Land Color Instrument (OLCI) Level-1B for the mapping of the four EVTs along with associated uncertainty estimates. We used the Whittaker smoother (WS) for the temporal reconstruction of the four EVTs, which led to continuous data streams, here applied to the year 2019. Cloud-free maps were produced at 5 km spatial resolution at 10-day time intervals. The consistency and plausibility of the EVT estimates for the resulting annual profiles were evaluated by per-pixel intra-annually correlating against corresponding vegetation products of both MODIS and Copernicus Global Land Service (CGLS). The most consistent results were obtained for LAI, which showed intra-annual correlations with an average Pearson correlation coefficient (R) of 0.57 against the CGLS LAI product. Globally, the EVT products showed consistent results, specifically obtaining higher correlation than

R >

0.5 with reference products between 30 and 60° latitude in the Northern Hemisphere. Additionally, intra-annual goodness-of-fit statistics were also calculated locally against reference products over four distinct vegetated land covers. As a general trend, vegetated land covers with pronounced phenological dynamics led to high correlations between the different products. However, sparsely vegetated fields as well as areas near the equator linked to smaller seasonality led to lower correlations. We conclude that the global gap-free mapping of the four EVTs was overall consistent. Thanks to GEE, the entire OLCI L1B catalogue can be processed efficiently into the EVT products on a global scale and made cloud-free with the WS temporal reconstruction method. Additionally, GEE facilitates the workflow to be operationally applicable and easily accessible to the broader community. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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21 pages, 35883 KiB

Open AccessArticle

A Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data

by Mohammad Hossain Dehghan-Shoar, Reddy R. Pullanagari, Gabor Kereszturi, Alvaro A. Orsi, Ian J. Yule and James Hanly

Remote Sens. 2023, 15(10), 2491; https://doi.org/10.3390/rs15102491 - 9 May 2023

Cited by 10 | Viewed by 3462

Abstract

The increasing number of satellite missions provides vast opportunities for continuous vegetation monitoring, crucial for precision agriculture and environmental sustainability. However, accurately estimating vegetation traits, such as nitrogen concentration (N%), from Landsat 7 (L7), Landsat 8 (L8), and Sentinel-2 (S2) satellite data is challenging due to the diverse sensor configurations and complex atmospheric interactions. To address these limitations, we developed a unified and physically based method that combines a soil–plant–atmosphere radiative transfer (SPART) model with the bottom-of-atmosphere (BOA) spectral bidirectional reflectance distribution function. This approach enables us to assess the effect of rugged terrain, viewing angles, and illumination geometry on the spectral reflectance of multiple sensors. Our methodology involves inverting radiative transfer model variables using numerical optimization to estimate N% and creating a hybrid model. We used Gaussian process regression (GPR) to incorporate the inverted variables into the hybrid model for N% prediction, resulting in a unified approach for N% estimation across different sensors. Our model shows a validation accuracy of 0.35 (RMSE %N), a mean prediction interval width (MPIW) of 0.35, and an R

^{2}

of 0.50, using independent data from multiple sensors collected between 2016 and 2019. Our unified method provides a promising solution for estimating N% in vegetation from L7, L8, and S2 satellite data, overcoming the limitations posed by diverse sensor configurations and complex atmospheric interactions. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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29 pages, 6027 KiB

Open AccessArticle

Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016

by Qiaoli Wu, Shaoyuan Chen, Yulong Zhang, Conghe Song, Weimin Ju, Li Wang and Jie Jiang

Remote Sens. 2023, 15(5), 1172; https://doi.org/10.3390/rs15051172 - 21 Feb 2023

Cited by 8 | Viewed by 2451

Abstract

The value of leaf photosynthetic capacity (V_cmax) varies with time and space, but state-of-the-art terrestrial biosphere models rarely include such V_cmax variability, hindering the accuracy of carbon cycle estimations on a large scale. In particular, while the European terrestrial ecosystem is particularly sensitive to climate change, current estimates of gross primary production (GPP) in Europe are subject to significant uncertainties (2.5 to 8.7 Pg C yr⁻¹). This study applied a process-based Farquhar GPP model (FGM) to improve GPP estimation by introducing a spatially and temporally explicit V_cmax derived from the satellite-based leaf chlorophyll content (LCC) on two scales: across multiple eddy covariance tower sites and on the regional scale. Across the 19 EuroFLUX sites selected for independent model validation based on 9 plant functional types (PFTs), relative to the biome-specific V_cmax, the inclusion of the LCC-derived V_cmax improved the model estimates of GPP, with the coefficient of determination (R²) increased by 23% and the root mean square error (RMSE) decreased by 25%. V_cmax values are typically parameterized with PFT-specific V_cmax calibrated from flux tower observations or empirical V_cmax based on the TRY database (which includes 723 data points derived from V_cmax field measurements). On the regional scale, compared with GPP, using the LCC-derived V_cmax, the conventional method of fixing V_cmax using the calibrated V_cmax or TRY-based V_cmax overestimated the annual GPP of Europe by 0.5 to 2.9 Pg C yr⁻¹ or 5 to 31% and overestimated the interannually increasing GPP trend by 0.007 to 0.01 Pg C yr⁻² or 14 to 20%, respectively. The spatial pattern and interannual change trend of the European GPP estimated by the improved FGM showed general consistency with the existing studies, while our estimates indicated that the European terrestrial ecosystem (including part of Russia) had higher carbon assimilation potential (9.4 Pg C yr⁻¹). Our study highlighted the urgent need to develop spatially and temporally consistent V_cmax products with a high accuracy so as to reduce uncertainties in global carbon modeling and improve our understanding of how terrestrial ecosystems respond to climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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23 pages, 8109 KiB

