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Remote Sensing of Land Surface Phenology
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Remote Sensing of Land Surface Phenology

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 40309

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Xuanlong Ma

E-Mail Website
Guest Editor
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Interests: remote sensing of vegetation; phenology; biodiversity; global change
Dr. Jiaxin Jin

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China
Interests: vegetation phenology; climate change; carbon–water coupling
Dr. Xiaolin Zhu

E-Mail Website
Guest Editor
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
Dr. Yuke Zhou

E-Mail Website
Guest Editor
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing; land surface phenology and climate feedback; vegetation productivity; carbon cycle; ecology of global change
Dr. Qiaoyun Xie

E-Mail Website
Guest Editor
Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: vegetation monitoring; ecological forecasting; vegetation parameter retrieval; vegetation phenology; climate variability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land surface phenology (LSP) involves the use of remote sensing to monitor seasonal dynamics in vegetated land surfaces and to retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP, as an important field in environmental and climate remote sensing science, has undergone rapid development over the last few decades. Both regional and global LSP products have been routinely generated and played prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing global change impacts on urban and natural ecosystems.

Recent advances in field and spaceborne sensor technologies as well as data fusion techniques have enabled novel LSP retrieval algorithms that refine LSP retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals are undergoing, and efforts in seeking ways to reduce these uncertainties are also forming an active research field. In addition, open source software and hardware are being developed and have greatly facilitated the use of LSP metrics by scientists beyond the remote sensing community. As such, we organized this Special Issue to cover the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. In doing so, we hope to not only summarize the ongoing diverse LSP developments but also boost discussions on future prospects in LSP research. We welcome contributions that fall within, but are not limited to, the following topics:

  • Advances in LSP retrieval algorithms
  • Assessing and reducing the uncertainties in LSP retrievals
  • Retrieving LSP using optical, microwave, LiDAR, and SIF
  • Ensuring long-term continuity of LSP across sensors and satellite missions
  • Applying multisensor data fusion techniques for LSP
  • Proposing improved satellite LSP validation strategies using ground observations
  • Near-real-time monitoring and short-term forecasting of LSP
  • Retrieving LSP from nanosat constellation and geostationary satellites
  • Developing LSP products using cloud platforms such as GEE and PIE-Engine
  • Developing phenocams (phenology cameras) for LSP applications
  • Developing open source computer code, software, and hardware for LSP
  • Prototyping and upgrading regional, national, and global LSP products
  • Applying LSP in agriculture, ecology, LULC, and global change
  • Tracking the long-term trends and IAV of LSP and its interaction with regional climate
  • Exploring the interactions between LSP and climate/human activities factors

Thank you and we look forward to receiving your contributions!

Dr. Xuanlong Ma
Dr. Jiaxin Jin
Dr. Xiaolin Zhu
Dr. Yuke Zhou
Dr. Qiaoyun Xie
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
  • Vegetation dynamics
  • Global change
  • Land use/land cover
  • Multisensor integration
  • Geostationary satellite
  • Micro/nanosatellite constellation
  • Phenology validation
  • Unmanned aerial vehicles (UAVs)
  • Phenology cameras and citizen science
  • Optical, microwave, chlorophyll fluorescence
  • Big data and cloud computation
  • Open source computer code, software, and hardware
  • Ecological surveillance and forecasting

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Published Papers (16 papers)

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Editorial

Jump to: Research

5 pages, 940 KiB  
Editorial
Remote Sensing of Land Surface Phenology: Editorial
by Xuanlong Ma, Jiaxin Jin, Xiaolin Zhu, Yuke Zhou and Qiaoyun Xie
Remote Sens. 2022, 14(17), 4310; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174310 - 01 Sep 2022
Viewed by 1418
Abstract
Land surface phenology (LSP) is an important research field in terrestrial remote sensing and has become an indispensable approach in global change research, as evidenced by many important scientific findings supported by LSP in recent decades [...] Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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Research

