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

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 7336

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 are 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, the 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 with topics including but not limited to those listed above.

Dr. Sara Venafra
Prof. Dr. Carmine Serio
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

Related Special Issue

Published Papers (3 papers)

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Research

14 pages, 3978 KiB  
Article
Rapid Land Cover Classification Using a 36-Year Time Series of Multi-Source Remote Sensing Data
by Xingguang Yan, Jing Li, Andrew R. Smith, Di Yang, Tianyue Ma and Yiting Su
Land 2023, 12(12), 2149; https://0-doi-org.brum.beds.ac.uk/10.3390/land12122149 - 11 Dec 2023
Cited by 2 | Viewed by 1851
Abstract
Long time series land cover classification information is the basis for scientific research on urban sprawls, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from [...] Read more.
Long time series land cover classification information is the basis for scientific research on urban sprawls, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the random forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of the sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of the sample points without land class change, determined by counting the sample points of each band of the Landsat time series from 1986 to 2022, was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of the TM and ETM+ sensor data from 2013 to 2022; and (iii) the addition of a mining land cover type increases the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and forest area. Among the land classifications via multi-source remote sensing, the combined variables of Spectral band + Index + Terrain + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and the use of sensors under complex terrain conditions. The use of the GEE cloud computing platform enabled the rapid analysis of remotely sensed data to produce land cover maps with high accuracy and a long time series. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery II)
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20 pages, 12254 KiB  
Article
Land Cover Mapping with Convolutional Neural Networks Using Sentinel-2 Images: Case Study of Rome
by Giulia Cecili, Paolo De Fioravante, Pasquale Dichicco, Luca Congedo, Marco Marchetti and Michele Munafò
Land 2023, 12(4), 879; https://0-doi-org.brum.beds.ac.uk/10.3390/land12040879 - 13 Apr 2023
Cited by 6 | Viewed by 2791
Abstract
Land cover monitoring is crucial to understand land transformations at a global, regional and local level, and the development of innovative methodologies is necessary in order to define appropriate policies and land management practices. Deep learning techniques have recently been demonstrated as a [...] Read more.
Land cover monitoring is crucial to understand land transformations at a global, regional and local level, and the development of innovative methodologies is necessary in order to define appropriate policies and land management practices. Deep learning techniques have recently been demonstrated as a useful method for land cover mapping through the classification of remote sensing imagery. This research aims to test and compare the predictive models created using the convolutional neural networks (CNNs) VGG16, DenseNet121 and ResNet50 on multitemporal and single-date Sentinel-2 satellite data. The most promising model was the VGG16 both with single-date and multi-temporal images, which reach an overall accuracy of 71% and which was used to produce an automatically generated EAGLE-compliant land cover map of Rome for 2019. The methodology is part of the land mapping activities of ISPRA and exploits its main products as input and support data. In this sense, it is a first attempt to develop a high-update-frequency land cover classification tool for dynamic areas to be integrated in the framework of the ISPRA monitoring activities for the Italian territory. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery II)
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21 pages, 8558 KiB  
Article
Drought Monitoring in Terms of Evapotranspiration Based on Satellite Data from Meteosat in Areas of Strong Land–Atmosphere Coupling
by Julia S. Stoyanova, Christo G. Georgiev and Plamen N. Neytchev
Land 2023, 12(1), 240; https://0-doi-org.brum.beds.ac.uk/10.3390/land12010240 - 12 Jan 2023
Cited by 2 | Viewed by 1698
Abstract
This study was focused on a key aspect of drought monitoring that has not been systematically studied in the literature: evaluation of the capacity of evapotranspiration data retrieved using geostationary meteorological satellites for use as a water stress precursor. The work was methodologically [...] Read more.
This study was focused on a key aspect of drought monitoring that has not been systematically studied in the literature: evaluation of the capacity of evapotranspiration data retrieved using geostationary meteorological satellites for use as a water stress precursor. The work was methodologically based on comparisons between constructed indexes of vegetation water stress (evapotranspiration drought index (ETDI) and evaporative stress ratio (ESR)) derived from the EUMETSAT LSASAF METREF and DMET satellite products and soil moisture availability (SMA) from a SVAT model. Long-term (2011–2021) data for regions with strong land–atmosphere coupling in Southeastern Europe (Bulgaria) were used. Stochastic graphical analysis and Q–Q (quantile–quantile) analyses were performed to compare water stress metrics and SMA. Analyses confirmed the consistency in the behavior of vegetation water-stress indexes and SMA in terms of their means, spatiotemporal variability at monthly and annual levels, and anomalous distributions. The biophysical aspects of the drought evaluation confirmed the complementary and parallel interaction of potential (METREF) and actual (DMET) evapotranspiration (in view of the Bouchet hypothesis) for the studied region. Anomalies in evapotranspiration stress indexes can provide useful early signals of agricultural/ecological drought, and the results confirm the validity of using their satellite-based versions to characterize SMA in the root zone and drought severity. Full article
(This article belongs to the Special Issue Land Surface Monitoring Based on Satellite Imagery II)
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