Recent Progress and Applications in Quantitative Remote Sensing

Topic Information

Dear Colleagues,

Hundreds of modern remote satellites—including optical, infrared, passive and active microwave remote satellites—have been launched over the past few years and more will be launched soon by various space agencies or commercial companies. With dedicated calibration efforts, a tremendous amount of high level 1 data has been provided for quantitative remote sensing applications. In recent years, the Committee on Earth Observations Satellites (CEOS) has defined Analysis-Ready Data (ARD) that allow immediate analysis with minimal additional user effort and interoperability both through time and with other datasets. Countries and international organizations have expressed their desire to facilitate access to and processing of satellite data into CEOS ARD products, which would bring us great convenience in quantitative remote sensing as we make more progress in remote sensing data processing and Earth surface parameter retrieval.

The aim of this Topic is to present the latest research in remote sensing theory, technology, and application. The scope includes, but is not limited to, the following topics:

1. Recent progress in sensor technology: techniques for new sensor design, calibration, and validation;
2. Quantitative remote sensing model development and inversion: recent progress in remote sensing models, inversion techniques, and Earth surface parameter retrieval;
3. Applications of remote sensing: quantitative remote sensing applications in agriculture, forestry, ecology and the environment, human activities, land systems, etc.

Researchers and practitioners in the field are encouraged to submit original research articles, reviews, and case studies. This topic aims to provide a comprehensive platform for disseminating new knowledge and fostering innovation in understanding and managing the progress and applications of quantitative remote sensing.

Prof. Dr. Huawei Wan
Prof. Dr. Yu Wang
Prof. Dr. Hongmin Zhou
Dr. Longhui Lu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Land land	3.2	4.9	2012	16.9 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (6 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Land Remote Sensing Sensors Applied Sciences

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

17 pages, 6551 KiB  

Open AccessArticle

Impact of Climate Change on Oriental Migratory Locust Suitability: A Multi-Source Data and MaxEnt-Based Analysis in Hainan Island

by Zhongxiang Sun, Huichun Ye, Weiping Kong, Chaojia Nie and Huiqing Bai

Remote Sens. 2025, 17(8), 1329; https://doi.org/10.3390/rs17081329 - 8 Apr 2025

Viewed by 267

Abstract

This study employed an integrated approach combining multi-source remote sensing data and the MaxEnt model to systematically assess the ecological niche characteristics of the oriental migratory locust (Locusta migratoria manilensis) in Hainan Island, while projecting the evolution of its suitable habitats [...] Read more.

This study employed an integrated approach combining multi-source remote sensing data and the MaxEnt model to systematically assess the ecological niche characteristics of the oriental migratory locust (Locusta migratoria manilensis) in Hainan Island, while projecting the evolution of its suitable habitats under both historical and future climate scenarios (up to 2040). Firstly, we synthesized traditional climate, soil, and topography data with remote sensing data to characterize the suitable areas of the oriental migratory locust based on MaxEnt model (with high accuracy of AUC = 0.935 and TSS = 0.76). Subsequently, six dominant environmental variables—precipitation in April (PRE04), precipitation in September (PRE09), maximum temperature in August (TMAX08), minimum temperature in December (TMIN12), NDVI in February (NDVI02), and NDVI in May (NDVI02)—were identified as key predictors. Their threshold values were determined, with PRE04, PRE09, TMAX08, and TMIN12 ranging from 39 to 44 mm, 196 to 223 mm, 31.1 to 32.2 °C, and 17.7 to 18.0 °C in high-suitability zones, respectively. Finally, these six predictors were used to assess habitat suitability across Hainan Island for both the 2001–2020 and 2021–2040 periods. Under historical climate conditions, highly suitable areas (505 km², 1.41% of total land area) were concentrated in the western and northeastern regions, particularly in Dongfang City (46.27%), Ledong Li Autonomous County (32.91%), and Changjiang Li Autonomous County (18.39%). Future projections indicate significant habitat expansion, with total suitable areas increasing by 13.4–42.0% and highly suitable areas reaching 571–831 km² by 2040. The study highlights the critical Dongfang–Danzhou–Ledong region for targeted locust control, providing scientific support for pest management in tropical island ecosystems under climate change. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

►▼ Show Figures

Figure 1

17 pages, 11246 KiB  

Open AccessArticle

Investigation of the Key Drivers of Vegetation Change Based on a Paired Land Use Experiment Approach—A Case Study of the Emin River Transboundary Basin

by Litian Zhu, Tiexi Chen, Xin Chen, Shuci Liu, Shengjie Zhou, Shengzhen Wang and Wenhui Li

Land 2025, 14(2), 437; https://doi.org/10.3390/land14020437 - 19 Feb 2025

Cited by 1 | Viewed by 513

Abstract

Remote sensing observations have shown an increasing trend in the vegetation leaf area index (LAI) over the past three decades, with climate change and human activities identified as the primary drivers of vegetation change. However, a challenge remains in identifying and quantifying the [...] Read more.

