Land Surface Phenology Research Articles

Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery.

VCPNET: A new dataset to benchmark vegetation carbon phenology metrics

Shifts in plant phenology associated with climate change have received unprecedented attention from land managers, policymakers, and the scientific community, with important implications for the structure and function of the biosphere. Eddy covariance (EC) has been used to measure carbon exchange between the land surface and atmosphere, which provides an opportunity to describe the seasonality of ecosystem processes. Using BASE flux products provided by the AmeriFlux network and a non-linear modeling approach, this study developed a vegetation carbon phenology (VCP) dataset, which we termed VCPNET (version 1.0). VCPNET was developed with fine spatial and temporal scales for a total of 87 EC sites (2820 site-years of data) across North America, including nine vegetation types. We extracted phenology metrics by site to provide long-term VCP metrics across these ecosystems. Analyses revealed significant differences in photosynthetic capacity and respiration rate-derived VCP metrics across the landscape, as well as differences associated with whether daily cumulative or half-hourly maximum measurements were used. Daily carbon fluxes may be more consistent in simulating phenology behavior than 30-min maximum fluxes across all vegetation types, except in sites classified as Grassland. Overall vegetation types, the seasonality of photosynthetic capacity may exert a larger control on the net carbon uptake of the ecosystem compared to the respiration rate. Thus, model-estimated photosynthesis-activated seasons may better represent true phenology, particularly the development of above- and below-ground plant biomass in tree-covered areas, which is crucial in forecasting ecosystem carbon sequestration. In addition, a nonlinear model can robustly capture day-to-day variation in carbon flux; however, precise simulation of phenological patterns with multiple peaks, typically observed in disturbed ecosystems, must be addressed during model optimization. The cross-ecosystem phenology metrics provided by VCPNET could be utilized for regional-global analysis and calibration of satellite-based vegetation indices, which accurately scale land surface phenology. Our findings provide a prospective tool for a deeper understanding of ecosystem function, allowing a direction and foundation for optimizing phenology models and algorithms and ultimately contributing to developing new ecological theories and practices in response to climate change.

Spatial and temporal heterogeneity of land surface phenology in Shanxi Province from 2001 to 2020

AbstractLand surface phenology encompasses variations in the life cycle events of plants induced by seasonal changes in environmental factors, primarily meteorological conditions. This study leverages Google Earth Engine to extract a comprehensive time series of two‐band Enhanced Vegetation Index (EVI 2) from Landsat images. Utilizing relatively sparse data spanning from 2001 to 2020, a Bayesian hierarchical model is applied at a 30 m resolution to capture the continuous temporal evolution of phenology. The fitting results of this study demonstrate excellent performance, with annual correlation coefficients consistently exceeding 0.89. The findings indicate that between 2001 and 2020, the Start of Season in Shanxi advanced by an average of 0.79 days per year, the End of Season was delayed by an average of 0.83 days per year, and the Length of Season (LOS) extended by an average of 0.80 days per year. Spatial disparities in phenological periods in Shanxi are evident, with an average LOS of 192 days on 35–36° N and only 122 days on 40–41° N. Below 1200 m, phenological periods exhibit significant changes influenced by human activities, while between 1200 m and 2600 m, LOS shows a weak trend of shortening. Above 2600 m, there is a noticeable reduction in LOS. With an increasing slope, LOS increases from an average of 175 days to 187 days (>25°). This study, utilizing Shanxi as a case study, explores the spatiotemporal evolution characteristics of vegetation phenology, aiming to support fine land management and enhance agricultural productivity.

