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Special Issue "Advances in Detecting and Understanding Land Surface Phenology"

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2187

Special Issue Editors

Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
Interests: remote sensing; phenology; radiometers; time series; vegetation

Special Issue Information

Dear Colleagues,

Land surface phenology (LSP) quantifies the seasonal dynamics of vegetated land surfaces in satellite pixels using remote sensing data. As phenological variations have strong impacts on ecosystems (e.g., productivity, carbon and water cycles, and interactions among species) and human health (e.g., allergenic pollen exposure), LSP has been largely investigated at local to global scales in recent decades. Unlike ground-observed species-specific phenology, LSP varies with both climate changes (e.g., temperature, precipitation, and photoperiod) and land surface changes caused by natural disturbances (e.g., wildfires, weather extremes, and species invasion) and human activities (urbanization, deforestation, and crop rotation). Moreover, the scaling effect arising from the heterogeneity of the landscape impedes cross-scale matching and ground-based validation of LSP. Recently, the development of new satellite sensors (e.g., Sentinel, PlanetScope), data fusion techniques, and LSP retrieval algorithms has advanced LSP detection at finer resolutions and improved the understanding of the scaling effect and LSP dynamics corresponding to both climate changes and land surface changes across ecosystems. In addition, efforts have been made to apply LSP to map vegetation (e.g., crop) types and to monitor LSP in near-real time which could support agriculture management such as irrigation and fertilization schedules and yield prediction. Thus, this Special Issue aims to collect studies that address the latest developments in the detection, understanding, and application of LSP. Specifically, we are inviting submissions on topics including, but not limited to:

  • New algorithms and remote sensors for LSP detection;
  • Multi-sensor data fusion techniques for LSP detection;
  • LSP dynamics responding to climate and land surface changes;
  • Spatial patterns and drivers of LSP variations across spatial scales;
  • Ground-based validation and cross-scale comparisons of LSP;
  • Near-real-time monitoring of LSP and its applications (e.g., agriculture and forestry management).

Dr. Jianmin Wang
Dr. Xiaoyang Zhang
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

  • land surface phenology
  • climate change
  • land use and land cover change
  • scaling effect
  • data fusion
  • near-real-time monitoring

Published Papers (2 papers)

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Article
Variations in Phenology Identification Strategies across the Mongolian Plateau Using Multiple Data Sources and Methods
Remote Sens. 2023, 15(17), 4237; https://doi.org/10.3390/rs15174237 - 29 Aug 2023
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Abstract
Satellite data and algorithms directly affect the accuracy of phenological estimation; therefore, it is necessary to compare and verify existing phenological models to identify the optimal combination of data and algorithms across the Mongolian Plateau (MP). This study used five phenology fitting algorithms—double [...] Read more.
Satellite data and algorithms directly affect the accuracy of phenological estimation; therefore, it is necessary to compare and verify existing phenological models to identify the optimal combination of data and algorithms across the Mongolian Plateau (MP). This study used five phenology fitting algorithms—double logistic (DL) and polynomial fitting (Poly) combined with the dynamic threshold method at thresholds of 35% and 50% (DL-G35, DL-G50, Poly-G35, and Poly-G50) and DL combined with the cumulative curvature extreme value method (DL-CUM)—and two data types—the enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF)—to identify the start (SOS), peak (POS), and end (EOS) of the growing season in alpine meadow (ALM), desert steppe (DRS), forest vegetation (FV), meadow grassland (MEG), and typical grassland (TYG) of the MP. The optimal methods for identifying the SOS, POS, and EOS of typical grassland areas were Poly-G50 (NSE = 0.12, Pbias = 0.22%), DL-G35/50 (NSE = −0.01, Pbias = −0.06%), and Poly-G35 (NSE = 0.02, Pbias = 0.08%), respectively, based on SIF data. The best methods for identifying the SOS, POS, and EOS of desert steppe areas were Poly-G35 (NSE = −0.27, Pbias = −1.49%), Poly-G35/50 (NSE = −0.58, Pbias = −1.39%), and Poly-G35 (NSE = 0.29, Pbias = −0.61%), respectively, based on EVI data. The data source explained most of the differences in phenological estimates. The accuracy of polynomial fitting was significantly greater than that of the DL method, while all methods were better at identifying SOS and POS than they were at identifying EOS. Our findings can help to facilitate the establishment of a phenological estimation system suitable for the Mongolian Plateau and improve the observation methods of vegetation phenology. Full article
(This article belongs to the Special Issue Advances in Detecting and Understanding Land Surface Phenology)
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Technical Note
Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data
Remote Sens. 2022, 14(16), 4027; https://doi.org/10.3390/rs14164027 - 18 Aug 2022
Viewed by 1133
Abstract
Vegetation phenology not only describes the life cycle events of periodic plants during the growing season but also acts as an indicator of biological responses to climate change. Satellite monitoring of vegetation phenology can capture the spatial patterns of vegetation dynamics at global [...] Read more.
Vegetation phenology not only describes the life cycle events of periodic plants during the growing season but also acts as an indicator of biological responses to climate change. Satellite monitoring of vegetation phenology can capture the spatial patterns of vegetation dynamics at global scales. However, the existing satellite products of global vegetation phenology still show uncertainties in estimating phenological metrices, especially for dormancy onset. The Second-Generation Global Imager (SGLI) onboard the satellite Global Change Observation Mission—Climate (GCOM-C) that launched in 2017 provides a new opportunity to improve the estimation of global vegetation phenology with a spatial resolution of 250 m. In this study, SGLI land surface reflectance data were employed to estimate the green-up and dormancy dates for different vegetation types based on a relative threshold method, in which a snow-free vegetation index (i.e., the normalized difference greenness index, NDGI) was adopted. The validation results show that there are significant agreements between the trajectories of the SGLI-based NDGI and the near-surface green color coordinate index (GCC) at the PhenoCam sites with different vegetation types. The SGLI-based estimation of the green-up dates slightly outperformed that of the existing MODIS and VIIRS phenology products, with an RMSE and R2 of 11.0 days and 0.71, respectively. In contrast, the estimation of the dormancy dates based on the SGLI data yielded much higher accuracies than the MODIS and VIIRS products, with an RMSE decreased from >23.8 days to 15.6 days, and R2 increased from <0.51 to 0.72. These results suggest that GCOM-C/SGLI data have the potential to generate improved monitoring of global vegetation phenology in the future. Full article
(This article belongs to the Special Issue Advances in Detecting and Understanding Land Surface Phenology)
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