Remote sensing observations have shown an increasing trend in the vegetation leaf area index (LAI) over the past three decades, with climate change and human activities identified as the primary drivers of vegetation change. However, a challenge remains in identifying and quantifying the role of different drivers. In this study, we employed the paired land use experiment (PLUE) approach, which is based on the concept of natural comparative controlled experiments, to assess the impacts of human activities, especially land management, in the Emin River Basin within the border between China and Kazakhstan. The comparable climate, alongside the significant differences in human activities between the two sides of the Emin River, makes it ideal for applying the PLUE method. We found that during 2001 to 2022, both regions experienced similar inter-annual trends. The leaf area index (LAI) increased in both regions (Chinese region: 8.3 × 10⁻³ yr⁻¹m²m⁻²; Kazakhstan region: 5.8 × 10⁻⁴ yr⁻¹m²m⁻²), with the most significant increase observed in the Chinese cropland region (2.79 × 10⁻² yr⁻¹m²m⁻²). Through residual trend analysis, we found that the increase in the LAI from April to May in the Kazakhstan region was mainly positively influenced by human grazing activities. Comparatively, the LAI growth from June to August in the Chinese cropland region was mainly attributed to land managements. This study emphasizes the influence of human activities, especially land management, on vegetation and reveals the key factors affecting the LAI within different periods. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

►▼ Show Figures

Figure 1

18 pages, 7377 KiB  

Open AccessArticle

Long-Term Quantitative Analysis of the Temperature Vegetation Dryness Index to Assess Mining Impacts on Surface Soil Moisture: A Case Study of an Open-Pit Mine in Arid and Semiarid China

by Bin Liu, Xinhua Liu, Huawei Wan, Yan Ma and Longhui Lu

Appl. Sci. 2025, 15(4), 1850; https://doi.org/10.3390/app15041850 - 11 Feb 2025

Viewed by 558

Abstract

High-intensity coal mining significantly impacts the surrounding soil moisture (SM) through water seepage, artificial watering for dust suppression, and geomorphological changes, which will lead to ecological degradation. This study explores the impact of open-pit mines on surface SM in an arid–semiarid open-pit mine [...] Read more.

High-intensity coal mining significantly impacts the surrounding soil moisture (SM) through water seepage, artificial watering for dust suppression, and geomorphological changes, which will lead to ecological degradation. This study explores the impact of open-pit mines on surface SM in an arid–semiarid open-pit mine area of China over the period from 2000 to 2021. Using the temperature vegetation dryness index (TVDI), derived from the Land Surface Temperature–Normalized Difference Vegetation Index (LST-NDVI) feature space, this paper proposes a method—the TVDI of climate factor separation (TVDI-CFS)—to disentangle the influence of climate factors. The approach employs the Geographically and Temporally Weighted Regression (GTWR) model to isolate the influence of temperature and precipitation, allowing for a precise quantification of mining-induced disturbances. Additional techniques, such as buffer analysis and the Dynamic Time Warping (DTW) algorithm, are used to examine spatiotemporal variations and identify disturbance years. The results indicate that mining impacts on surface SM vary spatially, with disturbance distances of 420–660 m and strong distance decay patterns. Mining expansion has increased disturbance ranges and intensified cumulative effects. Inter-annual TVDI trends from 2015 to 2021 reveal clustered disturbances in alignment with mining directions, with the largest affected area in 2016. These findings provide a systematic valuable insights for ecological restoration and sustainable environmental management in mining-affected areas. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

►▼ Show Figures

Figure 1

15 pages, 2654 KiB  

Open AccessTechnical Note

Analysis of Roadside Land Use Changes and Landscape Ecological Risk Assessment Based on GF-1: A Case Study of the Linghua Expressway

by Mengdi Wen, Liangliang Zhang, Huawei Wan, Peirong Shi, Longhui Lu, Zixin Zhao, Zhiru Zhang and Jinhui Wu

Remote Sens. 2025, 17(2), 211; https://doi.org/10.3390/rs17020211 - 8 Jan 2025

Cited by 1 | Viewed by 979

Abstract

The rapid construction of expressways in China has brought significant economic and social benefits, but it has also imposed substantial ecological pressures, particularly in sensitive regions. Landscape ecological risk assessment, as an important means to predict and measure the adverse effects of human [...] Read more.

