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

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 11076

Special Issue Editors

Dr. Bernard Lacaze

E-Mail Website
Guest Editor
PRODIG, UMR 8586 CNRS, Bâtiment Olympe de Gouges, Place Paul Ricoeur, 75013 Paris, France
Interests: land use/land cover monitoring; land degradation and desertification; vegetation ecology; ecosystem functioning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicolas Delbart

E-Mail Website
Guest Editor
Laboratoire Interdisciplinaire des Energies de Demain (LIED), Université Paris Diderot - Paris 7, Case 7001, CEDEX 13, 75205 Paris, France
Interests: vegetation mapping; phenology; ecosystem functioning; environmental monitoring

Special Issue Information

Dear Colleagues,

Land surface phenology (LSP) involves the use of multitemporal remote sensing data to monitor seasonal and interannual dynamics in vegetated land surfaces, to retrieve phenological metrics (start/end/duration of growing season, annual integrals, multi-year trend in primary production, etc.), and to provide bio-indicators of ongoing climate change. Traditional plant phenology provides very accurate information on individual plant species, but has limited spatial coverage. Remote sensing is especially well-suited for use in the monitoring of vegetation phenology at the local to global scales due to its ability to make continuous observations over a long period of time in different and complementary portions of the electromagnetic spectrum. First LSP studies started after the launch of ERTS-1 (Landsat-1) satellite in 1972, illustrating the possible use of space-borne greenness proxies to monitor vegetation phenology at regional scales. LSP, as an important field in environmental and climate remote sensing science, has undergone rapid development over the last few decades. Recent advances in field and spaceborne sensor technologies as well as data fusion techniques have enabled the development of novel LSP retrieval algorithms that refine LSP retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. A first LSP Special Issue including 16 papers  was published in September 2022. We have organized this new Special Issue is organized to cover the latest developments in LSP research, especially in the following domains: improving LSP retrievals using recent advances in sensor performances and multi-sensor approaches (data fusion); assessing and reducing the uncertainties in LSP retrievals, comparisons of algorithms and development of a versatile more generalized algorithm; proposing improved satellite LSP validation strategies using ground observations, UAV imagery and phenocams; near-real-time monitoring of LSP and its applications in agriculture and forestry; tracking the long-term trends of LSP and its interaction with regional climate; and exploring the interactions between LSP and human activities factors. We look forward to receiving your contributions!

Dr. Bernard Lacaze
Prof. Dr. Nicolas Delbart
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
  • climate change
  • optical, microwave, chlorophyll fluorescence
  • multisensor integration
  • geostationary satellite
  • micro/nanosatellite constellation
  • unmanned aerial vehicles (UAVs)
  • phenology cameras and citizen science
  • open source computer code, software, hardware
  • ecological surveillance and forecasting
  • near-real-time monitoring

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Related Special Issue

Published Papers (7 papers)

