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Land
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Land Surface Monitoring Based on Satellite Imagery
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Land Surface Monitoring Based on Satellite Imagery

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Systems and Global Change".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 47669

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Sara Venafra

E-Mail Website
Guest Editor
Italian Space Agency, 75100 Matera, Italy
Interests: satellite remote sensing; land surface change detection; retrieval of surface and atmospheric parameters; climate; radiative transfer; air quality; global warming and change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carmine Serio

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: satellite remote sensing of surface and atmospheric parameters; land surface change detection; radiative transfer in cloudy and clear atmosphere; Fourier spectroscopy applied to remote sensing of atmosphere; satellite instruments characterization; climate; global warming and change; inverse problems and dimensionality reduction of data space; satellite retrieval of atmospheric constituents and aerosols; greenhouse gases; air quality
Special Issues, Collections and Topics in MDPI journals
Dr. Guido Masiello

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: satellite remote sensing of surface and atmospheric parameters; land surface change detection; radiative transfer in cloudy and clear atmosphere; Fourier spectroscopy applied to remote sensing of atmosphere; satellite instruments characterization; climate; global warming and change; inverse problems and dimensionality reduction of data space; satellite retrieval of atmospheric constituents and aerosols; greenhouse gases; air quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land surface monitoring plays a significant role in the study of climate change and global warming. Even though in situ measurements represent the most accurate way to measure surface parameters, they lack in spatial and temporal resolution. For this purpose, satellite data provide a global coverage and higher temporal resolution with very accurate retrievals of land parameters such as surface temperature and emissivity. Land surface parameters from remote sensing are incredibly attractive for applications in different environmental fields, such as land use/change, monitoring of vegetation and soil water stress, and early warning and detection of forest fires and drought. Typically, monitoring of land cover changes is based on the definition of vegetation indices, exploiting the surface information provided by the spectral channels in the visible and the infrared.

We invite researchers and academics to submit papers that deal but are not limited to the previous topics.

Dr. Sara Venafra
Prof. Dr. Carmine Serio
Prof. Dr. Guido Masiello
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. Land is an international peer-reviewed open access monthly 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 2600 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

  • satellite remote sensing
  • land surface parameters
  • surface change detection
  • radiative transfer
  • infrared spectroscopy
  • global warming
  • climate
  • vegetation Indices

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Published Papers (11 papers)