Jump to: Editorial

15 pages, 4567 KiB  
Article
Seasonal Ecosystem Productivity in a Seasonally Dry Tropical Forest (Caatinga) Using Flux Tower Measurements and Remote Sensing Data
by Gabriel Brito Costa, Keila Rêgo Mendes, Losany Branches Viana, Gabriele Vieira Almeida, Pedro Rodrigues Mutti, Cláudio Moisés Santos e Silva, Bergson Guedes Bezerra, Thiago Valentim Marques, Rosária Rodrigues Ferreira, Cristiano Prestelo Oliveira, Weber Andrade Gonçalves, Pablo Eli Oliveira, Suany Campos, Maria Uilhiana Gomes Andrade, Antônio Celso Dantas Antonino and Rômulo Simões Cézar Menezes
Remote Sens. 2022, 14(16), 3955; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14163955 - 15 Aug 2022
Cited by 16 | Viewed by 2302
Abstract
The Caatinga dry forest encompasses 11% of the total continental territory of Brazil. Nevertheless, most research on the relationship between phenology and ecosystem productivity of Brazilian tropical forests is aimed at the Amazon basin. Thus, in this study we evaluated the seasonality of [...] Read more.
The Caatinga dry forest encompasses 11% of the total continental territory of Brazil. Nevertheless, most research on the relationship between phenology and ecosystem productivity of Brazilian tropical forests is aimed at the Amazon basin. Thus, in this study we evaluated the seasonality of ecosystem productivity (gross primary production—GPP) in a preserved Caatinga environment in northeast Brazil. Analyses were carried out using eddy covariance measurements and satellite-derived data from sensor MODIS (MODerate Resolution Imaging Spectroradiometer, MOD17 and MOD13 products). In addition to GPP, we investigated water use efficiency (WUE) and meteorological and phenological aspects through remotely sensed vegetation indices (NDVI and EVI). We verified that ecosystem productivity is limited mainly by evapotranspiration, with maximum GPP values registered in the wetter months, indicating a strong dependency on water availability. NDVI and EVI were positively associated with GPP (r = 0.69 and 0.81, respectively), suggesting a coupling between the emergence of new leaves and the phenology of local photosynthetic capacity. WUE, on the other hand, was strongly controlled by consecutive dry days and not necessarily by total precipitation amount. The vegetation indices adequately described interannual variations of the forest response to environmental factors, and GPP MODIS presented a good relationship with tower-measured GPP in dry (R2 = 0.76) and wet (R2 = 0.62) periods. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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18 pages, 7687 KiB  
Article
Influences of Seasonal Soil Moisture and Temperature on Vegetation Phenology in the Qilian Mountains
by Xia Cui, Gang Xu, Xiaofei He and Danqi Luo
Remote Sens. 2022, 14(15), 3645; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153645 - 29 Jul 2022
Cited by 10 | Viewed by 2083
Abstract
Vegetation phenology is a commonly used indicator of ecosystem responses to climate change and plays a vital role in ecosystem carbon and hydrological cycles. Previous studies have mostly focused on the response of vegetation phenology to temperature and precipitation. Soil moisture plays an [...] Read more.
Vegetation phenology is a commonly used indicator of ecosystem responses to climate change and plays a vital role in ecosystem carbon and hydrological cycles. Previous studies have mostly focused on the response of vegetation phenology to temperature and precipitation. Soil moisture plays an important role in maintaining vegetation growth. However, our understanding of the influences of soil moisture dynamics on vegetation phenology is sparse. In this study, using a time series of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (MODIS) dataset (2001–2020), the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS) in the Qilian Mountains (QLMs) were extracted. The spatiotemporal patterns of vegetation phenology (SOS, EOS, and LOS) were explored. The partial coefficient correlations between the SOS, EOS, and seasonal climatic factors (temperature, precipitation, and soil moisture) were analyzed. The results showed that the variation trends of vegetation phenology were not significant (p > 0.05) from 2001 to 2020, the SOS was advanced by 0.510 d/year, the EOS was delayed by 0.066 d/year, and the LOS was prolonged by 0.580 d/year. The EOS was significantly advanced and the LOS significantly shortened with increasing altitude. The seasonal temperature, precipitation, and soil moisture had spatiotemporal heterogeneous effects on the vegetation phenology. Overall, compared