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Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 35799

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Special Issue Editors

Dr. Ahmed Laamrani

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Guest Editor

Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir 43150, Morocco
Interests: terrestrial remote sensing; GIS; soil carbon; soil and vegetation monitoring; UAV and satellite remote sensing; land cover change; airborne hyperspectral imaging; LIDAR; Geoinformation; Boreal forest peatlands; digital soil mapping

Dr. Abdelghani Chehbouni

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International Water Research Institute, Mohammed VI Polytechnic University, 43150 Ben Guerir, Morocco
Interests: water resources management; irrigation management; remote sensing; GIS; agrometeorology; saltwater intrusion; water salinization; groundwater
Special Issues, Collections and Topics in MDPI journals

Dr. Abdelghani Boudhar

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Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir 43150, Morocco
Interests: hydrology; remote sensing applications on natural resources; water resources assessment; geospatial technologies; spatial long time series
Special Issues, Collections and Topics in MDPI journals

Dr. Tarik Benabdelouahab

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National Institute of Agricultural Research (INRA), Beni Mellal, Morocco
Interests: monitoring and management of natural resources; remote sensing modeling; soil and vegetation monitoring; land use/land cover change; multi- source/sensor satellite imagery; Spatial analysis of climatic parameters

Special Issue Information

Dear Colleagues,

Plant phenology is the study of recurring life cycle stages (e.g., growth reactivation and dormancy, leaf emergence and senescence, and flowering) and is considered as one of the most sensitive indicators of climate and environmental changes. Remote sensing-based techniques, ground-based observations, and bioclimatic models have been commonly used in plant phenology studies. Remote sensing sensors on board different space-borne or airborne platforms (e.g., satellite, aircraft, UAVs) data have the advantage of providing wide coverage with varying temporal, spectral and spatial resolutions; and are therefore widely recognized tools to study phenology in a number of ecosystems such as agriculture, forests, hydrology and grasslands. Several studies have demonstrated that the Normalized Difference Vegetation Index (NDVI) derived from different remote sensing imagery (e.g., MODIS, AVHRR, MERIS; Landsat, Sentinel, UAVs) is one of the reliable spectral indices widely used to study vegetation phenology for the reconstruction of phenological transitions of different vegetation types, including croplands, forests and grasslands. Because it is related to various biophysical parameters (e.g., green-leaf biomass, LAI and FPAR), NDVI time series data are commonly used for spatiotemporal phenology studies. In the last decade, substantial advances have been made in predicting many plant phenological stages (i.e., start and end dates, and length of the growing season) from both very high remote sensing technics (e.g., UAVs, hyperspectral sensing) and ground-based observations.

For this forthcoming Special Issue, we invite manuscripts in all aspects regarding remote sensing on plant phenology and its applications, including croplands, forests and grasslands among others. Both reviews and original research articles on systems, hardware, or algorithms are welcome. Reviews should provide an up-to-date overview of the state-of-the-art technologies such as existing methods for plant phenology development stages tracking/detection and emerging new techniques based on the analysis of time-series, multispectral, hyperspectral, and thermal remote sensing imagery. Original research papers should focus on new approaches; solve an important problem in plant phenology-based remote sensing; or any other no-contact proximal plant phenology sensing topics that have experienced significant advancements in the past decade (e.g., multi-sensors on board UAVs; chlorophyll fluorescence; mapping interface; mobile devices and apps; among others). We also encourage and welcome manuscript from developing countries (i.e., African countries). If you have ideas to discuss before submission, please feel free to contact us. We look forward to receiving your manuscript submitted to this Special Issue.

Dr. Ahmed Laamrani
Dr. Abdelghani Chehbouni
Dr. Abdelghani Boudhar
Dr. Tarik Benabdelouahab
Guest Editors

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Keywords

Vegetation phenology monitoring / long- and short-term
Phenology stages change detection
Time-series data
Satellite data (e.g., MODIS, AVHRR, MERIS; Landsat, Sentinel)
Satellite-derived vegetation phenological metrics
Phenological response to climate change and drought
Multi-sensor / UAV / big data
Multi-sensors techniques (Fusion ; Classification algorithms for time-series data)

Published Papers (9 papers)

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26 pages, 9149 KiB

Open AccessArticle

Climate Effects on Vertical Forest Phenology of Fagus sylvatica L., Sensed by Sentinel-2, Time Lapse Camera, and Visual Ground Observations

by Lars Uphus, Marvin Lüpke, Ye Yuan, Caryl Benjamin, Jana Englmeier, Ute Fricke, Cristina Ganuza, Michael Schwindl, Johannes Uhler and Annette Menzel