Tree species diversity drives the land surface phenology of seasonally dry tropical woodlands

Abstract Seasonal foliage display (leaf phenology) is a key determinant of ecosystem function. Variation in land surface phenology, observed via space‐borne remote sensing, can be explained at broad spatial scales by climate, but we lack understanding of how vegetation structure and floristic diversity mediates these relationships. This lack of understanding hampers our ability to predict changes in phenology and therefore ecosystem function, in light of rapid ongoing shifts in biodiversity and ecosystem structure due to land use and climate change. We combined a network of 619 vegetation monitoring sites across seasonally dry tropical deciduous woodlands in Zambia with land surface phenology metrics to investigate the role of tree species diversity, composition and vegetation structure on patterns of land surface phenology, including the phenomenon of pre‐rain green‐up. Tree species diversity was associated with earlier pre‐rain green‐up, a longer growing season, and greater cumulative foliage production. Independent of diversity, proportional abundance of Detarioideae species (subfamily of Fabaceae) was associated with a longer growing season by facilitating earlier pre‐rain green‐up. Woodland stands with larger trees green up earlier, suggesting access to deep groundwater reserves. Senescence metrics showed variation among sites but were not well‐explained by precipitation, temperature, structure or diversity. Synthesis: Tree diversity, composition and structure are co‐determinants of seasonal patterns of foliage display in seasonally dry tropical deciduous woodlands, as measured via land surface phenology, at regional scale. Our study identifies both a niche complementarity effect whereby diverse woodlands exhibit longer growing seasons, as well as a mass‐ratio effect whereby detarioid species drive earlier pre‐rain green‐up, providing insights into the mechanisms underlying the biodiversity ecosystem function relationship in this biome. We stress the importance of considering biotic controls on ecosystem functioning in the next generation of earth‐system models predicting the response of communities to global change.

Unpacking satellite pixels: UAVs reveal fine-scale drivers of land surface phenology in a winter rainfall shrubland

Land surface phenology (LSP) can reveal important connections between vegetation dynamics and climate but remains poorly understood in evergreen winter-rainfall shrublands globally. Field-based studies have indicated diverse plant functional strategies in shrublands, but further work is required to link LSP to vegetation functional composition in these regions. We analysed time-series of the normalised difference vegetation index (NDVI) in fynbos shrublands of South Africa using multi-spectral imagery from satellites and unmanned aerial vehicles (UAVs). We investigated the climate drivers of seasonal vegetative phenology and long-term NDVI trends at multiple spatial scales ranging from the landscape to individual species. At coarse spatial resolutions, NDVI time-series indicated rainfall-driven vegetation dynamics in fynbos, both at inter and intra-annual time scales. However, high-resolution time-series from UAVs exposed an underlying divergence in vegetative phenology and long-term NDVI trends between shallow and deep-rooted growth forms. Phenophases and NDVI trends of isolated, deep-rooted, overstory shrubs were decoupled from rainfall relative to dense overstory patches and shallow-rooted understory growth forms. Variations in growth form phenology were not detected at coarse spatial scales due to scaling and competitive effects based on the functional composition of the vegetation.

Chasing and surfing seasonal waves: Avian migration through the US tracks land surface phenology in fall, but not spring.

Climate change is altering the timing of seasonal events for many taxa. There is limited understanding of how northward/southward songbird migration follows or is limited by the latitudinal progression of seasonal transitions. Consistent environmental conditions that migrating birds encounter across latitudes likely represent or correlate with important resources or limiting factors for migration. We tested whether migratory passage-observed via radar-consistently tracked land surface variables and phenophases across latitudes in the US Central Flyway in both spring and fall. The daily temperatures, precipitation and vegetation greenness occurring on 10%, 50% and 90% cumulative passage dates changed substantially with latitude, indicating that most migrants experienced rapidly changing conditions as they headed north or south. Temperature did not limit the progression of migration in either season. Peak spring migration in the southern US occurred nearly 40 days after the spring green wave, the northward progression of vegetation growth, but nearly caught up to green-up at 48° N. Spring migration phenology may have evolved to prioritize earlier arrival for breeding. Across all latitudes, peak fall migration coincided with the same land surface phenophase, an interval of 26 days prior to dormancy onset. Migrants may rely on phenological events in vegetation during fall stopovers. Considering that (a) migratory passage tracked fall land surface phenology across latitudes at a continental scale, (b) previous studies at local scales have demonstrated the importance of fruit during fall migratory stopover and (c) fruiting phenology in North America is occurring later over time while fall migration is advancing, the potential for mismatch between fall fruiting and bird migration phenology urgently needs further investigation.