Open AccessArticle

A Remote Sensing Method for Crop Mapping Based on Multiscale Neighborhood Feature Extraction

by Yongchuang Wu, Yanlan Wu, Biao Wang and Hui Yang

Remote Sens. 2023, 15(1), 47; https://doi.org/10.3390/rs15010047 - 22 Dec 2022

Cited by 9 | Viewed by 3320

Abstract

Obtaining accurate and timely crop mapping is essential for refined agricultural refinement and food security. Due to the spectral similarity between different crops, the influence of image resolution, the boundary blur and spatial inconsistency that often occur in remotely sensed crop mapping, remotely sensed crop mapping still faces great challenges. In this article, we propose to extend a neighborhood window centered on the target pixel to enhance the receptive field of our model and extract the spatial and spectral features of different neighborhood sizes through a multiscale network. In addition, we also designed a coordinate convolutional module and a convolutional block attention module to further enhance the spatial information and spectral features in the neighborhoods. Our experimental results show that this method allowed us to obtain accuracy scores of 0.9481, 0.9115, 0.9307 and 0.8729 for OA, kappa coefficient, F1 score and IOU, respectively, which were better than those obtained using other methods (Resnet-18, MLP and RFC). The comparison of the experimental results obtained from different neighborhood window sizes shows that the spatial inconsistency and boundary blurring in crop mapping could be effectively reduced by extending the neighborhood windows. It was also shown in the ablation experiments that the coordinate convolutional and convolutional block attention modules played active roles in the network. Therefore, the method proposed in this article could provide reliable technical support for remotely sensed crop mapping. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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26 pages, 5495 KiB

Open AccessArticle

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

by Xuzhen Zhong, Jie Li, Jinliang Wang, Jianpeng Zhang, Lanfang Liu and Jun Ma

Remote Sens. 2022, 14(24), 6271; https://doi.org/10.3390/rs14246271 - 10 Dec 2022

Cited by 27 | Viewed by 3484

Abstract

Vegetation is the main body of the terrestrial ecosystem and is a significant indicator of environmental changes in the regional ecosystem. As an essential link connecting South Asia and Southeast Asia, the Lancang-Mekong River Basin(LMRB) can provide essential data support and a decision-making basis for the assessment of terrestrial ecosystem environmental changes and the research and management of hydrology and water resources in the basin by monitoring changes in its vegetation cover. This study takes the Lancang-Mekong River Basin as the study area, and employs the Sen slope estimation, Mann–Kendall test, and Hurst exponent based on the MODIS NDVI data from 2000 to 2021 to study the spatial and temporal evolution trend and future sustainability of its NDVI. Besides, the nonlinear characteristics such as mutation type and mutation year are detected and analyzed using the BFAST01 method. Results demonstrated that: (1) In the past 22 years, the NDVI of the Lancang-Mekong River Basin generally exhibited a fluctuating upward trend, and the NDVI value in 2021 was the largest, which was 0.825, showing an increase of 4.29% compared with 2000. However, the increase rate was different: China has the most considerable NDVI growth rate of 7.25%, followed by Thailand with an increase of 7.21%, Myanmar and Laos as the third, while Cambodia and Vietnam have relatively stable vegetation changes. The overall performance of NDVI is high in the south and low in the north, and is dominated by high and relatively high vegetation coverage, of which the area with vegetation coverage exceeding 0.8 accounts for 62%. (2) The Sen-MK trend showed that from 2000 to 2021, the area where the vegetation coverage in the basin showed a trend of increase and decrease accounted for 66.59% and 18.88%, respectively. The Hurst exponent indicated that the areas where NDVI will continue to increase, decrease, and remain unchanged in the future account for 60.14%, 25.29%, and 14.53%, respectively, and the future development trend of NDVI is uncertain, accounting for 0.04%. Thus, more attention should be paid to areas with a descending future development trend. (3) BFAST01 detected eight NDVI mutation types in the Lancang-Mekong River Basin over the past 22 years. The mutations mainly occurred in 2002–2018, while 2002–2004 and 2014–2018 were the most frequent periods of breakpoints. The mutation type of “interruption: increase with negative break” was changed the most during this period, which accounts for 36.54%, and the smallest was “monotonic decrease (with negative break)”, which only accounts for 0.65%. This research demonstrates that combining the conventional trend analysis method with the BFAST mutation test can more accurately analyze the spatiotemporal variation and nonlinear mutation of NDVI, thus providing a scientific reference to develop ecological environment-related work. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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23 pages, 52423 KiB