with temperature and soil moisture, precipitation had a weaker influence on the vegetation phenology in the QLMs. For different elevation zones, the temperature and soil moisture influenced the vegetation phenology in most areas of the QLMs, and spring temperature was the key driving factor influencing SOS; the autumn soil moisture and autumn temperature made the largest contributions to the variations in EOS at lower (<3500 m a.s.l.) and higher elevations (>3500 m a.s.l.), respectively. For different vegetation types, the spring temperature was the main factor influencing the SOS for broadleaf forests, needleleaf forests, shrublands, and meadows because of the relative lower soil moisture stress. The autumn soil moisture was the main factor influencing EOS for deserts because of the strong soil moisture stress. Our results demonstrate that the soil moisture strongly influences vegetation phenology, especially at lower elevations and water-limited areas. This study provides a scientific basis for better understanding the response of vegetation phenology to climate change in arid mountainous areas and suggests that the variation in soil moisture should be considered in future studies on the influence of climate warming and environmental effects on the phenology of water-limited areas. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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18 pages, 5012 KiB  
Article
Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types
by Yantao Liu, Wei Zhou, Si Gao, Xuanlong Ma and Kai Yan
Remote Sens. 2022, 14(15), 3629; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153629 - 29 Jul 2022
Cited by 3 | Viewed by 2078
Abstract
In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 [...] Read more.
In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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19 pages, 5604 KiB  
Article
WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
by Gabriel B. Costa, Cláudio M. Santos e Silva, Keila R. Mendes, José G. M. dos Santos, Theomar T. A. T. Neves, Alex S. Silva, Thiago R. Rodrigues, Jonh B. Silva, Higo J. Dalmagro, Pedro R. Mutti, Hildo G. G. C. Nunes, Lucas V. Peres, Raoni A. S. Santana, Losany B. Viana, Gabriele V. Almeida, Bergson G. Bezerra, Thiago V. Marques, Rosaria R. Ferreira, Cristiano P. Oliveira, Weber A. Gonçalves, Suany Campos and Maria U. G. Andradeadd Show full author list remove Hide full author list
Remote Sens. 2022, 14(14), 3241; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143241 - 06 Jul 2022
Cited by 8 | Viewed by 3294
Abstract
The analysis of gross primary production (GPP) is crucial to better understand CO2 exchanges between terrestrial ecosystems and the atmosphere, while the quantification of water-use efficiency (WUE) allows for the estimation of the compensation between carbon gained and water lost by the [...] Read more.
The analysis of gross primary production (GPP) is crucial to better understand CO2 exchanges between terrestrial ecosystems and the atmosphere, while the quantification of water-use efficiency (WUE) allows for the estimation of the compensation between carbon gained and water lost by the ecosystem. Understanding these dynamics is essential to better comprehend the responses of environments to ongoing climatic changes. The objective of the present study was to analyze, through AMERIFLUX and LBA network measurements, the variability of GPP and WUE in four distinct tropical biomes in Brazil: Pantanal, Amazonia, Caatinga and Cerrado (savanna). Furthermore, data measured by eddy covariance systems were used to assess remotely sensed GPP products (MOD17). We found a distinct seasonality of meteorological variables and energy fluxes with different latent heat controls regarding available energy in each site. Remotely sensed GPP was satisfactorily related with observed data, despite weak correlations in interannual estimates and consistent overestimations and underestimations during certain months. WUE was strongly dependent on water availability, with values of 0.95 gC kg−1 H2O (5.79 gC kg−1 H2O) in the wetter (drier) sites. These values reveal new thresholds that had not been previously reported in the literature. Our findings have crucial implications for ecosystem management and the design of climate policies regarding the conservation of tropical biomes, since WUE is expected to change in the ongoing climate change scenario that indicates an increase in frequency and severity of dry periods. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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11 pages, 3636 KiB  
Communication
Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations
by Cong Wang, Yijin Wu, Qiong Hu, Jie Hu, Yunping Chen, Shangrong Lin and Qiaoyun Xie
Remote Sens. 2022, 14(13), 3018; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133018 - 23 Jun 2022
Cited by 11 | Viewed by 2738
Abstract
Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF [...] Read more.
Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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16 pages, 2879 KiB  
Article
Remote Sensing Phenology of the Brazilian Caatinga and Its Environmental Drivers
by Rodolpho Medeiros, João Andrade, Desirée Ramos, Magna Moura, Aldrin Martin Pérez-Marin, Carlos A. C. dos Santos, Bernardo Barbosa da Silva and John Cunha
Remote Sens. 2022, 14(11), 2637; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112637 - 31 May 2022
Cited by 13 | Viewed by 2558
Abstract
The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants’ phenology. Thus, it [...] Read more.
The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants’ phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson’s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner–Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem’s phenology and the influence on the Caatinga’s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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19 pages, 63236 KiB  
Article
Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China
by Hui Guo, Xiaoyan Wang, Zecheng Guo and Siyong Chen
Remote Sens. 2022, 14(2), 262; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020262 - 07 Jan 2022
Cited by 12 | Viewed by 2045
Abstract
Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in [...] Read more.
Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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15 pages, 11564 KiB  
Article
Detecting the Turning Points of Grassland Autumn Phenology on the Qinghai-Tibetan Plateau: Spatial Heterogeneity and Controls
by Yanzheng Yang, Ning Qi, Jun Zhao, Nan Meng, Zijian Lu, Xuezhi Wang, Le Kang, Boheng Wang, Ruonan Li, Jinfeng Ma and Hua Zheng
Remote Sens. 2021, 13(23), 4797; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234797 - 26 Nov 2021
Cited by 8 | Viewed by 1719
Abstract
Autumn phenology, commonly represented by the end of season (EOS), is considered to be the most sensitive and crucial productivity indicator of alpine and cold grassland in the Qinghai-Tibetan Plateau. Previous studies typically assumed that the rates of EOS changes remain unchanged over [...] Read more.
Autumn phenology, commonly represented by the end of season (EOS), is considered to be the most sensitive and crucial productivity indicator of alpine and cold grassland in the Qinghai-Tibetan Plateau. Previous studies typically assumed that the rates of EOS changes remain unchanged over long time periods. However, pixel-scale analysis indicates the existence of turning points and differing EOS change rates before and after these points. The spatial heterogeneity and controls of these turning points remain unclear. In this study, the EOS turning point changes are extracted and their controls are explored by integrating long time-series remote sensing images and piecewise regression methods. The results indicate that the EOS changed over time with a delay rate of 0.08 days/year during 1982–2015. The rates of change are not consistent over different time periods, which clearly highlights the existence of turning points. The results show that temperature contributed most strongly to the EOS changes, followed by precipitation and insolation. Furthermore, the turning points of climate, human activities (e.g., grazing, economic development), and their intersections are found to jointly control the EOS turning points. This study is the first quantitative investigation into the spatial heterogeneity and controls of the EOS turning points on the Qinghai-Tibetan Plateau, and provides important insight into the growth mechanism of alpine and cold grassland. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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19 pages, 11062 KiB  
Article
Exploring the Applicability and Scaling Effects of Satellite-Observed Spring and Autumn Phenology in Complex Terrain Regions Using Four Different Spatial Resolution Products
by Fangxin Chen, Zhengjia Liu, Huimin Zhong and Sisi Wang
Remote Sens. 2021, 13(22), 4582; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224582 - 15 Nov 2021
Cited by 8 | Viewed by 1817
Abstract
