Satellite-derived Phenology Research Articles

Estimating global annual gross primary production based on satellite-derived phenology and maximal carbon uptake capacity

The high uncertainty regarding global gross primary production (GPP) remains unresolved. This study explored the relationships between phenology, physiology, and annual GPP to provide viable alternatives for accurate estimation. A statistical model of integrated phenology and physiology (SMIPP) was developed using GPP data from 145 FLUXNET sites to estimate the annual GPP for various vegetation types. By employing the SMIPP model driven by satellite-derived datasets of the global carbon uptake period (CUP) and maximal carbon uptake capacity (GPPmax), the global annual GPP was estimated for the period from 2001 to 2018. The results demonstrated that the SMIPP model accurately predicted annual GPP, with relative root mean square error values ranging from 11.20 to 19.29% for forest types and 20.49–35.71% for non-forest types. However, wetlands, shrublands, and evergreen forests exhibited relatively low accuracies. The average, trend, and interannual variation of global GPP during 2001–2018 were 132.6 Pg C yr−1, 0.25 Pg C yr−2, and 1.57 Pg C yr−1, respectively. They were within the ranges estimated in other global GPP products. Sensitivity analysis revealed that GPPmax had comparable effects to CUP in high-latitude regions but significantly greater impacts at the global scale, with sensitivity coefficients of 0.85 ± 0.23 for GPPmax and 0.46 ± 0.28 for CUP. This study provides a simple and practical method for estimating global annual GPP and highlights the influence of GPPmax and CUP on global-scale annual GPP.

Atlas of phytoplankton phenology indices in selected Eastern Mediterranean marine ecosystems.

Phytoplankton is a fundamental component of marine food webs and play a crucial role in marine ecosystem functioning. The phenology (timing of growth) of these microscopic algae is an important ecological indicator that can be utilized to observe its seasonal dynamics, and assess its response to environmental perturbations. Ocean colour remote sensing is currently the only means of obtaining synoptic estimates of chlorophyll-a (a proxy of phytoplankton biomass) at high temporal and spatial resolution, enabling the calculation of phenology metrics. However, ocean colour observations have acknowledged weaknesses compromising its reliability, while the scarcity of long-term in situ data has impeded the validation of satellite-derived phenology estimates. To address this issue, we compared one of the longest available in situ time series (20 years) of chlorophyll-a concentrations in the Eastern Mediterranean Sea (EMS), along with concurrent remotely-sensed observations. The comparison revealed a marked coherence between the two datasets, indicating the capability of satellite-based measurements in accurately capturing the phytoplankton seasonality and phenology metrics (i.e., timing of initiation, duration, peak and termination) in the studied area. Furthermore, by studying and validating these metrics we constructed a satellite-derived phytoplankton phenology atlas, reporting in detail the seasonal patterns in several sub-regions in coastal and open seas over the EMS. The open waters host higher concentrations from late October to April, with maximum levels recorded during February and lowest during the summer period. The phytoplankton growth over the Northern Aegean Sea appeared to initiate at least a month later than the rest of the EMS (initiating in late November and terminating in late May). The coastal waters and enclosed gulfs (such as Amvrakikos and Maliakos), exhibit a distinct seasonal pattern with consistently higher levels of chlorophyll-a and prolonged growth period compared to the open seas. The proposed phenology atlas represents a useful resource for monitoring phytoplankton growth periods in the EMS, supporting water quality management practices, while enhancing our current comprehension on the relationships between phytoplankton biomass and higher trophic levels (as a food source).

Recent advancements in the use of remote sensing observations to inform ground level pollen dynamics

