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New Advancements in the Field of Remote Sensing in Land Surface Processes

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 7579

Special Issue Editors

Prof. Dr. Massimo Menenti

E-Mail Website
Guest Editor
1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 Delft, The Netherlands
Interests: land surface processes; terrestrial water cycle; water management; optical remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jiyue Zhu

E-Mail Website
Co-Guest Editor
Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48109, USA
Interests: electromagnetics; random media; remote sensing; snow

Special Issue Information

Dear Colleagues,

Decades of remote sensing technology have transformed our understanding of the universe. We are now better able to observe, map, and model earth processes in order to understand not only the dynamics of these processes, but also to understand the earth–atmosphere interactions which they relate to. Natural resources mapping, simulation of water, energy and carbon fluxes, groundwater dynamics, soil moisture and precipitation prediction, natural hazards modelling, and many other areas of application have emerged because of the advancements in remote sensing. We can now acquire images that have much better spatiotemporal resolution, which can be interpreted faster and more efficiently.

This Special Issue aims at providing a snapshot of new horizons in earth observations, which have been opened by simultaneous advances in new physical measurements and by the rapid miniaturization of established sensor systems. Efforts by the science and engineering community have paved the way for fundamentally new measurements, such as Doppler lidars and fluorescence radiometry, and for far-reaching miniaturization of complex instruments, e.g., hyperspectral imagers. In addition, the feasibility of on-board data processing has been demonstrated even on nano-satellites. Such developments are observable in earth observation from space- and airborne platforms and a host of mobile systems. Both earth system science and sustainable use of natural resources are benefiting from such advances.

Land surface processes are complex processes that occur at the interface between the land and the atmosphere, which determine global and local climates at different scales. For example, urban surface energy balance determines the urban climate, while glaciers surface energy balance determines changes in glacier mass and the water balance at a regional scale. The applications of remote sensing in land surface processes have grown rapidly in different research fields dealing with, e.g., the cryosphere, forests, agriculture, and urban areas. However, no systematic exploration has been attempted of the specific advantages and synergies of the latest generation of earth observation systems to study land surface processes.

This Special Issue invites contributions describing applications of new remote sensing technologies to observe and model land surface processes. In particular, but not exclusively, manuscripts are encouraged addressing the following topics:

  • Land surface temperature retrieval and surface flux parameterization based on remote sensing of complex heterogeneous surfaces, e.g., urban areas and 3D vegetation canopies;
  • LiDAR to map land surface properties, such as aerodynamic roughness;
  • Remote sensing of the terrestrial carbon cycle in different biomes;
  • Remote sensing of glaciers and high-elevation water cycles;
  • Assimilation of remote sensing data in numerical models of land surface process.

Prof. Dr. Massimo Menenti
Dr. Jiyue Zhu
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

  • multispectral, hyperspectral, and LiDAR sensors
  • active and passive microwave sensors
  • space-borne, airborne, and UAV platforms
  • proximal sensing
  • robotic manned and unmanned systems
  • data processing techniques
  • big data
  • decision support system
  • AI
  • machine learning
  • downscaling and upscaling techniques

Published Papers (5 papers)