Evaluation of Land Surface Phenology in Northern Hemisphere Permafrost Regions

AbstractVegetation phenology interacts strongly with climate through the exchange of carbon, water, momentum, and energy between terrestrial ecosystem and atmosphere. These vegetation dynamics in Northern Hemisphere permafrost regions have substantial uncertainties in previous studies, partly due to differences in datasets. Thus, reliable land surface phenology (LSP) retrievals are crucial for understanding the effects of climate change on ecosystems and biosphere‐atmosphere‐hydrosphere interactions. We assessed various LSP datasets at different spatial resolutions for 2001–2014, generated by different methods based on different satellite observations. We also assessed the accuracy of LSP by comparing with CO2 flux phenology. For start of growing season (SOS), the comprehensive evaluation indicated MODIS phenology showed better consistency and higher accuracy with flux‐derived phenology observations (R = 0.54, RMSE = 36.2 days, bias<5 days). For end of growing season (EOS), we cannot conclusively determine which LSP performs best. In the Northern Hemisphere permafrost regions, SOS occurred at 100–150 days (April–May), and EOS occurred at 260–320 days (September–November). During 2001–2014, SOS occurred earlier by 0.33 ± 0.30 days/yr. Significant trends were observed for 6.4%–27.6% of pixels, averaging −1.30 ± 1.16 days/yr. EOS occurred earlier by 0.25 ± 0.43 days/yr, with significant trends averaging −0.52 ± 0.94 days/yr. Among LSPs, variability in EOS trends was significant, with even the direction of trends differing. This study may provide insights into LSP data selection and further understanding of vegetation dynamics and its mechanisms in permafrost regions.

Remotely-sensed phenoclusters of Wisconsin’s forests, shrublands, and grasslands for biodiversity applications

Heterogeneous vegetation supports higher species richness than homogenous vegetation, which is why efficiently identifying heterogenous vegetation can be useful for biodiversity conservation. Satellite remote-sensing data provide an opportunity to generate vegetation heterogeneity metrics and to explore the phenology of vegetation patterns. Phenoclusters are vegetation types with similar phenological characteristics, and valuable for capturing vegetation habitat heterogeneity patterns. Our goal was to map phenoclusters for Wisconsin, USA, at 10-m spatial resolution based on land surface phenology metrics from EVI (Enhanced Vegetation Index) Sentinel-2 data. We characterized each phenocluster based on landcover composition and structure, phenology timing, and environmental factors, and compared them to bird species richness. We also calculated the diversity of phenoclusters at multiple spatial extents. We identified 14 phenoclusters in Wisconsin, each with distinct landcover composition and structure, and unique phenological characteristics. Our remotely-sensed phenoclusters effectively captured environmental gradients, with elevation and temperature emerging as the most important driving variables. Furthermore, the phenoclusters successfully captured bird biodiversity patterns, especially richness of forest and grassland specialist. Our results identified phenological patterns among Wisconsin’s forests, shrublands, and grasslands, capturing phenological timing both among and within the same tree species. Phenoclusters are a valuable tool for capturing vegetation habitat heterogeneity, phenology diversity and biodiversity patterns, as well as climate change effects.

Patterns and trends in the spatial heterogeneity of land surface phenology of global forests

Land-surface phenology is a widely used indicator of how terrestrial ecosystems respond to environmental change. The spatial variability of this plant functional trait has also been advocated as an indicator of the functional composition of ecosystems. However, a global-scale assessment of spatial patterns in the spatial heterogeneity of forest phenology is currently lacking. To address this knowledge gap, we developed an index based on satellite retrievals and used it to quantify phenological diversity across global forest biomes. We show that there is considerable variation in phenological diversity among biomes, with the highest overall levels occurring in arid and temperate regions. An analysis of the drivers of the spatial patterns revealed that temperature-related factors primarily determine the variation in phenological diversity. Notably, temperature seasonality and mean annual temperature emerged as the most significant variables in explaining this global-scale variability. Furthermore, an assessment of temporal changes over an 18-year period revealed strong climate-driven shifts of phenological diversity in boreal and arid regions, suggesting that there may be an ongoing widespread homogenisation of land surface phenology within forest ecosystems. Our findings ultimately contribute to the development of a novel Essential Biodiversity Variable, which may enable scientists and practitioners to quantify the functional composition of ecosystems at unprecedented spatial and temporal scales.

Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data

Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R2>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.

Impacts of snow cover seasonality on spring land surface phenology of forests in Changbai mountains of Northeast China

Snow cover phenology (SCP) strongly affects forest spring phenology (the start of growing season, SOS), but the underlying mechanism of the relationship varies. In this study, we aimed to analyze the relationship between forest SOS and SCP, and investigate the mechanisms about how changes of SCP affect forest SOS. To do so, we extracted forest SOS and SCP from multiple remote sensing datasets and analyzed the spatio-temporal patterns of both in Changbai Mountains (2001−2020). We assessed the relationships between SCP and forest SOS using partial least squares regression analysis and investigated the potential mechanism of SCP changes affecting on forest SOS using path analysis. We found earlier forest SOS (−0.5 days/year), prolonged snow cover duration (SCD, 0.43 day/year), and earlier snow cover end day (SCED, −0.1 days/year) in the region. The results indicated that SCD showed negative influence while SCED showed positive influence on forest SOS in most of the region. Results revealed that the influence of SCP on forest SOS was mainly through altering spring temperature and the dominant path of SCP influencing forest SOS followed hydrothermal gradients. Our study reveals new insights into the influence of changing SCP on forest SOS, which provides the theoretical basis for including SCP in the phenological models.

A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions

Riparian woodlands in drylands are critically important to human society, global biodiversity, and regional water and energy budgets. These sensitive ecosystems have experienced substantial degradation over the last several decades from climatic change and direct human activity. Nevertheless, quantifying long-term change in dryland riparian woodlands remains a major challenge, and much uncertainty exists in their remaining extent, historical breadth, and likely future trajectories. Dryland landscapes show large, fine-scale spatial heterogeneity in seasonal greenness patterns, driven in part by spatial variation in water availability. Riparian woodlands occur where water is concentrated in the landscape, either as aboveground streamflow or subsurface groundwater. In arid and semi-arid climates, this renders them phenologically distinctive from upland ecosystems. However, despite their importance and distinctiveness, there are currently no automated methods for delineating dryland riparian woodlands across regional extents in the cloud. Here we designed and implemented a cloud-based algorithm to retrieve dryland land surface phenology patterns from multispectral satellite imagery and conducted sensitivity analyses using real and simulated data to demonstrate that the approach is robust for MODIS, Sentinel-2, and Landsat over realistic ranges of noise and cloud cover. We then designed a series of random forest vegetation classifiers that integrate phenological and spectral information, vegetative structure from LiDAR, and topography from LiDAR or the Shuttle Radar Topography Mission. We implemented classifiers for three local study sites and then generalized our model to run regionally across the southwestern United States, with balanced accuracy for the riparian woodland class ranging from 94.5% to 97.5% when validated with local to regional datasets. Generally, phenological information proved more important than any other data source for mapping riparian woodlands, which showed more stability in interannual phenology than did upland vegetation types. To our knowledge, ours is the first regional, annual, automatically-generated and updated approach for mapping dryland riparian woodlands in the southwestern United States, paving the way for improved modeling and management efforts on watershed to regional scales. We also provide one of the first operational, exclusively cloud-based methods to extract dryland land surface phenology patterns using Landsat, Sentinel-2, MODIS, or other sensors, providing a framework for future studies investigating other aspects of long-term or spatial variation in dryland vegetative seasonality across the globe.

