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Time Series Analysis in Remote Sensing: Algorithm Development and Applications

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 24422

Special Issue Editors

Center for Environmental Remote Sensing, Chiba University, Chiba 263-8522, Japan
Interests: quantitative remote sensing; algorithm development; environmental modeling; phenology
Special Issues, Collections and Topics in MDPI journals
Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: geophysical image processing; image classification; land cover; soil; remote sensing; vegetation
Special Issues, Collections and Topics in MDPI journals
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: agriculture; quantitative remote sensing; chlorophyll fluorescence; phenology
Special Issues, Collections and Topics in MDPI journals
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: algorithm development; time-series remote sensing; vegetation phenology
Special Issues, Collections and Topics in MDPI journals
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: remote sensing; spatial data analysis; data fusion; vegetation phenology
Special Issues, Collections and Topics in MDPI journals
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: plant phenology; climate change ecology; vegetation remote sensing; alpine ecosystem
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, planet Earth has been encountering extensive environmental changes caused either by the climate or human beings, such as the shrinking of inland lakes, the expansion of croplands, deforestation, desertification, and urbanization. Understanding these environmental changes, as well as ensuring that they are monitered in a timely fashion, is crucial for supporting our planet’s sustainable development.

Remote sensing provides us with a practical tool to monitor and quantify global changes. In particular, many archived satellite data have become freely available, which makes it possible to track the history of environmental changes in the long term. For example, the Landsat series data covering nearly the last 50 years have been made publicly accessible by the USA. MODIS also provides us daily observations of the globe from 2000 to now. Nevertheless, it is usually a difficult task to analyze a large amount of all available satellite data in terms of time-series observations. The analysis on time series data is much more challenging than just comparing several satellite imageries derived in different periods. Time series analysis usually needs to make computations on hundreds, or thousands, of remote sensing datasets, which must be calibrated, harmonized, filtered and/or interpolated to a frequent interval before mechanic analysis. Additionally, the satellite observation data are getting bigger and bigger every day, which poses an extra challenge to fully exploit the wealth of the latest information. Thanks to the rapid advancement of mathematic methods (e.g., machine learning, deep learning, and data assimilation) and cloud computation platforms (e.g., Google Earth Engine) in recent years, we have got some new opportunities to improve the analysis and applications of remote sensing time series data. However, there is still a clear need to share approaches and new ideas on time series analysis in remote sensing toward applications to all aspects of geosciences.

Consequently, a Special Issue entitled “Time Series Analysis in Remote Sensing: Algorithm Development and Applications” is being planned by the international journal, Remote Sensing, to address the technical challenges for time series analysis in remote sensing sciences and to demonstrate successful applications of remote sensing time series data in all aspects of geosciences.

We solicit your contributions in this field to our Remote Sensing Special Issue. Research or review articles with respect (but not necessarily restricted) to the following topics are welcome if remote sensing time series data are used: Data fusion, Classification algorithm, Machine learning, Filtering algorithm, Cloud computation, Cropland monitoring, Urbanization, Vegetation dynamics, Deforestation, Land surface phenology, Land use/cover mapping.

Dr. Wei Yang
Dr. Xuehong Chen
Dr. Cong Wang
Dr. Ruyin Cao
Dr. Xiaolin Zhu
Prof. Dr. Miaogen Shen
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

  • Data fusion, Classification algorithm
  • Machine learning
  • Filtering algorithm
  • Cloud computation
  • Cropland monitoring
  • Urbanization
  • Vegetation dynamics
  • Deforestation
  • Land surface phenology
  • Land use/cover mapping

Published Papers (6 papers)