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Research

18 pages, 12517 KiB  
Article
Robust Cloud Suppression and Anomaly Detection in Time-Lapse Thermography
by Christopher Small and Daniel Sousa
Remote Sens. 2024, 16(2), 255; https://doi.org/10.3390/rs16020255 - 09 Jan 2024
Cited by 1 | Viewed by 843
Abstract
Due to their transient nature, clouds represent anomalies relative to the underlying landscape of interest. Hence, the challenge of cloud identification can be considered a specific case in the more general problem of anomaly detection. The confounding effects of transient anomalies are particularly [...] Read more.
Due to their transient nature, clouds represent anomalies relative to the underlying landscape of interest. Hence, the challenge of cloud identification can be considered a specific case in the more general problem of anomaly detection. The confounding effects of transient anomalies are particularly troublesome for spatiotemporal analysis of land surface processes. While spatiotemporal characterization provides a statistical basis to quantify the most significant temporal patterns and their spatial distributions without the need for a priori assumptions about the observed changes, the presence of transient anomalies can obscure the statistical properties of the spatiotemporal processes of interest. The objective of this study is to implement and evaluate a robust approach to distinguish clouds and other transient anomalies from diurnal and annual thermal cycles observed with time-lapse thermography. The approach uses Robust Principal Component Analysis (RPCA) to statistically distinguish low-rank (L) and sparse (S) components of the land surface temperature image time series, followed by a spatiotemporal characterization of its low rank component to quantify the dominant diurnal and annual thermal cycles in the study area. RPCA effectively segregates clouds, sensor anomalies, swath gaps, geospatial displacements and transient thermal anomalies into the sparse component time series. Spatiotemporal characterization of the low-rank component time series clearly resolves a variety of diurnal and annual thermal cycles for different land covers and water bodies while segregating transient anomalies potentially of interest. Full article
(This article belongs to the Special Issue New Advancements in the Field of Remote Sensing in Land Surface Processes)
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20 pages, 2724 KiB  
Article
Global Analysis of the Cover-Management Factor for Soil Erosion Modeling
by Muqi Xiong, Guoyong Leng and Qiuhong Tang
Remote Sens. 2023, 15(11), 2868; https://doi.org/10.3390/rs15112868 - 31 May 2023
Cited by 1 | Viewed by 1567
Abstract
Land use and management practices (LUMPs) play a critical role in regulating soil loss. The cover-management factor (C-factor) in Universal Soil Loss Equation (USLE)-type models is an important parameter for quantifying the effects of LUMPs on soil erosion. However, accurately determining the C-factor, [...] Read more.
Land use and management practices (LUMPs) play a critical role in regulating soil loss. The cover-management factor (C-factor) in Universal Soil Loss Equation (USLE)-type models is an important parameter for quantifying the effects of LUMPs on soil erosion. However, accurately determining the C-factor, particularly for large-scale assessments using USLE-type models, remains challenging. This study aims to address this gap by analyzing and comparing the methods used for C-factor quantification in 946 published articles, providing insights into their strengths and weaknesses. Through our analysis, we identified six main categories of methods for C-factor quantification in USLE-type modeling. Many studies have relied on empirical C-factor values for different land-use types or calculated C-factor values based on vegetation indices (VIs) in large study areas (>100 km2). However, we found that no single method could robustly estimate C-factor values for large-scale studies. For small-scale investigations, conducting experiments or consulting the existing literature proved to be more feasible. In the context of large-scale studies, employing methods based on VIs for C-factor quantification can enhance our understanding of the relationship between vegetation changes and soil erosion potential, particularly when considering spatial and spatiotemporal variations. For the global scale, we recommend the combined use of different equations. We suggest further efforts to develop C-factor datasets at large scales by synthesizing field-level experiment data and combining high-resolution satellite imagery. These efforts will facilitate the development of effective soil conservation practices, ensuring sustainable land use and environmental protection. Full article
(This article belongs to the Special Issue New Advancements in the Field of Remote Sensing in Land Surface Processes)
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21 pages, 10031 KiB  
Article
Applicability Assessment of Passive Microwave LST Downscaling over Semi–Homogeneous Desert Underlying Surface Based on Machine Learning
by Yongkang Li, Yongqiang Liu, Wenjiang Huang, Yang Yan, Jiao Tan and Qing He
Remote Sens. 2023, 15(10), 2626; https://doi.org/10.3390/rs15102626 - 18 May 2023
Viewed by 1011
Abstract
The spatial and temporal resolution of remote sensing products in land surface temperature (LST) studies can be improved using the downscaling method. This is a crucial area of research as it provides basic data for the study of climate change. However, there have [...] Read more.