Effects of land surface temperatures on vegetation phenology along urban–rural local climate zone gradients

ContextUrbanization and local urban climate have multiple impacts on vegetation phenology in urban and suburban areas. Understanding these effects and their interactions with the surface urban heating effect remains limited.ObjectiveWe employed a time series of Earth observation data to analyze land surface phenology (LSP) dynamics and related environmental drivers in the highly urbanized Pearl River Delta (PRD) region.MethodsFirst, local climate zone (LCZ) maps were generated from Earth observation datasets of 2000 and 2019. Second, LSP (i.e., start, end, and length of season) were extracted from vegetation indices for 2000–2019. Thirdly, land surface temperature (LST) was used as an explanatory variable based on the LCZ of cities. Finally, interannual trends of LSP and their association with LST were analyzed, depending on the distance gradient of vegetation to compact high-rise buildings.ResultsUrban surface characteristics showed that LSP in regions dominated by compact and high-rise urban areas presented significant spatiotemporal variation at the start and end of season than those dominated by open, mid-rise, and low-rise areas. The impacts of spring and autumn LST in the daytime on LSP were slightly more substantial than those in the nighttime. The association of decreasing spring LST in the daytime with a delayed start of season is especially pronounced in urban domains.ConclusionsThe results indicate that vegetated areas adjacent to urban domains presented greater spatiotemporal dynamics than suburban and rural regions. Our study emphasizes the dependence of spatiotemporal changes in vegetation phenology on the effects of urban surface warming.

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020

Abstract. Plant phenology refers to cyclic plant growth events, and is one of the most important indicators of climate change. Integration of plant phenology information is crucial for understanding the ecosystem response to global change and modeling the material and energy balance of terrestrial ecosystems. Utilizing 24 552 in situ phenological observations of 24 representative woody plant species from the Chinese Phenology Observation Network (CPON), we have developed maps delineating species phenology (SP) and ground phenology (GP) of forests over China from 1951 to 2020. These maps offer a detailed spatial resolution of 0.1° and a temporal resolution of 1 d. Our method involves a model-based approach to upscale in situ phenological observations to SP maps, followed by the application of weighted average and quantile methods to derive GP maps from the SP data. The resulting SP maps for the 24 woody plants exhibit a high degree of concordance with in situ observations, manifesting an average deviation of 6.9 d for spring and 10.8 d for autumn phenological events. Moreover, the GP maps demonstrate robust alignment with extant land surface phenology (LSP) products sourced from remote sensing data, particularly within deciduous forests, where the average discrepancy is 8.8 d in spring and 15.1 d in autumn. This dataset provides an independent and reliable phenology data source for China on a long-time scale of 70 years, and contributes to more comprehensive research on plant phenology and climate change at both regional and national scales. The dataset can be accessed at https://doi.org/10.57760/sciencedb.07995 (Zhu and Dai, 2023).

Understanding spatio-temporal variation of autumn phenology in temperate China from 1982 to 2018

Land surface phenology plays a crucial role in accurately parameterizing interactions between land ecosystems and the atmosphere. Changes in autumn phenology directly impact the length of vegetation growing season and year-to-year changes in carbon uptake. Previous studies have often focused solely on the end of growing season when characterizing autumn phenology, neglecting the onset and duration of autumn. Here, we extracted the annual autumn phenological metrics, i.e., the start of brown-down phase (SOB), the end of brown-down phase (EOB), and the length of brown-down phase (LOB), for temperate China from 1982 to 2018 based on our self-developed global LSP dataset. We conducted a comparative analysis of SOB, EOB, and LOB in the field of their spatial distribution, temporal trends, and performance on various categories of vegetated regions (i.e., forests, grasslands, croplands, and vegetated areas in urban lands (V_Urban)). The results showed a significant negative correlation between the timing of autumn phenological metrics and latitude in temperate China. Between 1982 and 2018, there were significant positive increasing trends in EOB and LOB in V_Urban, as well as in LOB in forests in temperate China. However, the annual mean SOB, EOB, and LOB did not show significant trends across the entire study area. At the local pixel scale, SOB, EOB, and LOB exhibited a combination of advanced and delayed trends within various vegetated categories. The trends of the same phenological metric were not uniform across these diverse vegetated regions. For instance, the majority of pixels with significant trends in SOB exhibited delayed trends in forests and croplands, while displaying advanced trends in grasslands and V_Urban. In this study, employing three metrics (SOB, EOB, and LOB) to describe autumn phenology enhances our understanding of the impacts of global climate change on ecosystems, offering a more comprehensive and detailed perspective. In the future, satellite-based monitoring and phenological modeling should contemplate incorporating additional potential phenological metrics.