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Research

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28 pages, 7193 KiB  
Article
Country-Scale Crop-Specific Phenology from Disaggregated PROBA-V
by Henry Rivas, Nicolas Delbart, Fabienne Maignan, Emmanuelle Vaudour and Catherine Ottlé
Remote Sens. 2024, 16(23), 4521; https://doi.org/10.3390/rs16234521 - 2 Dec 2024
Viewed by 759
Abstract
Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail [...] Read more.
Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail for smaller plots. This study generated crop-specific phenomaps for mainland France (2016–2020) using PROBA-V data. A spatial disaggregation method reconstructed NDVI time series for individual crops within mixed pixels. Then, phenometrics were extracted from disaggregated PROBA-V and Sentinel-2 separately and compared to observed phenological stages. Results showed that PROBA-V-based phenomaps closely matched observations at regional level, with moderate accuracy at municipal level. PROBA-V demonstrated a higher detection rate than Sentinel-2, especially in cloudy periods, and successfully generated phenomaps before Sentinel-2B’s launch. The study highlights PROBA-V’s potential for operational crop monitoring, i.e., wheat heading and oilseed rape flowering, with performance comparable to Sentinel-2. PROBA-V outputs complement Sentinel-2: phenometrics cannot be generated at plot level but are efficiently produced at regional or national scales to study phenological gradients more easily than with Sentinel-2 and with similar accuracy. This approach could be extended to MODIS or SPOT-VGT, to generate historical phenological data, providing that a crop map is available. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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32 pages, 28061 KiB  
Article
Linking Vegetation Phenology to Net Ecosystem Productivity: Climate Change Impacts in the Northern Hemisphere Using Satellite Data
by Hanmin Yin, Xiaofei Ma, Xiaohan Liao, Huping Ye, Wentao Yu, Yue Li, Junbo Wei, Jincheng Yuan and Qiang Liu
Remote Sens. 2024, 16(21), 4101; https://doi.org/10.3390/rs16214101 - 2 Nov 2024
Cited by 1 | Viewed by 1714
Abstract
With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and [...] Read more.
With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC·m−2. The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC·m−2/d for EVI vs. 0.63 gC·m−2/d for LAI vs. 0.30 gC·m−2/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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22 pages, 16238 KiB  
Article
Spectroscopic Phenological Characterization of Mangrove Communities
by Christopher Small and Daniel Sousa
Remote Sens. 2024, 16(15), 2796; https://doi.org/10.3390/rs16152796 - 30 Jul 2024
Viewed by 1590
Abstract
Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology [...] Read more.
Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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18 pages, 2938 KiB  
Article
An Improved Approach to Estimate Stocking Rate and Carrying Capacity Based on Remotely Sensed Phenology Timings
by Yan Shi, Gary Brierley, George L. W. Perry, Jay Gao, Xilai Li, Alexander V. Prishchepov, Jiexia Li and Meiqin Han
Remote Sens. 2024, 16(11), 1991; https://doi.org/10.3390/rs16111991 - 31 May 2024
Viewed by 934
Abstract
Accurate estimation of livestock carrying capacity (LCC) and implementation of an appropriate actual stocking rate (ASR) are key to the sustainable management of grazing adapted alpine grassland ecosystems. The reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGB [...] Read more.
Accurate estimation of livestock carrying capacity (LCC) and implementation of an appropriate actual stocking rate (ASR) are key to the sustainable management of grazing adapted alpine grassland ecosystems. The reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here, we demonstrate the limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in an effort to support more accurate and reliable estimation of LCC and ASR. The results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. The findings from a smaller-scale study that incorporate phenology timings into the estimation of annual aboveground biomass reveal that summer pastures in Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000 to 2005. The methods proposed can be extended to map grassland grazing pressure by predicting the LCC and tracking the ASR, thereby improving sustainable resource use in alpine grasslands. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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20 pages, 6728 KiB  
Article
Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria
by Ilina Kamenova, Milen Chanev, Petar Dimitrov, Lachezar Filchev, Bogdan Bonchev, Liang Zhu and Qinghan Dong
Remote Sens. 2024, 16(7), 1144; https://doi.org/10.3390/rs16071144 - 25 Mar 2024
Cited by 4 | Viewed by 2284
Abstract
The aim of this study is to predict and map winter wheat yield in the Parvomay municipality, situated in the Upper Thracian Lowland of Bulgaria, utilizing satellite data from Sentinel-2. The main crops grown in the research area are winter wheat, rapeseed, sunflower, [...] Read more.
The aim of this study is to predict and map winter wheat yield in the Parvomay municipality, situated in the Upper Thracian Lowland of Bulgaria, utilizing satellite data from Sentinel-2. The main crops grown in the research area are winter wheat, rapeseed, sunflower, and maize. To distinguish winter wheat fields accurately, we evaluated classification methods such as Support Vector Machines (SVM) and Random Forest (RF). These methods were applied to satellite multispectral data acquired by the Sentinel-2 satellites during the growing season of 2020–2021. In accordance with their development cycles, temporal image composites were developed to identify suitable moments when each crop is most accurately distinguished from others. Ground truth data obtained from the integrated administration and control system (IACS) were used for training the classifiers and assessing the accuracy of the final maps. Winter wheat fields were masked using the crop mask created from the best-performing classification algorithm. Yields were predicted with regression models calibrated with in situ data collected in the Parvomay study area. Both SVM and RF algorithms performed well in classifying winter wheat fields, with SVM slightly outperforming RF. The produced crop maps enable the application of crop-specific yield models on a regional scale. The best predictor of yield was the green NDVI index (GNDVI) from the April monthly composite image. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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13 pages, 14755 KiB  
Article
The Sensitivity of Green-Up Dates to Different Temperature Parameters in the Mongolian Plateau Grasslands
by Meiyu Wang, Hongyan Zhang, Bohan Wang, Qingyu Wang, Haihua Chen, Jialu Gong, Mingchen Sun and Jianjun Zhao
Remote Sens. 2023, 15(15), 3830; https://doi.org/10.3390/rs15153830 - 1 Aug 2023
Cited by 2 | Viewed by 1416
Abstract
The rise in global average surface temperature has promoted the advancement of spring vegetation phenology. However, the response of spring vegetation phenology to different temperature parameters varies. The Mongolian Plateau, one of the largest grasslands in the world, has green-up dates (GUDs) with [...] Read more.
The rise in global average surface temperature has promoted the advancement of spring vegetation phenology. However, the response of spring vegetation phenology to different temperature parameters varies. The Mongolian Plateau, one of the largest grasslands in the world, has green-up dates (GUDs) with unclear sensitivity to different temperature parameters. To address this issue, we investigated the responses of GUDs to different temperature parameters in the Mongolian Plateau grasslands. The results show that GUDs responded significantly differently to changes in near-surface temperature (TMP), near-surface temperature maximum (TMX), near-surface temperature minimum (TMN), and diurnal temperature range (DTR). GUDs advanced as TMP, TMX, and TMN increased, with TMN having a more significant effect, whereas increases in DTR inhibited the advancement of GUDs. GUDs were more sensitive to TMX and TMN than to TMP. The sensitivity of GUDs to DTR showed an increasing trend from 1982 to 2015 and showed this parameter’s great importance to GUDs. Our results also show that the spatial and temporal distributions of temperature sensitivity are only related to temperature conditions in climatic zones instead of whether they are arid. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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Review