The information on land surface phenology (LSP) was extracted from remote sensing data in many studies. However, few studies have evaluated the impacts of satellite products with different spatial resolutions on LSP extraction over regions with a heterogeneous topography. To bridge this knowledge [...] Read more.
The information on land surface phenology (LSP) was extracted from remote sensing data in many studies. However, few studies have evaluated the impacts of satellite products with different spatial resolutions on LSP extraction over regions with a heterogeneous topography. To bridge this knowledge gap, this study took the Loess Plateau as an example region and employed four types of satellite data with different spatial resolutions (250, 500, and 1000 m MODIS NDVI during the period 2001–2020 and ~10 km GIMMS3g during the period 1982–2015) to investigate the LSP changes that took place. We used the correlation coefficient (r) and root mean square error (RMSE) to evaluate the performances of various satellite products and further analyzed the applicability of the four satellite products. Our results showed that the MODIS-based start of the growing season (SOS) and end of the growing season (EOS) were highly correlated with the ground-observed data with r values of 0.82 and 0.79, respectively (p < 0.01), while the GIMMS3g-based phenology signal performed badly (r < 0.50 and p > 0.05). Spatially, the LSP that was derived from the MODIS products produced more reasonable spatial distributions. The inter-annual averaged MODIS SOS and EOS presented overall advanced and delayed trends during the period 2001–2020, respectively. More than two-thirds of the SOS advances and EOS delays occurred in grasslands, which determined the overall phenological changes across the entire Loess Plateau. However, both inter-annual trends of SOS and EOS derived from the GIMMS3g data were opposite to those seen in the MODIS results. There were no significant differences among the three MODIS datasets (250, 500, and 1000 m) with regard to a bias lower than 2 days, RMSE lower than 1 day, and correlation coefficient greater than 0.95 (p < 0.01). Furthermore, it was found that the phenology that was derived from the data with a 1000 m spatial resolution in the heterogeneous topography regions was feasible. Yet, in forest ecosystems and areas with an accumulated temperature ≥10 °C, the differences in phenological phase between the MODIS products could be amplified. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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21 pages, 45827 KiB  
Article
Specific Drivers and Responses to Land Surface Phenology of Different Vegetation Types in the Qinling Mountains, Central China
by Jiaqi Guo, Xiaohong Liu, Wensen Ge, Xiaofeng Ni, Wenyuan Ma, Qiangqiang Lu and Xiaoyu Xing
Remote Sens. 2021, 13(22), 4538; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224538 - 11 Nov 2021
Cited by 6 | Viewed by 2044
Abstract
Land surface phenology (LSP), as a precise bio-indicator that responds to climate change, has received much attention in fields concerned with climate change and ecology. Yet, the dynamics of LSP changes in the Qinling Mountains (QMs)—A transition zone between warm-temperate and north subtropical [...] Read more.
Land surface phenology (LSP), as a precise bio-indicator that responds to climate change, has received much attention in fields concerned with climate change and ecology. Yet, the dynamics of LSP changes in the Qinling Mountains (QMs)—A transition zone between warm-temperate and north subtropical climates with complex vegetation structure—under significant climatic environmental evolution are unclear. Here, we analyzed the spatiotemporal dynamics of LSP for different vegetation types in the QMs from 2001 to 2019 and quantified the degree of influence of meteorological factors (temperature, precipitation, and shortwave radiation), and soil (temperature and moisture), and biological factors (maximum of NDVI and middle date during the growing season) on LSP changes using random forest models. The results show that there is an advanced trend (0.15 days/year) for the start of the growing season (SOS), a delayed trend (0.24 days/year) for the end of the growing season (EOS), and an overall extended trend (0.39 days/year) for the length of the growing season (LOS) in the QMs over the past two decades. Advanced SOS and delayed EOS were the dominant patterns leading to a lengthened vegetation growing season, followed by a joint delay of SOS and EOS, and the latter was particularly common in shrub and evergreen broadleaved forests. The growth season length increased significantly in western QMs. Furthermore, we confirmed that meteorological factors are the main factors affecting the interannual variations in SOS and EOS, especially the meteorological factor of preseason mean shortwave radiation (SWP). The grass and crop are most influenced by SWP. The soil condition has, overall, a minor influence the regional LSP. This study highlighted the specificity of different vegetation growth in the QMs under warming, which should be considered in the accurate prediction of vegetation growth in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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19 pages, 15096 KiB  
