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Advances in Remote Sensors for Earth Observation
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Advances in Remote Sensors for Earth Observation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (21 April 2022) | Viewed by 11037

Special Issue Editor

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

Special Issue Information

Dear Colleagues,

This is an invitation to contribute to a Special Issue aiming to provide a snapshot of new horizons in Earth observations introduced simultaneously by advances in new physical measurements and by the rapid miniaturization of established sensor systems. The efforts of 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, including hyperspectral imagers. In addition, the feasibility of on-board data processing has been demonstrated even on nano-satellites. This leads to instrument design and information extraction based on increasingly focused and solid physics underpinning narrowly defined measurement concepts. In addition, technological evolution has led to a larger and diverse community of players in sensor development and exploitation. Such developments are evident in Earth observation from space- and airborne platforms and a host of mobile systems. Both Earth system science and the sustainable use of natural resources are benefiting from such advances.

We aim at publishing manuscripts of high quality, both scientific and review contributions, that demonstrate the advancement of remote sensing technology and successfully present new science and application areas. We are particularly interested in studies on new measurement concepts and on the demonstration of miniaturized instruments and platforms, possibly in combination with an evaluation of the impacts on current Earth observation science and applications. Likewise, the combination of on-board data processing and cloud computing to offer information services to end-users, reducing the reliance on centralized data centers, are highly relevant.

Dr. Massimo Menenti
Guest Editor

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. Sensors 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 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

  • multispectral, hyperspectral, and active radar and LiDAR sensors
  • space-borne, air-borne, and UAV platforms
  • proximal sensing and robotic manned and unmanned systems
  • water resources
  • extreme events
  • cryospheric processes
  • land cover dynamics
  • ecosystem functioning
  • air quality

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Published Papers (3 papers)

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Research

16 pages, 6244 KiB  
Article
R-YOLO: A YOLO-Based Method for Arbitrary-Oriented Target Detection in High-Resolution Remote Sensing Images
by Yongjie Hou, Gang Shi, Yingxiang Zhao, Fan Wang, Xian Jiang, Rujun Zhuang, Yunfei Mei and Xinjiang Ma
Sensors 2022, 22(15), 5716; https://doi.org/10.3390/s22155716 - 30 Jul 2022
Cited by 24 | Viewed by 3682
Abstract
In view of the existence of remote sensing images with large variations in spatial resolution, small and dense objects, and the inability to determine the direction of motion, all these components make object detection from remote sensing images very challenging. In this paper, [...] Read more.
In view of the existence of remote sensing images with large variations in spatial resolution, small and dense objects, and the inability to determine the direction of motion, all these components make object detection from remote sensing images very challenging. In this paper, we propose a single-stage detection network based on YOLOv5. This method introduces the MS Transformer module at the end of the feature extraction network of the original network to enhance the feature extraction capability of the network model and integrates the Convolutional Block Attention Model (CBAM) to find the attention area in dense scenes. In addition, the YOLOv5 target detection network is improved by incorporating a rotation angle approach from the a priori frame design and the bounding box regression formulation to make it suitable for rotating frame-based detection scenarios. Finally, the weighted combination of the two difficult sample mining methods is used to improve the focal loss function, so as to improve the detection accuracy. The average accuracy of the test results of the improved algorithm on the DOTA data set is 77.01%, which is higher than the previous detection algorithm. Compared with the average detection accuracy of YOLOv5, the average detection accuracy is improved by 8.83%. The experimental results show that the algorithm has higher detection accuracy than other algorithms in remote sensing scenes. Full article
(This article belongs to the Special Issue Advances in Remote Sensors for Earth Observation)
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19 pages, 17998 KiB  
Article
Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine
by Fernando Pech-May, Raúl Aquino-Santos, German Rios-Toledo and Juan Pablo Francisco Posadas-Durán
Sensors 2022, 22(13), 4729; https://doi.org/10.3390/s22134729 - 23 Jun 2022
Cited by 15 | Viewed by 4141
Abstract
Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction [...] Read more.
Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops. Full article
(This article belongs to the Special Issue Advances in Remote Sensors for Earth Observation)
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16 pages, 2567 KiB  
Article
PROSPECT-PMP+: Simultaneous Retrievals of Chlorophyll a and b, Carotenoids and Anthocyanins in the Leaf Optical Properties Model
by Yao Zhang, Xinkai Li, Chengjie Wang, Rongxu Zhang, Lisong Jin, Zongtai He, Shoupeng Tian, Kaihua Wu and Fumin Wang
Sensors 2022, 22(8), 3025; https://doi.org/10.3390/s22083025 - 14 Apr 2022
Cited by 4 | Viewed by 2296
Abstract
The PROSPECT leaf optical radiative transfer models, including PROSPECT-MP, have addressed the contributions of multiple photosynthetic pigments (chlorophyll a and b, and carotenoids) to leaf optical properties, but photo-protective pigment (anthocyanins), another important indicator of vegetation physiological and ecological functions, has not been [...] Read more.
The PROSPECT leaf optical radiative transfer models, including PROSPECT-MP, have addressed the contributions of multiple photosynthetic pigments (chlorophyll a and b, and carotenoids) to leaf optical properties, but photo-protective pigment (anthocyanins), another important indicator of vegetation physiological and ecological functions, has not been simultaneously combined within a leaf optical model. Here, we present a new calibration and validation of PROSPECT-MP+ that separates the contributions of multiple photosynthetic and photo-protective pigments to leaf spectrum in the 400–800 nm range using a new empirical dataset that contains multiple photosynthetic and photo-protective pigments (LOPEX_ZJU dataset). We first provide multiple distinct in vivo individual photosynthetic and photo-protective pigment absorption coefficients and leaf average refractive index of the leaf interior using the LOPEX_ZJU dataset. Then, we evaluate the capabilities of PROSPECT-MP+ for forward modelling of leaf directional hemispherical reflectance and transmittance spectra and for retrieval of pigment concentrations by model inversion. The main result of this study is that the absorption coefficients of chlorophyll a and b, carotenoids, and anthocyanins display the physical principles of absorption spectra. Moreover, the validation result of this study demonstrates the potential of PROSPECT-MP+ for improving capabilities in remote sensing of leaf photosynthetic pigments (chlorophyll a and b, and carotenoids) and photo-protective pigment (anthocyanins). Full article
(This article belongs to the Special Issue Advances in Remote Sensors for Earth Observation)
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