BACKGROUND AND AIM: Pollen information (e.g., pollen seasons and concentrations) is highly needed for allergy disease studies. However, in-situ pollen data from stations are still limited. Satellite data with repeated observations and global coverage provide the possibility to inform ground pollen dynamics over large areas. This study aims to improve the understanding of the relationship between vegetation phenology and pollen season, for its potential in developing fine spatiotemporal pollen maps over large areas. METHODS: We developed a framework to explore the relationship between the onset of vegetation phenology and pollen season. First, we derived pollen indicators using a double-Gaussian function and the collected pollen concentration data at five in-situ pollen stations. Next, we estimated vegetation phenology indicators and their interannual variability using satellite derived vegetation index. Finally, we explored the relationship between the indicators of pollen and phenology, and evaluated the scale effect of the relationship at the five stations during 2001–2015. RESULTS:Although indicators of pollen season (i.e., start of pollen season, SPS) and the vegetation phenology (i.e., start of season, SOS) vary spatially and temporally over stations, they exhibit consistent temporal trends during 2001-2015. SOS derived from finer Landsat satellite observations outperforms that from MODIS observations with a coarse spatial resolution. Our results indicate that satellite-derived SOS can serve as a proxy for pollen seasons in birch dominated areas. CONCLUSIONS:In this study, we explored the relationship between satellite-derived vegetation phenology and station-based pollen season at stations. Our results indicate that satellite-derived phenology can be used as an indicator for pollen dynamics over large areas, and therefore provides the possibility to assess the risk of respiratory allergies, especially in highly heterogeneous urban areas with high spatiotemporal satellite observations. KEYWORDS: Satellite remote sensing, Phenology, Start of Season, Pollen

Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.

All models of satellite-derived phenology are wrong, but some are useful: A case study from northern Australia

Satellite-derived phenology (or apparent phenology) is frequently used to illustrate changes in plant phenology (i.e. true phenology) and the effects of climate forcing. However, each study uses a different method to detect phenology. Plant phenology refers to the relationship between the life cycle of plants and weather and climate events. Phenology is often studied in the field, but recently studies have transitioned towards using satellite images to monitor phenology at the plot, country, and continental scales. The problem with this approach is that there is an ever-increasing variety of earth observation satellites collecting data with different spatial, spectral, and temporal characteristics. In this paper we ask if studies that detect phenology using different sensors over the same site produce comparable results. Mangrove forests are one example where different methods have been used to examine their apparent phenology. In general, plant phenology, including mangroves, is described using few individual plants, but continental-scale descriptions of phenological events are scarce or inexistent. Few attempts have been made to describe the phenology of mangroves using satellite imagery, and each study presents a different method. We hypothesize that apparent phenology changes with: 1) areal extent; 2) site location; 3) frequency of observation; 4) spatial resolution; 5) temporal coverage; and 6) the number of cloud contaminated observations. Intuitively, one would assume that these hypotheses hold true, yet few studies have investigated this. For example, one would expect that clouds change the observed phenology of vegetation, that the number of species captured at spatial resolution will impact the apparent phenology, or that mangroves in different places display different phenologies, but how are these changes represented in the apparent phenology? We use the Enhanced Vegetation Index (EVI) to examine the changes in the start of season and peak growing season dates, as well as the shape and amplitude of the apparent phenology in each hypothesis. We use Landsat and Sentinel 2 imagery over the mangrove forests in Darwin Harbour (Northern Territory, Australia) as a case study, and found that apparent phenology does change with the sensor, site, and cloud contamination. Importantly, the apparent phenology is comparable between Landsat and Sentinel 2 sensors, but it is not comparable to phenology derived from MODIS. This is due to differences in the spatial resolution of the sensors. Cloud contamination also significantly changes the apparent phenology of vegetation. In this paper we expose the complexity of modelling phenology with remote sensing and help guide future phenology investigations.

Trail camera networks provide insights into satellite-derived phenology for ecological studies

Repeat digital photography at or near ground-level is a proven and efficient approach for tracking plant phenology. Here, we explored the potential to monitor phenology using the Snapshot Wisconsin (SW) trail camera network, a citizen science program. Using three curve-fitting methods for characterizing phenological transition dates, we assessed the phenological offset between understory vegetation and the overstory canopy in the trailcam observations and compared variations in derived phenology over the different spatial scales represented by trailcams (~20–50 m), Harmonized Landsat and Sentinel-2 (HLS, 30 m), and Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m). Our results showed that the apparent phenological offset between understory and overstory vegetation differed among forest types: in broadleaf deciduous forests, understory vegetation had an earlier start-of-spring (SOS) and later end-of-autumn (EOA) than the overstory canopy; in mixed forests, the understory showed an earlier SOS than the overstory, but no significant difference in EOA; in evergreen conifer forests, neither SOS nor EOA differed significantly between the understory and overstory. We found moderate correlations (0.25 ≤ r ≤ 0.57) between trailcam- and satellite-derived phenological dates. Moreover, those derived dates varied significantly among the applied curve-fitting methods: total growing season length (from SOS to EOA) could be 19 days longer for a threshold-based method than for a logistic curve-fitting method (our reference model), but 17 days shorter than the logistic method when using a piecewise-continuous method based on fitted sine curves. Despite the spatial limitations of trailcams for characterizing phenology on landscape and regional scales, trailcam networks have considerable potential for informing local phenological studies and disentangling the many drivers of phenology that can remain undetected from the satellite perspective.

Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function

Seasonal phenological dynamics of vegetation hold important clues on ecosystem performance towards management goals, such as carbon uptake, and thus should be considered in projections of their targeted services. However, in wetlands spatio-temporal heterogeneity due to mixing of open water, soil, green and dead vegetation makes it difficult to generalize ecosystem functioning across different regions. Remote sensing observations can provide spatially-explicit, cost-effective phenology indicators; however, little is known about their capacity to indicate the links between wetland ecosystem structure and function. Here we assessed this potential by comparing one-year Enhanced Vegetation Index (EVI) from satellite products at high (5m; RapidEye) and low (30m; Landsat) spatial resolutions with eddy covariance time series of net carbon exchange, field digital camera (phenocam) greenness and water temperature among three floristically similar restored wetlands in California, USA. Phenological timing differed by wetland site: depending on satellite, the range in site-median start of greening was up to 28 days, end of greening – up to 73 days, start of senescence – up to 79 days, and end of senescence – up to 10 days. Key transition dates from satellite inputs agreed with seasonal changes in net carbon exchange, phenocam greenness and water temperatures, suggesting that phenological contrasts could result in part from site differences in vegetation configuration and litter affecting the exposure of canopy, soil and water to sunlight and thus sub-canopy microclimate and ecosystem functioning. Yet, the agreement between satellite inputs was non-systematic, with the greatest disparities at the more heterogeneous, less vegetated site. Phenological model fitting uncertainty increased with greater spatial resolution, highlighting the tradeoff between the accuracy of representing vegetation and the complexity of local seasonal variation. These findings highlight the sensitivity of satellite-derived phenology to structural and functional heterogeneity of ecosystems and call for more rigorous spatially-explicit analyses to inform assessments of restoration and management outcomes.

Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States

Satellite-derived phenology metrics are valuable tools for understanding broad-scale patterns and changes in vegetated landscapes over time. However, the extraction and interpretation of phenology in ecosystems with subtle growth dynamics can be challenging. US National Park Service monitoring of evergreen pinyon-juniper ecosystems in the western US revealed an unexpected winter-peaking phenological pattern in normalized difference vegetation index (NDVI) time-series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. In this paper, we assess the validity of the winter peaks through ground-based observation of phenology and examination of solar and satellite geometry effects. To test the premise of a true vegetation response, we analyzed NDVI values extracted from a time series of ground-based digital camera (‘phenocam’) images collected September 2017 to December 2018 in a pinyon-juniper woodland in Arizona, US. Results show pinyon and juniper growth peaked in the warm season, as did the other species in the phenocam field of view. NDVI time series from four other sensors (Landsat 7, Sentinel-2, VIIRS, and Proba-V) confirmed that winter peaks in this ecosystem are not limited to MODIS products. Examination of NDVI time series (2003–2018) derived from daily 250-m MODIS data in the broader pinyon-juniper ecosystem revealed that solar-to-sensor angle, sensor zenith angle, and forward/back-scatter reflectance explained >80% of intra-annual variability. Solar-to-sensor angle exerted the greatest control, and the direction of its correlation (positive) was the opposite of that which would be expected if it were driven by vegetation greenness. Solar-to-sensor angle is controlled seasonally by solar zenith angle and daily by variations in satellite overpass geometry. We mapped winter peaks across the western US in Google Earth Engine using 500-m MODIS MCD43A4 data, which correct for reflectance differences caused by view angle. In areas where winter vegetation peaks are ecologically improbable (i.e., locations with sub-freezing December temperatures), consistent winter peaks (≥ 14 years in 2003 to 2018) are widespread in both pinyon-juniper and non-pinyon-juniper conifer ecosystems; winter peaks are common (≥ 5 years in 2003 to 2018) across areas of shrubland. We attribute winter peaks to the positive correlation of NDVI with solar-to-sensor angle and solar zenith angle in combination with sparse, vertically oriented evergreen vegetation canopies. Increasing shadow visibility has been shown to increase overall NDVI, and the prevalence of the winter peaking in evergreen western sparse canopy ecosystems is consistent with this hypothesis. The extent of winter peaking patterns may have been previously overlooked due to temporal compositing, curve fitting, and incomplete snow screening.