Article
Quantification of Urban Heat Island-Induced Contribution to Advance in Spring Phenology: A Case Study in Hangzhou, China
by Yingying Ji, Jiaxin Jin, Wenfeng Zhan, Fengsheng Guo and Tao Yan
Remote Sens. 2021, 13(18), 3684; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183684 - 15 Sep 2021
Cited by 10 | Viewed by 2367
Abstract
Plant phenology is one of the key regulators of ecosystem processes, which are sensitive to environmental change. The acceleration of urbanization in recent years has produced substantial impacts on vegetation phenology over urban areas, such as the local warming induced by the urban [...] Read more.
Plant phenology is one of the key regulators of ecosystem processes, which are sensitive to environmental change. The acceleration of urbanization in recent years has produced substantial impacts on vegetation phenology over urban areas, such as the local warming induced by the urban heat island effect. However, quantitative contributions of the difference of land surface temperature (LST) between urban and rural (ΔLST) and other factors to the difference of spring phenology (i.e., the start of growing season, SOS) between urban and rural (ΔSOS) were rarely reported. Therefore, the objective of this study is to explore impacts of urbanization on SOS and distinguish corresponding contributions. Using Hangzhou, a typical subtropical metropolis, as the study area, vegetation index-based phenology data (MCD12Q2 and MYD13Q1 EVI) and land surface temperature data (MYD11A2 LST) from 2006–2018 were adopted to analyze the urban–rural gradient in phenology characteristics through buffers. Furthermore, we exploratively quantified the contributions of the ΔLST to the ΔSOS based on a temperature contribution separation model. We found that there was a negative coupling between SOS and LST in over 90% of the vegetated areas in Hangzhou. At the sample-point scale, SOS was weakly, but significantly, negatively correlated with LST at the daytime (R2 = 0.2 and p < 0.01 in rural; R2 = 0.14 and p < 0.05 in urban) rather than that at nighttime. Besides, the ΔSOS dominated by the ΔLST contributed more than 70% of the total ΔSOS. We hope this study could help to deepen the understanding of responses of urban ecosystem to intensive human activities. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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19 pages, 6572 KiB  
Article
Remote Sensing-Based Quantification of the Summer Maize Yield Gap Induced by Suboptimum Sowing Dates over North China Plain
by Sha Zhang, Yun Bai and Jiahua Zhang
Remote Sens. 2021, 13(18), 3582; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183582 - 08 Sep 2021
Cited by 7 | Viewed by 2057
Abstract
Estimating yield potential (Yp) and quantifying the contribution of suboptimum field managements to the yield gap (Yg) of crops are important for improving crop yield effectively. However, achieving this goal on a regional scale remains difficult because of challenges in collecting field management [...] Read more.
Estimating yield potential (Yp) and quantifying the contribution of suboptimum field managements to the yield gap (Yg) of crops are important for improving crop yield effectively. However, achieving this goal on a regional scale remains difficult because of challenges in collecting field management information. In this study, we retrieved crop management information (i.e., emerging stage information and a surrogate of sowing date (SDT)) from a remote sensing (RS) vegetation index time series. Then, we developed a new approach to quantify maize Yp, total Yg, and the suboptimum SDT-induced Yg (Yg0) using a process-based RS-driven crop yield model for maize (PRYM–Maize), which was developed in our previous study. PRYM–Maize and the newly developed method were used over the North China Plain (NCP) to estimate Ya, Yp, Yg, and Yg0 of summer maize. Results showed that PRYM–Maize outputs reasonable estimates for maize yield over the NCP, with correlations and root mean standard deviation of 0.49 ± 0.24 and 0.88 ± 0.14 t