The rapid construction of expressways in China has brought significant economic and social benefits, but it has also imposed substantial ecological pressures, particularly in sensitive regions. Landscape ecological risk assessment, as an important means to predict and measure the adverse effects of human activities on the ecological environment, is being paid more and more attention. However, most studies focus on the static landscape mosaic pattern and lack dynamic analysis. Moreover, they mainly focus on the ecological effect of the road operation stage, ignoring the monitoring and analysis of the whole construction process. Based on this, the current study examines the landscape ecological risk and land use changes along the Linghua Expressway in Gansu Province using high-resolution GF-1 remote sensing imagery. A landscape ecological risk assessment (LERA) model was employed to quantify the land use changes and assess the ecological risks before and after the expressway construction between 2018 and 2022. The results revealed a decrease in cropland and forest land, accompanied by an increase in the grassland and road areas. The landscape ecological risk index decreased from 0.318 in 2018 to 0.174 in 2022, indicating an improvement in ecological resilience. However, high-risk zones remain near the expressway, emphasizing the need for continuous monitoring and proactive ecological management strategies. These findings contribute to sustainable infrastructure planning, particularly in ecologically sensitive regions. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

►▼ Show Figures

Figure 1

20 pages, 12596 KiB  

Open AccessArticle

Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model

by Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie and Shaofang He

Appl. Sci. 2024, 14(24), 11687; https://doi.org/10.3390/app142411687 - 14 Dec 2024

Cited by 3 | Viewed by 1855

Abstract

Accurate prediction of soil properties is essential for sustainable land management and precision agriculture. This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. Utilizing the LUCAS soil dataset, the model analyzes spectral [...] Read more.

Accurate prediction of soil properties is essential for sustainable land management and precision agriculture. This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. Utilizing the LUCAS soil dataset, the model analyzes spectral data to estimate key soil properties, including organic carbon (OC), nitrogen (N), calcium carbonate (CaCO₃), and pH (in H₂O). The Long Short-Term Memory (LSTM) component captures temporal dependencies, the Convolutional Neural Network (CNN) extracts spatial features, and the attention mechanism highlights critical information within the data. Experimental results show that the proposed model achieves excellent prediction performance, with coefficient of determination (R²) values of 0.949 (OC), 0.916 (N), 0.943 (CaCO₃), and 0.926 (pH), along with corresponding ratio of percent deviation (RPD) values of 3.940, 3.737, 5.377, and 3.352. Both R² and RPD values exceed those of traditional machine learning models, such as partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), as well as deep learning models like CNN-LSTM and Gated Recurrent Unit (GRU). Additionally, the proposed model outperforms S-AlexNet in effectively capturing temporal and spatial patterns. These findings emphasize the potential of the proposed model to significantly enhance the accuracy and reliability of soil property predictions by capturing both temporal and spatial patterns effectively. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

►▼ Show Figures

Figure 1

19 pages, 23579 KiB  

Open AccessArticle

Assessment of the Impact of Road Construction on the Ecological Environment

by Ziyu Wang, Hongmin Zhou, Huawei Wan, Peirong Shi, Chen Li, Jinlin Qi and Ruojing Fang

Remote Sens. 2024, 16(23), 4478; https://doi.org/10.3390/rs16234478 - 28 Nov 2024

Cited by 2 | Viewed by 2030

Abstract

In recent years, China has made remarkable progress in infrastructure construction, which has greatly contributed to the development of the regional economy. However, the impacts of construction on the ecological environment are of increasing concern. This study aimed to quantitatively assess the ecological [...] Read more.

In recent years, China has made remarkable progress in infrastructure construction, which has greatly contributed to the development of the regional economy. However, the impacts of construction on the ecological environment are of increasing concern. This study aimed to quantitatively assess the ecological environment of two expressways (the Chanliu Expressway and the Linghua Expressway) constructed during different time periods, to assess the impact of road construction on the ecosystem and the effectiveness of the Chinese government’s efforts in environmental protection. The pressure–state–response (PSR) model was adopted, which integrates a variety of remote sensing indicators. The ecological pressure, ecological state, and ecological response in the pre-, mid-, and post-construction periods of the road were assessed. The results reveal that the impacts of the construction of the Chanliu (1999–2002) and Linghua Expressways (2019–2023) on ecosystems are different. For the Chanliu Expressway, the ecological pressure continually increased, and the ecological state significantly declined during the construction period. When the road construction was finished, the environment continuously deteriorated. This was due to the lack of effective ecological protective measures during its construction. In contrast, the Linghua Expressway experienced reduced ecological pressure during the construction period, with the ecological state remaining relatively stable, as more protective measures were implemented. However, it later relied on natural recovery, which led to an increase in ecological pressure in the post-construction period. The results indicate that China’s ecological protective measures in road construction have achieved significant progress in recent years. In the future, it is essential to maintain long-term ecological health by strengthening ecological restoration management and continuous environmental monitoring. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-6