Milk composition in a wild mammal: a physiological signature of phenological changes

Understanding how spring phenology influences early life can provide important insights into drivers of future development and survival. We combined unique, long-term data from a bighorn sheep population and satellite-derived phenology indices to quantify the relative importance of maternal and environmental influences on milk composition and lamb overwinter survival. Based on 216 milk samples from 34 females monitored over 6years, we found that longer snow-free and vegetation growing seasons increased milk fatty acid, iron and lactose concentrations. Structural equation modelling revealed no causality between milk energy content, lamb weaning mass and lamb overwinter survival. Our results suggest that spring conditions can affect milk energy content, but we did not detect any effect on lamb overwinter survival either directly or indirectly through lamb weaning mass. The effect of green-up date on milk composition and energy content suggests that herbivores living in seasonal environments, such as the bighorn sheep, might rely on a strategy intermediate between 'capital' and 'income' breeding when energy demands are high.

Delayed autumn leaf senescence date prolongs the growing season length of herbaceous plants on the Qinghai–Tibetan Plateau

Plant phenological shifts on the Qinghai–Tibetan Plateau (QTP) have been a hot research topic during the recent decade. Most previous studies based on remote sensing data suggest that earlier spring green-up is the main contributor to the prolonged vegetation growing season on the QTP. Given the large uncertainties of satellite-derived phenology, this study collected a large number (1455+ records) of long-term and continuous ground phenological observation data across the QTP. Based on these data, we conducted comprehensive analyses of temporal changes in spring green-up date (GUD), autumn leaf senescence date (LSD), and growing season length (GSL) of herbaceous plants from 1981 to 2017 through ordinary least squares regression and meta-analysis, and evaluated the relative contribution of shifts in GUD and LSD to change in GSL using the C-index. Results showed that the overall trends of GUD, LSD, and GSL insignificantly advanced at a rate of –0.17 days/decade, significantly delayed at a rate of 5.29 days/decade, and significantly lengthened at a rate of 5.46 days/decade, respectively. On average, the relative contribution of LSD shift to GSL change was 69.3% which was more than twice that of GUD shift (30.7%). Therefore, it was mainly the delayed LSD, rather than an advanced GUD, that prolonged GSL of QTP herbaceous plants, which was contrary to the prevailing conclusion reported by studies based on remote sensing data. Besides, among three functional groups of herbaceous plants, we found that change trends and patterns of the growing seasons seemed to be more favorable to the growth of forbs, which might shift community structure and further influence ecosystem function. Our study suggests that the importance of autumn phenology has been underestimated on the QTP, and more attention should be paid to the shift in autumn phenology and its influences on ecosystem structure and function under global warming.

Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. This article assesses the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data (5 cm spatial resolution, ~7 day temporal resolution) were acquired in tandem with an intensive ground campaign during the spring season of 2015 across a 15 ha mixed woodland. Phenophase transition dates were estimated at an individual tree-level using UAV time series of Normalized Difference Vegetation Index (NDVI) and Green Chromatic Coordinate (GCC) and validated against visual observations of tree phenology. UAV-derived start of season dates could be predicted with an accuracy of <1 week. The analysis was scaled to a plot level, where ground (visual assessment and understorey development), UAV and Landsat metrics were compared, indicating UAV data is effective for tracking canopy phenology, as opposed to ecosystem dynamics detected by satellites. The UAV data were used to automatically map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This, and a large temporal gap in the Landsat series, accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2 < 0.50) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, providing information which could improve characterization of vegetation phenology at multiple scales.