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Research

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16 pages, 10002 KiB  
Article
Correlation Analysis of Evapotranspiration, Emissivity Contrast and Water Deficit Indices: A Case Study in Four Eddy Covariance Sites in Italy with Different Environmental Habitats
by Michele Torresani, Guido Masiello, Nadia Vendrame, Giacomo Gerosa, Marco Falocchi, Enrico Tomelleri, Carmine Serio, Duccio Rocchini and Dino Zardi
Land 2022, 11(11), 1903; https://0-doi-org.brum.beds.ac.uk/10.3390/land11111903 - 26 Oct 2022
Cited by 6 | Viewed by 1511
Abstract
Evapotranspiration (ET) represents one of the essential processes controlling the exchange of energy by terrestrial vegetation, providing a strong connection between energy and water fluxes. Different methodologies have been developed in order to measure it at different spatial scales, ranging from individual plants [...] Read more.
Evapotranspiration (ET) represents one of the essential processes controlling the exchange of energy by terrestrial vegetation, providing a strong connection between energy and water fluxes. Different methodologies have been developed in order to measure it at different spatial scales, ranging from individual plants to an entire watershed. In the last few years, several methods and approaches based on remotely sensed data have been developed over different ecosystems for the estimation of ET. In the present work, we outline the correlation between ET measured at four eddy covariance (EC) sites in Italy (situated either in forest or in grassland ecosystems) and (1) the emissivity contrast index (ECI) based on emissivity data from thermal infrared spectral channels of the MODIS and ASTER satellite sensors (CAMEL data-set); (2) the water deficit index (WDI), defined as the difference between the surface and dew point temperature modeled by the ECMWF (European Centre for Medium-Range Weather Forecasts) data. The analysis covers a time-series of 1 to 7 years depending on the site. The results showed that both the ECI and WDI correlate to the ET calculated through EC. In the relationship WDI-ET, the coefficient of determination ranges, depending on the study area, between 0.5 and 0.9, whereas it ranges between 0.5 and 0.7 when ET was correlated to the ECI. The slope and the sign of the latter relationship is influenced by the vegetation habitat, the snow cover (particularly in winter months) and the environmental heterogeneity of the area (calculated in this study through the concept of the spectral variation hypothesis using Rao’s Q heterogeneity index). Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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31 pages, 6204 KiB  
Article
Characterization of Land-Cover Changes and Forest-Cover Dynamics in Togo between 1985 and 2020 from Landsat Images Using Google Earth Engine
by Arifou Kombate, Fousseni Folega, Wouyo Atakpama, Marra Dourma, Kperkouma Wala and Kalifa Goïta
Land 2022, 11(11), 1889; https://0-doi-org.brum.beds.ac.uk/10.3390/land11111889 - 25 Oct 2022
Cited by 8 | Viewed by 2279
Abstract
Carbon stocks in forest ecosystems, when released as a result of forest degradation, contribute to greenhouse gas (GHG) emissions. To quantify and assess the rates of these changes, the Intergovernmental Panel on Climate Change (IPCC) recommends that the REDD+ mechanism use a combination [...] Read more.
Carbon stocks in forest ecosystems, when released as a result of forest degradation, contribute to greenhouse gas (GHG) emissions. To quantify and assess the rates of these changes, the Intergovernmental Panel on Climate Change (IPCC) recommends that the REDD+ mechanism use a combination of Earth observational data and field inventories. To this end, our study characterized land-cover changes and forest-cover dynamics in Togo between 1985 and 2020, using the supervised classification of Landsat 5, 7, and 8 images on the Google Earth Engine platform with the Random Forest (RF) algorithm. Overall image classification accuracies for all target years ranged from 0.91 to 0.98, with Kappa coefficients ranging between 0.86 and 0.96. Analysis indicated that all land cover classes, which were identified at the beginning of the study period, have undergone changes at several levels, with a reduction in forest area from 49.9% of the national territory in 1985, to 23.8% in 2020. These losses of forest cover have mainly been to agriculture, savannahs, and urbanization. The annual change in forest cover was estimated at −2.11% per year, with annual deforestation at 422.15 km2 per year, which corresponds to a contraction in forest cover of 0.74% per year over the 35-year period being considered. Ecological Zone IV (mountainous, with dense semi-deciduous forests) is the one region (of five) that has best conserved its forest area over this period. This study contributes to the mission of forestry and territorial administration in Togo by providing methods and historical data regarding land cover that would help to control the factors involved in forest area reductions, reinforcing the system of measurement, notification, and verification within the REDD+ framework, and ensuring better, long-lasting management of forest ecosystems. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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14 pages, 2759 KiB  
Article
Innovative Fusion-Based Strategy for Crop Residue Modeling
by Solmaz Fathololoumi, Mohammad Karimi Firozjaei and Asim Biswas
Land 2022, 11(10), 1638; https://0-doi-org.brum.beds.ac.uk/10.3390/land11101638 - 23 Sep 2022
Cited by 3 | Viewed by 1260
Abstract
The purpose of this study was to present a new strategy based on fusion at the decision level for modeling the crop residue. To this end, a set of satellite imagery and field data, including the Residue Cover Fraction (RCF) of corn, wheat [...] Read more.