Remote Sens. 2021, 13(19), 3982; https://doi.org/10.3390/rs13193982 - 5 Oct 2021

Cited by 5 | Viewed by 2623

Abstract

Contemporary climate change leads to earlier spring phenological events in Europe. In forests, in which overstory strongly regulates the microclimate beneath, it is not clear if further change equally shifts the timing of leaf unfolding for the over- and understory of main deciduous forest species, such as Fagus sylvatica L. (European beech). Furthermore, it is not known yet how this vertical phenological (mis)match—the phenological difference between overstory and understory—affects the remotely sensed satellite signal. To investigate this, we disentangled the start of season (SOS) of overstory F.sylvatica foliage from understory F. sylvatica foliage in forests, within nine quadrants of 5.8 × 5.8 km, stratified over a temperature gradient of 2.5 °C in Bavaria, southeast Germany, in the spring seasons of 2019 and 2020 using time lapse cameras and visual ground observations. We explained SOS dates and vertical phenological (mis)match by canopy temperature and compared these to Sentinel-2 derived SOS in response to canopy temperature. We found that overstory SOS advanced with higher mean April canopy temperature (visual ground observations: −2.86 days per °C; cameras: −2.57 days per °C). However, understory SOS was not significantly affected by canopy temperature. This led to an increase of vertical phenological mismatch with increased canopy temperature (visual ground observations: +3.90 days per °C; cameras: +2.52 days per °C). These results matched Sentinel-2-derived SOS responses, as pixels of higher canopy height advanced more by increased canopy temperature than pixels of lower canopy height. The results may indicate that, with further climate change, spring phenology of F. sylvatica overstory will advance more than F. sylvatica understory, leading to increased vertical phenological mismatch in temperate deciduous forests. This may have major ecological effects, but also methodological consequences for the field of remote sensing, as what the signal senses highly depends on the pixel mean canopy height and the vertical (mis)match. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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17 pages, 5229 KiB

Open AccessArticle

Response of Vegetation Photosynthetic Phenology to Urbanization in Dongting Lake Basin, China

by Peng Li, Mai Sun, Yuxin Liu, Peixin Ren, Changhui Peng, Xiaolu Zhou and Jiayi Tang

Remote Sens. 2021, 13(18), 3722; https://doi.org/10.3390/rs13183722 - 17 Sep 2021

Cited by 8 | Viewed by 2500

Abstract

Urbanization can induce environmental changes such as the urban heat island effect, which in turn influence the terrestrial ecosystem. However, the effect of urbanization on the phenology of subtropical vegetation remains relatively unexplored. This study analyzed the changing trend of vegetation photosynthetic phenology in Dongting Lake basin, China, and its response to urbanization using nighttime light and chlorophyll fluorescence datasets. Our results indicated the start of the growing season (SOS) of vegetation in the study area was significantly advanced by 0.70 days per year, whereas the end of the growing season (EOS) was delayed by 0.24 days per year during 2000–2017. We found that urbanization promoted the SOS advance and EOS delay. With increasing urbanization intensity, the sensitivity of SOS to urbanization firstly increased then decreased, while the sensitivity of EOS to urbanization decreased with urbanization intensity. The climate sensitivity of vegetation phenology varied with urbanization intensity; urbanization induced an earlier SOS by increasing preseason minimum temperatures and a later EOS by increasing preseason precipitation. These findings improve our understanding of the vegetation phenology response to urbanization in subtropical regions and highlight the need to integrate human activities into future vegetation phenology models. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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15 pages, 2693 KiB