Open AccessArticle

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles

by Gabriel Caballero, Alejandro Pezzola, Cristina Winschel, Alejandra Casella, Paolo Sanchez Angonova, Luciano Orden, Katja Berger, Jochem Verrelst and Jesús Delegido

Remote Sens. 2022, 14(22), 5867; https://doi.org/10.3390/rs14225867 - 19 Nov 2022

Cited by 8 | Viewed by 3078

Abstract

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition’s geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R

_{C V}^{2}

= 0.67 and RMSE

_{C V}

= 0.88 m

^{2}

^{- 2}

. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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24 pages, 14528 KiB

Open AccessArticle

Unsupervised Domain Adaptation with Adversarial Self-Training for Crop Classification Using Remote Sensing Images

by Geun-Ho Kwak and No-Wook Park

Remote Sens. 2022, 14(18), 4639; https://doi.org/10.3390/rs14184639 - 16 Sep 2022

Cited by 27 | Viewed by 3244

Abstract

Crop type mapping is regarded as an essential part of effective agricultural management. Automated crop type mapping using remote sensing images is preferred for the consistent monitoring of crop types. However, the main obstacle to generating annual crop type maps is the collection of sufficient training data for supervised classification. Classification based on unsupervised domain adaptation, which uses prior information from the source domain for target domain classification, can solve the impractical problem of collecting sufficient training data. This study presents self-training with domain adversarial network (STDAN), a novel unsupervised domain adaptation framework for crop type classification. The core purpose of STDAN is to combine adversarial training to alleviate spectral discrepancy problems with self-training to automatically generate new training data in the target domain using an existing thematic map or ground truth data. STDAN consists of three analysis stages: (1) initial classification using domain adversarial neural networks; (2) the self-training-based updating of training candidates using constraints specific to crop classification; and (3) the refinement of training candidates using iterative classification and final classification. The potential of STDAN was evaluated by conducting six experiments reflecting various domain discrepancy conditions in unmanned aerial vehicle images acquired at different regions and times. In most cases, the classification performance of STDAN was found to be compatible with the classification using training data collected from the target domain. In particular, the superiority of STDAN was shown to be prominent when the domain discrepancy was substantial. Based on these results, STDAN can be effectively applied to automated cross-domain crop type mapping without analyst intervention when prior information is available in the target domain. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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11 pages, 2059 KiB

Open AccessCommunication

A Novel Method to Simultaneously Measure Leaf Gas Exchange and Water Content

by Samuli Junttila, Teemu Hölttä, Yann Salmon, Iolanda Filella and Josep Peñuelas

Remote Sens. 2022, 14(15), 3693; https://doi.org/10.3390/rs14153693 - 2 Aug 2022

Cited by 4 | Viewed by 2818

Abstract

Understanding the relationship between plant water status and productivity and between plant water status and plant mortality is required to effectively quantify and predict the effects of drought on plants. Plant water status is closely linked to leaf water content that may be estimated using remote sensing technologies. Here, we used an inexpensive miniature hyperspectral spectrometer in the 1550–1950 nm wavelength domain to measure changes in silver birch (Betula pendula Roth) leaf water content combined with leaf gas exchange measurements at a sub-minute time resolution, under increasing vapor pressure deficit, CO₂ concentrations, and light intensity within the measurement cuvette; we also developed a novel methodology for calibrating reflectance measurements to predict leaf water content for individual leaves. Based on reflectance at 1550 nm, linear regression modeling explained 98–99% of the variation in leaf water content, with a root mean square error of 0.31–0.43 g cm⁻². The prediction accuracy of the model represents a c. ten-fold improvement compared to previous studies that have used destructive sampling measurements of several leaves. This novel methodology allows the study of interlinkages between leaf water content, transpiration, and assimilation at a high time resolution that will increase understanding of the movement of water within plants and between plants and the atmosphere. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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