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17 pages, 8052 KiB  
Article
Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series
Remote Sens. 2021, 13(20), 4160; https://doi.org/10.3390/rs13204160 - 17 Oct 2021
Cited by 16 | Viewed by 3195
Abstract
Crop rotations, the farming practice of growing crops in sequential seasons, occupy a core position in agriculture management, showing a key influence on food security and agro-ecosystem sustainability. Despite the improvement in accuracy of identifying mono-agricultural crop distribution, crop rotation patterns remain poorly [...] Read more.
Crop rotations, the farming practice of growing crops in sequential seasons, occupy a core position in agriculture management, showing a key influence on food security and agro-ecosystem sustainability. Despite the improvement in accuracy of identifying mono-agricultural crop distribution, crop rotation patterns remain poorly mapped. In this study, a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) architecture, namely crop rotation mapping (CRM), were proposed to synergize the synthetic aperture radar (SAR) and optical time series in a rotational mapping task. The proposed end-to-end architecture had reasonable accuracies (i.e., accuracy > 0.85) in mapping crop rotation, which outperformed other state-of-the-art non-deep or deep-learning solutions. For some confusing rotation types, such as fallow-single rice and crayfish-single rice, CRM showed substantial improvements from traditional methods. Furthermore, the deeply synergistic SAR-optical, time-series data, with a corresponding attention mechanism, were effective in extracting crop rotation features, with an overall gain of accuracy of four points compared with ablation models. Therefore, our proposed method added wisdom to dynamic crop rotation mapping and yields important information for the agro-ecosystem management of the study area. Full article
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22 pages, 7178 KiB  
Article
Investigating the Temporal and Spatial Dynamics of Human Development Index: A Comparative Study on Countries and Regions in the Eastern Hemisphere from the Perspective of Evolution
Remote Sens. 2021, 13(12), 2415; https://doi.org/10.3390/rs13122415 - 20 Jun 2021
Viewed by 3071
Abstract
The Human Development Index (HDI) is a prevailing indicator to present the status and trend of sustainability of nations, hereby offers a valuable measurement on the Sustainable Development Goals (SDGs). Revealing the dynamics of the HDI of the Eastern Hemisphere countries is vital [...] Read more.
The Human Development Index (HDI) is a prevailing indicator to present the status and trend of sustainability of nations, hereby offers a valuable measurement on the Sustainable Development Goals (SDGs). Revealing the dynamics of the HDI of the Eastern Hemisphere countries is vital for measurement and evaluation of the human development process and revealing the spatial disparities and evolutionary characteristics of human development. However, the statistical data-based HDI, which is currently widely applied, has defects in terms of data availability and inconsistent statistical caliber. To tackle such an existing gap, we applied nighttime lights (NTL) data to reconstruct new HDI indicators named HDINTL and quantify the HDINTL at multispatial scales of Eastern Hemisphere countries during 1992–2013. Results showed that South Central Asia countries had the smallest discrepancies in HDINTL, while the largest was found in North Africa. The national-level HDINTL values in the Eastern Hemisphere ranged between 0.138 and 0.947 during 1992–2013. At the subnational scale, the distribution pattern of HDINTL was spatially clustered based on the results of spatial autocorrelation analysis. The evolutionary trajectory of subnational level HDINTL exhibited a decreasing and then increasing trend along the northwest to the southeast direction of Eastern Hemisphere. At the pixel scale, 93.52% of the grids showed an increasing trend in HDINTL, especially in the urban agglomerations of China and India. These results are essential for the ever-improvement of policy making to reduce HDI’s regional disparity and promote the continuous development of humankind’s living qualities. This study offers an improved HDI accounting method. It expects to extend the channel of HDI application, e.g., potential integration with environmental, physical, and socioeconomic data where the NTL data could present as well. Full article
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22 pages, 4988 KiB  
Article
Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery
Remote Sens. 2021, 13(12), 2409; https://doi.org/10.3390/rs13122409 - 19 Jun 2021
Cited by 10 | Viewed by 2840
Abstract
The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for [...] Read more.
The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule. Full article
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20 pages, 7842 KiB  
Article
Adopting “Difference-in-Differences” Method to Monitor Crop Response to Agrometeorological Hazards with Satellite Data: A Case Study of Dry-Hot Wind
Remote Sens. 2021, 13(3), 482; https://doi.org/10.3390/rs13030482 - 29 Jan 2021
Cited by 11 | Viewed by 3992
Abstract
Rapid changing climate has increased the risk of natural hazards and threatened global and regional food security. Near real-time monitoring of crop response to agrometeorological hazards is fundamental to ensuring national and global food security. However, quantifying crop responses to a specific hazard [...] Read more.
Rapid changing climate has increased the risk of natural hazards and threatened global and regional food security. Near real-time monitoring of crop response to agrometeorological hazards is fundamental to ensuring national and global food security. However, quantifying crop responses to a specific hazard in the natural environment is still quite challenging, especially over large areas, due to the lack of tools to separate the independent impact of the hazard on crops from other confounding factors. In this study, we present a general difference-in-differences (DID) framework to monitor crop response to agrometeorological hazards at near real-time using widely accessible remotely sensed vegetation indices (VIs). To demonstrate the effectiveness of the DID framework, we applied it in quantifying the dry-hot wind impact on winter wheat in northern China as a case study using the VIs calculated from the MODIS data. The monitoring results for three years with varying severity levels of dry-hot events (i.e., 2007, 2013, and 2014) demonstrated that the framework can effectively detect winter wheat growing areas affected by dry-hot wind hazards. The estimated damage shows a notable relationship (R2 = 0.903, p < 0.001) with the dry-hot wind intensity calculated from meteorological data, suggesting the effectiveness of the method when field data on a large scale is not available for direct validation. The main advantage of this method is that it can effectively isolate the impact of a specific hazard (i.e., dry-hot wind in the case study) from the mixed signals caused by other confounding factors. This general DID framework is very flexible and can be easily extended to other natural hazards and crop types with proper adjustment. Not only can this framework improve the crop yield forecast but also it can provide near real-time assessment for farmers to adapt their farming practice to mitigate impacts of agricultural hazards. Full article
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19 pages, 7799 KiB  
Article
Remote Sensing Index for Mapping Canola Flowers Using MODIS Data
Remote Sens. 2020, 12(23), 3912; https://doi.org/10.3390/rs12233912 - 28 Nov 2020
Cited by 18 | Viewed by 3638
Abstract
Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important [...] Read more.
Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R2 ranged from 0.31 to 0.70, compared to the previous indices with R2 ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data. Full article
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15 pages, 4787 KiB  
Technical Note
BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis
Remote Sens. 2021, 13(16), 3308; https://doi.org/10.3390/rs13163308 - 21 Aug 2021
Cited by 23 | Viewed by 4953
Abstract
BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid [...] Read more.
BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas. Full article
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