Effectiveness of Spatiotemporal Data Fusion in Fine-Scale Land Surface Phenology Monitoring: A Simulation Study

Spatiotemporal data fusion technologies have been widely used for land surface phenology (LSP) monitoring since it is a low-cost solution to obtain fine-resolution satellite time series. However, the reliability of fused images is largely affected by land surface heterogeneity and input data. It is unclear whether data fusion can really benefit LSP studies at fine scales. To explore this research question, this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms, namely, pair-based Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data in extracting pixel-wise spring phenology (i.e., the start of the growing season, SOS) and its spatial gradient and temporal variation. Our results reveal that: (a) STARFM can improve the accuracy of pixel-wise SOS by up to 74.47% and temporal variation by up to 59.13%, respectively, compared with only using Landsat images, but it can hardly improve the retrieval of spatial gradient. For SSFIT, the accuracy of pixel-wise SOS, spatial gradient, and temporal variation can be improved by up to 139.20%, 26.36%, and 162.30%, respectively; (b) the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year, and it has a large variation with the same number of available Landsat images, and (c) this large variation is highly related to the temporal distributions of available Landsat images, suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period. This study calls for caution with the use of data fusion in LSP studies at fine scales.

Multiscale phenology of seasonally dry tropical forests in an aridity gradient

The leaf phenology of seasonally dry tropical forests (SDTFs) is highly seasonal, marked by synchronized flushing of new leaves triggered by the first rains of the wet season. Such phenological transitions may not be accurately detected by remote sensing vegetation indices and derived transition dates (TDs) due to the coarse spatial and temporal resolutions of satellite data. The aim of this study was to compared TDs from PhenoCams and satellite remote sensing (RS) and used the TDs calculated from PhenoCams to select the best thresholds for RS time series and calculate TDs. For this purpose, we assembled cameras in seven sites along an aridity gradient in the Brazilian Caatinga, a region dominated by SDTFs. The leafing patterns were registered during one to three growing seasons from 2017 to 2020. We drew a region of interest (ROI) in the images to calculate the normalized green chromatic coordinate index. We compared the camera data with the NDVI time series (2000–2019) derived from near-infrared (NIR) and red bands from MODIS product data. Using calibrated PhenoCam thresholds reduced the mean absolute error by 5 days for SOS and 34 days for EOS, compared to common thresholds in land surface phenology studies. On average, growing season length (LOS) did not differ significantly among vegetation types, but the driest sites showed the highest interannual variation. This pattern was applied to leaf flushing (SOS) and leaf fall (EOS) as well. We found a positive relationship between the accumulated precipitation and the LOS and between the accumulated precipitation and maximum and minimum temperatures and the vegetation productivity (peak and accumulated NDVI). Our results demonstrated that (A) the fine temporal resolution of phenocamera phenology time series improved the definitions of TDs and thresholds for RS landscape phenology; (b) long-term RS greening responded to the variability in rainfall, adjusting their timing of green-up and green-down, and (C) the amount of rainfall, although not determinant for the length of the growing season, is related to the estimates of vegetation productivity.