Evaluating tropical phytoplankton phenology metrics using contemporary tools

The timing of phytoplankton growth (phenology) in tropical oceans is a crucial factor influencing the survival rates of higher trophic levels, food web structure and the functioning of coral reef ecosystems. Phytoplankton phenology is thus categorised as an ‘ecosystem indicator’, which can be utilised to assess ecosystem health in response to environmental and climatic perturbations. Ocean-colour remote sensing is currently the only technique providing global, long-term, synoptic estimates of phenology. However, due to limited available in situ datasets, studies dedicated to the validation of satellite-derived phenology metrics are sparse. The recent development of autonomous oceanographic observation platforms provides an opportunity to bridge this gap. Here, we use satellite-derived surface chlorophyll-a (Chl-a) observations, in conjunction with a Biogeochemical-Argo dataset, to assess the capability of remote sensing to estimate phytoplankton phenology metrics in the northern Red Sea – a typical tropical marine ecosystem. We find that phenology metrics derived from both contemporary platforms match with a high degree of precision (within the same 5-day period). The remotely-sensed surface signatures reflect the overall water column dynamics and successfully capture Chl-a variability related to convective mixing. Our findings offer important insights into the capability of remote sensing for monitoring food availability in tropical marine ecosystems, and support the use of satellite-derived phenology as an ecosystem indicator for marine management strategies in regions with limited data availability.

Caracterización de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION

&lt;p align="justify"&gt;Land surface phenology from time series of satellite data are expected to contribute to improve the representation of vegetation phenology in earth system models. We characterized the baseline phenology of the vegetation at the global scale from GEOCLIM-LAI, a global climatology of leaf area index (LAI) derived from 1-km SPOT VEGETATION time series for 1999-2010. The calibration with ground measurements showed that the start and end of season were best identified using respectively 30% and 40% threshold of LAI amplitude values. The satellite-derived phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests in Europe, cherry in Asia and lilac shrubs in North America showed an overall root mean square error lower than 19 days for the start, end and length of season, and good agreement between the latitudinal gradients of VEGETATION LAI phenology and ground data.&lt;/p&gt;

Spatiotemporal dynamics of the climatic impacts on greenup date in the Tibetan Plateau

Studying the response of vegetation phenology to climate change at different temporal and spatial scales is important for understanding and predicting future terrestrial ecosystem dynamics and the adaptation of ecosystems to global change. In this study, satellite-derived phenology metrics were used to explore spatial and temporal changes in climatic influences on the start of the vegetation greenup season (SOS) across the Tibetan Plateau during 1982–2012. The results showed that SOS changed to a greater degree in areas where the SOS started earlier. In areas where the spring temperature (T-spring) increased at a faster rate, SOS was more likely to be advanced, and the faster the T-spring increase the more obvious the advance of SOS was. The T-spring had a stronger impact on SOS in relatively moist eco-geographic regions, while spring precipitation (P-spring) had a stronger impact on SOS in relatively warm regions. The results also indicated that P-spring effect on SOS tend to get weaker from west to east, while T-spring effect on SOS would produce no significant variation along both altitude and latitude. The T-spring effect on SOS became gradually stronger from west to east, while the P-spring effect on SOS tended to become weaker. In addition, the thermal impacts on SOS became weaker from 1982 to 2012 which may attribute to the vegetation acclimation to environment changes.

Improving North American terrestrial CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux diagnosis using spatial structure in land surface model residuals

Abstract. We evaluate spatial structure in North American CO2 flux observations using a simple diagnostic land surface model. The vegetation photosynthesis respiration model (VPRM) calculates net ecosystem exchange (NEE) using locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture. We use observed NEE from a group of 65 North American eddy covariance tower sites spanning North America to estimate VPRM parameters for these sites. We investigate spatial coherence in regional CO2 fluxes at several different time scales by using geostatistical methods to examine the spatial structure of model–data residuals. We find that persistent spatial structure does exist in the model–data residuals at a length scale of approximately 400 km (median 402 km, mean 712 km, standard deviation 931 km). This spatial structure defines a flux-tower-based VPRM residual covariance matrix. The residual covariance matrix is useful in constructing prior fluxes for atmospheric CO2 concentration inversion calculations, as well as for constructing a VPRM North American CO2 flux map optimized to eddy covariance observations. Finally (and secondarily), the estimated VPRM parameter values do not separate clearly by plant functional type (PFT). This calls into question whether PFTs can successfully partition ecosystems' fundamental ecological drivers when the viewing lens is a simple model.