The spatial and temporal resolution of remote sensing products in land surface temperature (LST) studies can be improved using the downscaling method. This is a crucial area of research as it provides basic data for the study of climate change. However, there have been few studies evaluating the applicability of downscaling methods using underlying surfaces of varying complexities. In this study, we focused on the semi–homogeneous underlying surface of Gurbantunggut Desert and evaluated the applicability of five classical, passive microwave, downscaling methods based on the machine learning of Catboost, using 365 days of AMSR–2 and MODIS data in 2019, which can be scanned once during the day and night. Our results showed four main points: (1) The correlation coefficients between feature vectors and the LST of the semi–homogeneous underlying surface were clearly different from those of the surrounding oases. The correlation coefficient of the semi–homogeneous underlying surface was high, and that of the surrounding oases was low. (2) At the same frequency, the correlation coefficient between vertically polarized BT and LST was greater than that between horizontally polarized BT and LST. Considering the semi–heterogeneous underlying surface, 23.8 GHz and 36.5 GHz may be more suitable for passive microwave LST retrieval than 89 GHz according to physical mechanisms. (3) The fine–scale LST downscaling accuracy achieved with all BT channels of AMSR–2 was higher than that achieved with the other four classical models. The day and night RMSE values verified with MYD11A1 data were 2.82 K and 1.38 K, respectively. (4) The correlation coefficients between downscaled LST and the soil temperature of the top layer of the site were the highest, with daytime–nighttime R2 values of 0.978 and 0.970, and RMSE values of 3.42 and 4.99 K, respectively. The all–channel–based LST downscaling method is very effective and can provide a theoretical foundation for the acquisition of all–weather, multi–layer soil temperature. Full article
(This article belongs to the Special Issue New Advancements in the Field of Remote Sensing in Land Surface Processes)
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18 pages, 3597 KiB  
Article
Topological Generality and Spectral Dimensionality in the Earth Mineral Dust Source Investigation (EMIT) Using Joint Characterization and the Spectral Mixture Residual
by Daniel Sousa and Christopher Small
Remote Sens. 2023, 15(9), 2295; https://doi.org/10.3390/rs15092295 - 27 Apr 2023
Viewed by 1301
Abstract
NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) mission seeks to use spaceborne imaging spectroscopy (hyperspectral imaging) to map the mineralogy of arid dust source regions. Here we apply recent developments in Joint Characterization (JC) and the spectral Mixture Residual (MR) to explore [...] Read more.
NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) mission seeks to use spaceborne imaging spectroscopy (hyperspectral imaging) to map the mineralogy of arid dust source regions. Here we apply recent developments in Joint Characterization (JC) and the spectral Mixture Residual (MR) to explore the information content of data from this novel mission. Specifically, for a mosaic of 20 spectrally diverse scenes, we find: (1) a generalized three-endmember (Substrate, Vegetation, Dark; SVD) spectral mixture model is capable of capturing the preponderance (99% in three dimensions) of spectral variance with low misfit (99% pixels with <3.7% RMSE); (2) manifold learning (UMAP) is capable of identifying spatially coherent, physically interpretable clustering relationships in the spectral feature space; (3) UMAP yields results that are at least as informative when applied to the MR as when applied to raw reflectance; (4) SVD fraction information usefully contextualizes UMAP clustering relationships, and vice-versa (JC); and (5) when EMIT data are convolved to spectral response functions of multispectral instruments (Sentinel-2, Landsat 8/9, Planet SuperDove), SVD fractions correlate strongly across sensors, but UMAP clustering relationships for the EMIT hyperspectral feature space are far more informative than for simulated multispectral sensors. Implications are discussed for both the utility of EMIT data in the near-term and for the potential of high signal-to-noise (SNR) spaceborne imaging spectroscopy more generally, to transform the future of optical remote sensing in the years and decades to come. Full article
(This article belongs to the Special Issue New Advancements in the Field of Remote Sensing in Land Surface Processes)
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15 pages, 10035 KiB  
Article
Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network
by Yulin Cai, Puran Fan, Sen Lang, Mengyao Li, Yasir Muhammad and Aixia Liu
Remote Sens. 2022, 14(22), 5681; https://doi.org/10.3390/rs14225681 - 10 Nov 2022
Cited by 3 | Viewed by 2048
Abstract
The spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM product. First, the factors affecting soil [...] Read more.
The spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM product. First, the factors affecting soil surface moisture were analyzed, and the significantly correlated ones were selected as predictors for the downscaling model. Second, a DBN model was trained and used to downscale the 9 km SMAP L4 SM to 1 km in the study area on 25 September 2019. Validation was performed using original SMAP L4 SM data and in situ measurements from SM and temperature wireless sensor network with 34 sites. Finally, the DBN method was compared with another commonly used machine learning model-random forest (RF). Results showed that (1) the downscaled 1 km SM data are in good agreement with the original SMAP L4 SM data and field measured data, and (2) DBN has a higher correlation coefficient and a lower root mean square error than those of RF. The coefficients of determination for fitting the two models with the measured data at the site were 0.5260 and 0.4816, with relative mean square errors of 0.0303 and 0.0342 m3/m3, respectively. The study also demonstrated the applicability of the DBN method to AMSR SM data downscaling besides SMAP. The proposed method can provide a framework to support future hydrological modeling, regional drought monitoring, and agricultural research. Full article
(This article belongs to the Special Issue New Advancements in the Field of Remote Sensing in Land Surface Processes)
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