Open AccessArticle

Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing

by Koffi Dodji Noumonvi, Gal Oblišar, Ana Žust and Urša Vilhar

Remote Sens. 2021, 13(15), 3015; https://doi.org/10.3390/rs13153015 - 1 Aug 2021

Cited by 6 | Viewed by 2967

Abstract

Phenological events are good indicators of the effects of climate change, since phenological phases are sensitive to changes in environmental conditions. Although several national phenological networks monitor the phenology of different plant species, direct observations can only be conducted on individual trees, which cannot be easily extended over large and continuous areas. Remote sensing has often been applied to model phenology for large areas, focusing mostly on pure forests in which it is relatively easier to match vegetation indices with ground observations. In mixed forests, phenology modelling from remote sensing is often limited to land surface phenology, which consists of an overall phenology of all tree species present in a pixel. The potential of remote sensing for modelling the phenology of individual tree species in mixed forests remains underexplored. In this study, we applied the seasonal midpoint (SM) method with MODIS GPP to model the start of season (SOS) and the end of season (EOS) of six different tree species in Slovenian mixed forests. First, substitute locations were identified for each combination of observation station and plant species based on similar environmental conditions (aspect, slope, and altitude) and tree species of interest, and used to retrieve the remote sensing information used in the SM method after fitting the best of a Gaussian and two double logistic functions to each year of GPP time series. Then, the best thresholds were identified for SOS and EOS, and the results were validated using cross-validation. The results show clearly that the usual threshold of 0.5 is not best in most cases, especially for estimating the EOS. Despite the difficulty in modelling the phenology of different tree species in a mixed forest using remote sensing, it was possible to estimate SOS and EOS with moderate errors as low as <8 days (Fagus sylvatica and Tilia sp.) and <10 days (Fagus sylvatica and Populus tremula), respectively. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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20 pages, 6009 KiB

Open AccessArticle

The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany)

by Johannes Löw, Tobias Ullmann and Christopher Conrad

Remote Sens. 2021, 13(15), 2951; https://doi.org/10.3390/rs13152951 - 27 Jul 2021

Cited by 5 | Viewed by 2463

Abstract

This study explores the potential of Sentinel-1 Synthetic Aperture Radar (SAR) to identify phenological phases of wheat, sugar beet, and canola. Breakpoint and extreme value analyses were applied to a dense time series of interferometric (InSAR) and polarimetric (PolSAR) features recorded during the growing season of 2017 at the JECAM site DEMMIN (Germany). The analyses of breakpoints and extrema allowed for the distinction of vegetative and reproductive stages for wheat and canola. Certain phenological stages, measured in situ using the BBCH-scale, such as leaf development and rosette growth of sugar beet or stem elongation and ripening of wheat, were detectable by a combination of InSAR coherence, polarimetric Alpha and Entropy, and backscatter (VV/VH). Except for some fringe cases, the temporal difference between in situ observations and breakpoints or extrema ranged from zero to five days. Backscatter produced the signature that generated the most breakpoints and extrema. However, certain micro stadia, such as leaf development of BBCH 10 of sugar beet or flowering BBCH 69 of wheat, were only identifiable by the InSAR coherence and Alpha. Hence, it is concluded that combining PolSAR and InSAR features increases the number of detectable phenological events in the phenological cycles of crops. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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22 pages, 2189 KiB

Open AccessArticle

Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

by Yanjun Yang, Bo Tao, Liang Liang, Yawen Huang, Chris Matocha, Chad D. Lee, Michael Sama, Bassil El Masri and Wei Ren

Remote Sens. 2021, 13(9), 1615; https://doi.org/10.3390/rs13091615 - 21 Apr 2021

Cited by 7 | Viewed by 4125

Abstract

Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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21 pages, 12379 KiB

Open AccessArticle

Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

by Youssef Lebrini, Abdelghani Boudhar, Ahmed Laamrani, Abdelaziz Htitiou, Hayat Lionboui, Adil Salhi, Abdelghani Chehbouni and Tarik Benabdelouahab

Remote Sens. 2021, 13(4), 578; https://doi.org/10.3390/rs13040578 - 6 Feb 2021

Cited by 11 | Viewed by 3941

Abstract

Changing land use patterns is of great importance in environmental studies and critical for land use management decision making over farming systems in arid and semi-arid regions. Unfortunately, ground data scarcity or inadequacy in many regions can cause large uncertainties in the characterization of phenological changes in arid and semi-arid regions, which can hamper tailored decision making towards best agricultural management practices. Alternatively, state-of-the-art methods for phenological metrics’ extraction and long time-series analysis techniques of multispectral remote sensing imagery provide a viable solution. In this context, this study aims to characterize the changes over farming systems through trend analysis. To this end, four farming systems (fallow, rainfed, irrigated annual, and irrigated perennial) in arid areas of Morocco were studied based on four phenological metrics (PhM) (i.e., great integral, start, end, and length of the season). These were derived from large Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series using both a machine learning algorithm and a pixel-based change analysis method. Results showed that during the last twenty-year period (i.e., 2000–2019), a significant dynamism of the plant cover was linked to the behavior of farmers who tend to cultivate intensively and to invest in high-income crops. More specifically, a relevant variability in fallow and rainfed areas, closely linked to the weather conditions, was found. In addition, significant lag trends of the start (−6 days) and end (+3 days) were found, which indicate that the length of the season was related to the spatiotemporal variability of rainfall. This study has also highlighted the potential of multitemporal moderate spatial resolution data to accurately monitor agriculture and better manage land resources. In the meantime, for operationally implementing the use of such work in the field, we believe that it is essential consider the perceptions, opinions, and mutual benefits of farmers and stakeholders to improve strategies and synergies whilst ensuring food, welfare, and sustainability. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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16 pages, 4737 KiB