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29 pages, 6891 KiB  
Review
Advances in Optical and Thermal Remote Sensing of Vegetative Drought and Phenology
by Ting Li and Shaobo Zhong
Remote Sens. 2024, 16(22), 4209; https://doi.org/10.3390/rs16224209 - 12 Nov 2024
Viewed by 1520
Abstract
In recent decades, remote sensing of vegetative drought and phenology has gained considerable attention from researchers, leading to a significant increase in research activity in this area. While new drought indices are being proposed, there is also growing attention on how variations in [...] Read more.
In recent decades, remote sensing of vegetative drought and phenology has gained considerable attention from researchers, leading to a significant increase in research activity in this area. While new drought indices are being proposed, there is also growing attention on how variations in phenology affect drought detection. This review begins by exploring the crucial role of satellite optical and thermal remote sensing technologies in monitoring vegetative drought. It presents common methods after revisiting the foundational concepts. Then, the review examines remote sensing of land surface phenology (LSP) due to its strong connection with vegetative drought. Subsequently, we investigate vegetative drought detection techniques that consider phenological variability and recommend approaches to improve the detection of vegetative drought, emphasizing the necessity to incorporate phenological metrics. Finally, we suggest potential future work and directions. Unlike other review papers on remote sensing of vegetative drought, this review uniquely surveys the comprehensive advancements in both detecting vegetative drought and estimating LSP through optical and thermal remote sensing. It also highlights the necessity and potential applications for these practices. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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