The purpose of this study was to present a new strategy based on fusion at the decision level for modeling the crop residue. To this end, a set of satellite imagery and field data, including the Residue Cover Fraction (RCF) of corn, wheat and soybean was used. Firstly, the efficiency of Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Partial-Least-Squares Regression (PLSR) in RCF modeling was evaluated. Furthermore, to increase the accuracy of RCF modeling, different algorithms results were combined based on their modeling error, which is called the decision-based fusion strategy. The R2 (RMSE) between the actual and modeled RCF based on ANN, RFR, SVR and PLSR algorithms for corn were 0.83 (3.89), 0.86 (3.25), 0.76 (4.56) and 0.75 (4.81%), respectively. These values were 0.81 (4.86), 0.85 (4.22), 0.78 (5.45) and 0.74 (6.20%) for wheat and 0.81 (3.96), 0.83 (3.38), 0.76 (5.01) and 0.72 (5.65%) for soybean, respectively. The error of corn, wheat and soybean RCF estimating decision-based fusion strategy was reduced by 0.90, 0.96 and 0.99%, respectively. The results showed that by implementing the decision-based fusion strategy, the accuracy of the RCF modeling was significantly improved. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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21 pages, 13500 KiB  
Article
Land Subsidence Detection in the Coastal Plain of Tabasco, Mexico Using Differential SAR Interferometry
by Zenia Pérez-Falls, Guillermo Martínez-Flores and Olga Sarychikhina
Land 2022, 11(9), 1473; https://0-doi-org.brum.beds.ac.uk/10.3390/land11091473 - 03 Sep 2022
Cited by 3 | Viewed by 1784
Abstract
Land subsidence (LS) increases flood vulnerability in coastal areas, coastal plains, and river deltas. The coastal plain of Tabasco (TCP) has been the scene of recurring floods, which caused economic and social damage. Hydrocarbon extraction is the main economic activity in the TCP [...] Read more.
Land subsidence (LS) increases flood vulnerability in coastal areas, coastal plains, and river deltas. The coastal plain of Tabasco (TCP) has been the scene of recurring floods, which caused economic and social damage. Hydrocarbon extraction is the main economic activity in the TCP and could be one of the causes of LS in this region. This study aimed to investigate the potential of differential SAR interferometric techniques for LS detection in the TCP. For this purpose, Sentinel-1 SLC descending and ascending images from the 2018–2019 period were used. Conventional DInSAR, together with the differential interferograms stacking (DIS) approach, was applied. The causes of interferometric coherence degradation were analyzed. In addition, Sentinel-1 GRD images were used for delimitation of areas recurrently affected by floods. Based on the results of the interferometric processing, several subsiding zones were detected. The results indicate subsidence rates of up to −6 cm/yr in the urban centers of Villahermosa, Paraíso, Comalcalco, and other localities. The results indicate the possibility of an influence of LS on the flood vulnerability of the area south of Villahermosa city. They also suggest a possible relationship between hydrocarbon extraction and surface deformation. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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18 pages, 8370 KiB  
Article
The IASI Water Deficit Index to Monitor Vegetation Stress and Early Drying in Summer Heatwaves: An Application to Southern Italy
by Guido Masiello, Francesco Ripullone, Italia De Feis, Angelo Rita, Luigi Saulino, Pamela Pasquariello, Angela Cersosimo, Sara Venafra and Carmine Serio
Land 2022, 11(8), 1366; https://0-doi-org.brum.beds.ac.uk/10.3390/land11081366 - 21 Aug 2022
Cited by 6 | Viewed by 7128
Abstract
The boreal hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change. The continental extension of this phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this [...] Read more.
The boreal hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change. The continental extension of this phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, satellite data can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm−1 with a sampling of 0.25 cm−1. The index has been used to study the 2017 heatwave, which hit continental Europe from May to October. In particular, we have examined southern Italy, where Mediterranean forests suffer from climate change. We have computed the index’s time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of an anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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11 pages, 4493 KiB  
Article
A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions
by Kyle Hartfield, Jeffrey K. Gillan, Cynthia L. Norton, Charles Conley and Willem J. D. van Leeuwen
Land 2022, 11(6), 786; https://0-doi-org.brum.beds.ac.uk/10.3390/land11060786 - 26 May 2022
Cited by 2 | Viewed by 2878
Abstract
Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique [...] Read more.
Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti’s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the ability to calculate two normalized vegetation indices that highlight cacti. Furthermore, we explore the impacts of spatial resolution by presenting spectral signatures from cacti samples taken with a handheld field spectroradiometer, drone-based hyperspectral sensor, and aerial hyperspectral sensor. These cacti indices will help measure baseline levels of cacti around the world and examine changes due to climate, disturbance, and management influences. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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21 pages, 6087 KiB  
Article
A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium
by Sophie Petit, Mattia Stasolla, Coraline Wyard, Gérard Swinnen, Xavier Neyt and Eric Hallot
Land 2022, 11(3), 360; https://0-doi-org.brum.beds.ac.uk/10.3390/land11030360 - 01 Mar 2022
Cited by 3 | Viewed by 2501
Abstract
Urban planning is a challenge, especially when it comes to limiting land take. In former industrial regions such as Wallonia, the presence of a large number of brownfields, here called “redevelopment sites”, opens up new opportunities for sustainable urban planning through their revalorization. [...] Read more.