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on land surface phenology, primarily related to the start and end of the growing season. In contrast, the monitoring of crop growth within an agronomic framework has been limited, particularly in the context of recently developed solar-induced chlorophyll fluorescence (SIF) data. Additionally, some critical growth stages have not received adequate attention or evaluation. This study aims to assess the utility of SIF data, collected from both ground and satellite measurements, for identifying critical crop growth stages within the realm of remote sensing phenological estimation. A comparative analysis was conducted using enhanced vegetation index (EVI) data at the Shangqiu site in the North China Plain from 2018 to 2022. Both SIF and EVI time-series data, obtained from ground and satellite sources, undergo a comprehensive phenological estimation framework encompassing pre-processing, modeling, and transition characterization. This approach involves reconciling time-series phenological patterns with crop growth stages, revealing the necessity of redefining the mapping relationship between these two fundamental concepts. After preprocessing the time-series data, the framework incorporates the phenological modeling process employing two double logistic models and a spline model for comparison. Additionally, it includes phenological transition characterization using four different methods. Consequently, each input dataset undergoes an assessment, resulting in 12 sets of estimations, which are compared to select the ideal estimation portfolio for identifying the growth stages of maize and winter wheat. Our findings highlight the efficacy of SIF data in accurately identifying the growth stages of maize and winter wheat, achieving remarkable results with an R-square exceeding 0.9 and an RMSE of less than 1 week for key growth stages (KGSs). Notably, SIF data demonstrate superior accuracy, robustness, and sensitivity to phenological events when compared to EVI data. This study establishes an estimation portfolio utilizing SIF data, involving the Gu model, a double logistic model, as the preferred phenological modelling method together with various compositing methods and transition characterization methods, suitable for most KGSs. These findings create opportunities for future research aimed at enhancing and standardizing crop growth stage identification using remote sensing data for a wide range of KGSs.

Seasonal Effect of the Vegetation Clumping Index on Gross Primary Productivity Estimated by a Two-Leaf Light Use Efficiency Model

Recently, light use efficiency (LUE) models driven by remote sensing data have been widely employed to estimate the gross primary productivity (GPP) of different terrestrial ecosystems at global or regional scales. Furthermore, the two-leaf light use efficiency (TL-LUE) model has been reported to improve the accuracy of GPP estimation, relative to the big-leaf MOD17 model, by separating the entire canopy into sunlit and shaded leaves through the use of constant clumping index estimation (Ω). However, ignoring obvious seasonal changes in the vegetation clumping index (CI) most likely results in GPP estimation errors since the CI tends to present seasonal changes, especially with respect to the obvious presence or absence of leaves within the canopy of deciduous vegetation. Here, we propose a TL-CLUE model that considers the seasonal difference in the CI based on the TL-LUE model to characterize general changes in canopy seasonality. This method composites monthly CI values into two or three Ω values to capture the general seasonal changes in CI while attempting to reduce the potential uncertainty caused during CI inversion. In theory, CI seasonality plays an essential role in the distribution of photosynthetically active radiation absorbed by the canopy (APAR). Specifically, the seasonal difference in CI values mainly considers the state of leaf growth, which is determined by the MODIS land surface phenology (LSP) product (MCD12Q2). Therefore, the one-year cycle (OYC) of leaf life is divided into two (leaf-off and leaf-on) or three seasons (leaf-off, leaf-scattering, and leaf-gathering) according to this MODIS LSP product, and the mean CI of each corresponding season for each vegetation class is computed to smoothen the uncertainties within each seasonal section. With these two or three seasonal Ω values as inputs, the TL-CLUE model by which the seasonal differences in CI are incorporated into the TL-LUE model is run and evaluated based on observations from 84 eddy covariance (EC) tower sites across North America. The results of the analysis reveal that the TL-LUE model widely overestimates GPP for most vegetation types during the leaf-on season, particularly during the growth peak. Although the TL-LUE model shows that the temporal characteristics of GPP agree with the EC observations in terms of general trends, the TL-CLUE model further improves the accuracy of GPP estimation by considering the seasonal changes in the CI. The result of GPP estimation from the TL-CLUE model shows a lower error (RMSE = 2.46 g C m−2 d−1) than the TL-LUE model (RMSE = 2.75 g C m−2 d−1) and somewhat decreases the eight-day GPP overestimation in the TL-LUE model with a constant Ω by approximately 9.76 and 8.970% when adapting three and two Ωs from different seasons, respectively. The study demonstrates that the uncertainty of seasonal disturbance in the CI, quantified by a standard deviation of approximately 0.071 relative to the mean CI of 0.746, is diminished through simple averaging. The seasonal difference in CI should be considered in GPP estimation of terrestrial ecosystems, particularly for vegetation with obvious canopy changes, where leaves go through the complete physiological processes of germination, stretching, maturity, and falling within a year. This study demonstrates the potential of the MODIS CI application in developing ecosystem and hydrological models.