hm−2, respectively, for modeled annual maize yields versus the reference value for each year over the period 2010 to 2015 on a city level. Yp estimated using our new method can reasonably capture the spatial variations in site-level estimates from crop growth models in previous literature. The mean annual regional Yp of 2010–2015 was estimated to be 11.99 t hm−2, and a Yg value of 5.4 t hm−2 was found between Yp and Ya on a regional scale. An estimated 29–42% of regional Yg in each year (2010–2015) was induced by suboptimum SDT. Results also show that not all Yg0 was persistent over time. Future studies using high spatial-resolution RS images to disaggregate Yg0 into persistent and non-persistent components on a small scale are required to increase maize yield over the NCP. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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21 pages, 12023 KiB  
Article
Spatial, Phenological, and Inter-Annual Variations of Gross Primary Productivity in the Arctic from 2001 to 2019
by Dujuan Ma, Xiaodan Wu, Xuanlong Ma, Jingping Wang, Xingwen Lin and Cuicui Mu
Remote Sens. 2021, 13(15), 2875; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152875 - 22 Jul 2021
Cited by 3 | Viewed by 2920
Abstract
Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MOD17A2H GPP product [...] Read more.
Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MOD17A2H GPP product using the FLUXNET 2015 dataset in the Arctic, then explored the spatial patterns, seasonal variations, and interannual trends of GPP, and investigated the dependence of the spatiotemporal variations in GPP on land cover types, latitude, and elevation from 2001 to 2019. The results showed that MOD17A2H was consistent with in situ measurements (R = 0.8, RMSE = 1.26 g C m−2 d−1). The functional phenology was also captured by the MOD17A2H product (R = 0.62, RMSE = 9 days) in the Arctic. The spatial variation of the seasonal magnitude of GPP and its interannual trends is partly related to land cover types, peaking in forests and lowest in grasslands. The interannual trend of GPP decreased as the latitude and elevation increased, except for the latitude between 62°~66° N and elevation below 700 m. Our study not only revealed the variation of GPP in the Arctic but also helped to understand the carbon cycle over this region. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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17 pages, 5111 KiB  
Article
Dynamics and Drivers of Vegetation Phenology in Three-River Headwaters Region Based on the Google Earth Engine
by Jiyan Wang, Huaizhang Sun, Junnan Xiong, Dong He, Weiming Cheng, Chongchong Ye, Zhiwei Yong and Xianglin Huang
Remote Sens. 2021, 13(13), 2528; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132528 - 28 Jun 2021
Cited by 18 | Viewed by 3357
Abstract
Phenology shifts over time are known as the canary in the mine when studying the response of terrestrial ecosystems to climate change. Plant phenology is a key factor controlling the productivity of terrestrial vegetation under climate change. Over the past several decades, the [...] Read more.
Phenology shifts over time are known as the canary in the mine when studying the response of terrestrial ecosystems to climate change. Plant phenology is a key factor controlling the productivity of terrestrial vegetation under climate change. Over the past several decades, the vegetation in the three-river headwaters region (TRHR) has been reported to have changed greatly owing to the warming climate and human activities. However, uncertainties related to the potential mechanism and influence of climatic and soil factors on the plant phenology of the TRHR are poorly understood. In this study, we used harmonic analysis of time series and the relative and absolute change rate on Google Earth Engine to calculate the start (SOS), end (EOS), and length (LOS) of the growing season based on MOD09A1 datasets; the results were verified by the observational data from phenological stations. Then, the spatiotemporal patterns of plant phenology for different types of terrain and basins were explored. Finally, the potential mechanism involved in the influence of climatic and soil factors on the phenology of plants in the TRHR were explored based on the structural equation model and Pearson’s correlation coefficients. The results show the remotely sensed monitoring data of SOS (R2 = 0.84, p < 0.01), EOS (R2 = 0.72, p < 0.01), and LOS (R2 = 0.86, p < 0.01) were very similar to the observational data from phenological stations. The SOS and LOS of plants possessed significant trends toward becoming advanced (Slope < 0) and extended (Slope > 0), respectively, from 2001 to 2018. The SOS was the earliest and the LOS was the longest in the Lancang River Basin, while the EOS was the latest in the Yangtze River Basin owing to the impact of climate change and soil factors. Meanwhile, the spatial patterns of SOS, EOS, and LOS have strong spatial heterogeneity at different elevations, slopes, and aspects. In addition, the results show that the drivers of plant phenology have basin-wide and stage differences. Specifically, the influence of soil factors on plant phenology in the Yangtze River Basin was greater than that of climatic factors, but climatic factors were key functional indicators of LOS in the Yellow and Lancang river basins, which directly or indirectly affect plant LOS through soil factors. This study will be helpful for understanding the relationship between the plant phenology of the alpine wetland ecosystem and climate change and improving the level of environmental management. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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20 pages, 2117 KiB  
Article
Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization
by A Reum Kim, Chi Hong Lim, Bong Soon Lim, Jaewon Seol and Chang Seok Lee
Remote Sens. 2021, 13(10), 1890; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101890 - 12 May 2021
Cited by 2 | Viewed by 2126
Abstract
Urbanization and the resulting increase in development areas and populations cause micro-climate changes such as the urban heat island (UHI) effect. This micro-climate change can affect vegetation phenology. It can advance leaf unfolding and flowering and delay the timing of fallen leaves. This [...] Read more.
Urbanization and the resulting increase in development areas and populations cause micro-climate changes such as the urban heat island (UHI) effect. This micro-climate change can affect vegetation phenology. It can advance leaf unfolding and flowering and delay the timing of fallen leaves. This study was carried out to clarify the impact of urbanization on the leaf unfolding of Mongolian oak. The survey sites for this study were established in the urban center (Mts. Nam, Mido, and Umyeon in Seoul), suburbs (Mts. Cheonggye and Buram in Seoul), a rural area (Gwangneung, Mt. Sori in Gyeonggi-do), and a natural area (Mt. Jeombong in Gangwon-do). Green-up dates derived from the analyses of digital camera images and MODIS satellite images were the earliest in the urban center and delayed through the suburbs and rural area to the natural area. The difference in the observed green-up date compared to the expected one, which was determined by regarding the Mt. Jeombong site located in the natural area as the reference site, was the biggest in the urban center and decreased through the suburbs and rural area to the natural area. Green-up dates in the rural area, suburbs, and urban center were earlier by 11.0, 14.5, and 16.3 days than the expected ones. If these results are transformed into the air temperature based on previous research results, it could be deduced that the air temperature in the urban center, suburbs, and rural area rose by 3.8 to 4.6 °C, 3.3 to 4.1 °C, and 2.5 to 3.1 °C, respectively. Green-up dates derived based on the accumulated growing degree days (AGDD) showed the same trend as those derived from the image interpretation. Green-up dates derived from the change in sap flow as a physiological response of the plant showed a difference within one day from the green-up dates derived from digital camera and MODIS satellite image analyses. The change trajectory of the curvature K value derived from the sap flow also showed a very similar trend to that of the curvature K value derived from the vegetation phenology. From these results, we confirm the availability of AGDD and sap flow as tools predicting changes in ecosystems due to climate change including phenology. Meanwhile, the green-up dates in survey sites were advanced in proportion to the land use intensity of each survey site. Green-up dates derived based on AGDD were also negatively correlated with the land use intensity of the survey site. This result implies that differences in green-up dates among the survey sites and between the expected and observed green-up dates in the urban center, suburbs, and rural area were due to the increased temperature due to land use in the survey sites. Based on these results, we propose conservation and restoration of nature as measures to reduce the impact of climate change. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology)
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