Open AccessArticle

Mapping Spatiotemporal Changes in Vegetation Growth Peak and the Response to Climate and Spring Phenology over Northeast China

by Xiaoying Wang, Yuke Zhou, Rihong Wen, Chenghu Zhou, Lili Xu and Xi Xi

Remote Sens. 2020, 12(23), 3977; https://doi.org/10.3390/rs12233977 - 4 Dec 2020

Cited by 16 | Viewed by 2933

Abstract

Global climate change has led to significant changes in seasonal rhythm events of vegetation growth, such as spring onset and autumn senescence. Spatiotemporal shifts in these vegetation phenological metrics have been widely reported over the globe. Vegetation growth peak represents plant photosynthesis capacity and responds to climate change. At present, spatiotemporal changes in vegetation growth peak characteristics (timing and maximum growth magnitude) and their underlying governing mechanisms remain unclear at regional scales. In this study, the spatiotemporal dynamics of vegetation growth peak in northeast China (NEC) was investigated using long-term NDVI time series. Then, the effects of climatic factors and spring phenology on vegetation growth peak were examined. Finally, the contribution of growth peak to vegetation production variability was estimated. The results of the phenological analysis indicate that the date of vegetation green up in spring and growth peak in summer generally present a delayed trend, while the amplitude of growth peak shows an increasing trend. There is an underlying cycle of 11 years in the vegetation growth peak of the entire study area. Air temperature and precipitation before the growing season have a small impact on vegetation growth peak amplitude both in its spatial extent and magnitude (mainly over grasslands) but have a significant influence on the date of the growth peak in the forests of the northern area. Spring green-up onset has a more significant impact on growth peak than air temperature and precipitation. Although green-up date plays a more pronounced role in controlling the amplitude of the growth peak in forests and grasslands, it also affects the date of growth peak in croplands. The amplitude of the growth peak has a significant effect on the inter-annual variability of vegetation production. The discrepant patterns of growth peak response to climate and phenology reflect the distinct adaptability of the vegetation growth peak to climate change, and result in different carbon sink patterns over the study area. The study of growth peak could improve our understanding of vegetation photosynthesis activity over various land covers and its contribution to carbon uptake. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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23 pages, 8148 KiB

Open AccessArticle

Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2

by Manjunatha Venkatappa, Sutee Anantsuksomsri, Jose Alan Castillo, Benjamin Smith and Nophea Sasaki

Remote Sens. 2020, 12(18), 3109; https://doi.org/10.3390/rs12183109 - 22 Sep 2020

Cited by 12 | Viewed by 8617

Abstract

Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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Other

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12 pages, 5793 KiB

Open AccessTechnical Note

Start of the Green Season and Normalized Difference Vegetation Index in Alaska’s Arctic National Parks

by David K. Swanson

Remote Sens. 2021, 13(13), 2554; https://doi.org/10.3390/rs13132554 - 30 Jun 2021

Cited by 4 | Viewed by 1990

Abstract

Daily Normalized Difference Vegetation Index (NDVI) values from the MODIS Aqua and Terra satellites were compared with on-the-ground camera observations at five locations in northern Alaska. Over half of the spring rise in NDVI was due to the transition from the snow-covered landscape to the snow-free surface prior to the deciduous leaf-out. In the fall after the green season, NDVI fluctuated between an intermediate level representing senesced vegetation and lower values representing clouds and intermittent snow, and then dropped to constant low levels after establishment of the permanent winter snow cover. The NDVI value of snow-free surfaces after fall leaf senescence was estimated from multi-year data using a 90th percentile smoothing spline curve fit to a plot of daily NDVI values vs. ordinal date. This curve typically showed a flat region of intermediate NDVI values in the fall that represent cloud- and snow-free days with senesced vegetation. This “fall plateau” was readily identified in a large systematic sample of MODIS NDVI values across the study area, in typical tundra, shrub, and boreal forest environments. The NDVI level of the fall plateau can be extrapolated to the spring rising leg of the annual NDVI curve to approximate the true start of green season. Full article

(This article belongs to the Special Issue Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling)

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