Urban planning is a challenge, especially when it comes to limiting land take. In former industrial regions such as Wallonia, the presence of a large number of brownfields, here called “redevelopment sites”, opens up new opportunities for sustainable urban planning through their revalorization. The Walloon authorities are currently managing an inventory of more than 2200 sites, which requires a significant amount of time and resources to update. In this context, the Sentinel satellites and the Terrascope platform, the Sentinel Collaborative Ground Segment for Belgium, enabled us to deploy SARSAR, an Earth observation service used for the automated monitoring of redevelopment sites that generates regular and automatic change reports that are directly usable by the Walloon authorities. In this paper, we present the methodological aspects and implementation details of the service, which combines two well-known and robust methods: the Pruned Exact Linear Time method for change point detection and threshold-based classification, which assigns the detected changes to three different classes (vegetation, building and soil). The overall accuracy of the system is in the range of 70–90%, depending on the different methods and classes considered. Some remarks on the advantages and possible drawbacks of this approach are also provided. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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16 pages, 2206 KiB  
Article
Land Cover Dynamics along the Urban–Rural Gradient of the Port-au-Prince Agglomeration (Republic of Haiti) from 1986 to 2021
by Waselin Salomon, Yannick Useni Sikuzani, Kouagou Raoul Sambieni, Akoua Tamia Madeleine Kouakou, Yao Sadaiou Sabas Barima, Jean Marie Théodat and Jan Bogaert
Land 2022, 11(3), 355; https://0-doi-org.brum.beds.ac.uk/10.3390/land11030355 - 27 Feb 2022
Cited by 3 | Viewed by 3061
Abstract
The landscape of the Port-au-Prince agglomeration in the Republic of Haiti has undergone profound changes linked to (peri-)urban expansion supported by rapid demographic growth. We quantify the land cover dynamics along the urban–rural gradient of the Port-au-Prince agglomeration using Landsat images from 1986, [...] Read more.
The landscape of the Port-au-Prince agglomeration in the Republic of Haiti has undergone profound changes linked to (peri-)urban expansion supported by rapid demographic growth. We quantify the land cover dynamics along the urban–rural gradient of the Port-au-Prince agglomeration using Landsat images from 1986, 1998, 1999, 2010, and 2021 coupled with geographic information systems and landscape ecology analysis tools. The results show that over 35 years the acreage of the urban zone increased seven-fold while that of the peri-urban area increased five-fold, to the detriment of the rural zone, which was reduced by 14%. The dynamics of the landscape composition along the urban–rural gradient are characterized by a rapid progression of built-up and bare land in urban and peri-urban zones and by fields in the rural zone, in contrast to the more accentuated regression of vegetation in the peri-urban zone. The landscape of the study area has undergone significant changes due to the high demand for housing resulting from rapid population growth, in the context of a lack of territorial development planning by public authorities. This impacts the sustainability of socio-economic and ecological processes in an area where populations are highly dependent on plant resources. Our results underline the necessity to orient territorial development planning in urban, peri-urban and rural zones through an integrated and participatory approach. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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23 pages, 2924 KiB  
Article
Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations
by Gregoriy Kaplan, Lior Fine, Victor Lukyanov, V. S. Manivasagam, Josef Tanny and Offer Rozenstein
Land 2021, 10(7), 680; https://0-doi-org.brum.beds.ac.uk/10.3390/land10070680 - 28 Jun 2021
Cited by 22 | Viewed by 11074
Abstract
Public domain synthetic-aperture radar (SAR) imagery, particularly from Sentinel-1, has widened the scope of day and night vegetation monitoring, even when cloud cover limits optical Earth observation. Yet, it is challenging to combine SAR images acquired at different incidence angles and from ascending [...] Read more.
Public domain synthetic-aperture radar (SAR) imagery, particularly from Sentinel-1, has widened the scope of day and night vegetation monitoring, even when cloud cover limits optical Earth observation. Yet, it is challenging to combine SAR images acquired at different incidence angles and from ascending and descending orbits because of the backscatter dependence on the incidence angle. This study demonstrates two transformations that facilitate collective use of Sentinel-1 imagery, regardless of the acquisition geometry, for agricultural monitoring of several crops in Israel (wheat, processing tomatoes, and cotton). First, the radar backscattering coefficient (σ0) was multiplied by the local incidence angle (θ) of every pixel. This transformation improved the empirical prediction of the crop coefficient (Kc), leaf area index (LAI), and crop height in all three crops. The second method, which is based on the radar brightness coefficient (β0), proved useful for estimating Kc, LAI, and crop height in processing tomatoes and cotton. Following the suggested transformations, R2 increased by 0.0172 to 0.668, and RMSE improved by 5 to 52%. Additionally, the models based on the suggested transformations were found to be superior to the models based on the dual-polarization radar vegetation index (RVI). Consequently, vegetation monitoring using SAR imagery acquired at different viewing geometries became more effective. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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13 pages, 3983 KiB  
Article
Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2
by Gregoriy Kaplan and Offer Rozenstein
Land 2021, 10(5), 505; https://0-doi-org.brum.beds.ac.uk/10.3390/land10050505 - 09 May 2021
Cited by 14 | Viewed by 4743
Abstract
Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive [...] Read more.
Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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Review

Jump to: Research

30 pages, 2083 KiB  
Review
Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology
by Hua Shi, George Xian, Roger Auch, Kevin Gallo and Qiang Zhou
Land 2021, 10(8), 867; https://0-doi-org.brum.beds.ac.uk/10.3390/land10080867 - 18 Aug 2021
Cited by 19 | Viewed by 6534
Abstract
Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal [...] Read more.
Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery)
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