Comparison of spatial patterns of satellite-derived and ground-based phenology for the deciduous broadleaf forest of China

Using spatial phenology model-based Ulmus pumila leaf unfolding and leaf fall data at 8 km × 8 km grids, we analysed spatial relationships between ground-based and satellite-derived growing season beginning and end dates during the period 2001–2005 and examined climatic controls on spatial correlations between ground-based and satellite-derived growing seasons. The results show that the regional mean satellite-derived growing season clearly started earlier and terminated slightly later than the regional mean ground-based growing season. Meanwhile, spatial patterns of satellite-derived growing season beginning and end dates correlate positively with spatial patterns of ground-based growing season beginning and end dates in each year (p < 0.001). Interannual variation in the difference of the slope of the spatial regression between ground-based/satellite-derived growing season beginning date and February–April temperature controls interannual variation of the spatial correlation coefficient between ground-based and satellite-derived growing season beginning date. In contrast, interannual variation of the spatial correlation coefficient between ground-based and satellite-derived growing season end date is not associated with interannual variation in the difference of the slope of the spatial regression between ground-based/satellite-derived growing season end date and September–November temperature.

Phenology shift from 1989 to 2008 on the Tibetan Plateau: an analysis with a process-based soil physical model and remote sensing data

Phenology is critical to ecosystem carbon quantification, and yet has not been well modeled considering both aboveground and belowground environmental variables. This is especially true for alpine and pan-arctic regions where soil physical conditions play a significant role in determining the timing of phenology. Here we examine how the spatiotemporal pattern of satellite-derived phenology is related to soil physical conditions simulated with a soil physical model on the Tibetan Plateau for the period 1989-2008. Our results show that spatial patterns and temporal trends of phenology are parallel with the corresponding soil physical conditions for different study periods. On average, 1 °C increase in soil temperature advances the start of growing season (SOS) by 4.6 to 9.9 days among

The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests

Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites of satellite data, ranging from 8 days to 16 days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with in situ leaf unfolding (LU) of trembling aspen ( Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date. In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to in situ LU dates ranged between 13 and 40 days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (~ 13 to 20 days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to − 5 days) than the SOS estimates from NDVI composites that use the midpoint (− 2 to − 27 days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both, SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28 days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing in situ phenology networks to improve the validation of satellite derived green-up dates.

Mapping crop key phenological stages in the North China Plain using NOAA time series images

Six key phenological stages were defined based on NOAA/AVHRR NDVI time series data collected in the Huang-Huai-Hai (HHH) Plain of China from 1990 through 2000. In a winter wheat-summer maize rotation, the recovering, heading and maturity stages of winter wheat and the emergence, tasseling and maturity stages of summer maize were recorded using 6km resolution 10-day composite NDVI. The satellite-derived data proved to be consistent with the ‘green wave’ moving through the HHH Plain in spring. The recovering stage of winter wheat recorded by satellite was closely correlated to the temperatures measured in February whereas summer maize yields (at zone level) were correlated well with the satellite-derived length of the crop cycle. Comparison with synchronous phenological observations on the ground confirmed the coherence of satellite-derived phenology data. It is expected that satellite data with greater spatial and temporal resolutions and improved smoothing methods will increase the precision of the estimated data still further.

Monitoring Phenological Key Stages and Cycle Duration of Temperate Deciduous Forest Ecosystems with NOAA/AVHRR Data

In this study we attempted to monitor two main key stages in the phenological cycle of deciduous forests—budburst and senescence—using the normalized difference vegetation index (NDVI) derived from NOAA/AVHRR. These stages induce rapid (time scale of a month), large ( >0.3) and nearly linear NDVI variations. The method we developed consists of a fit of NDVI predicted by line segment to AVHRR-NDVI time series. It made it possible to derive the budburst and senescence timing, and then the phenological cycle duration. A relationship found in the literature between leaf area index (LAI) and NDVI showed that LAI was about 1 for the satellite-derived budburst and about 1.5 for the satellite-derived senescence. We tested the method on three nearly monospecific ( Quercus petraea and Fagus sylvatica L.) forests located in France using a 6-year NOAA/AVHRR archive during the 1989–1994 period. The satellite-derived phenology revealed differences in relation to the composition and the climatic features of the study areas: 1) the phenological cycle duration of oak was longer (34 days) for southern than for northern forests, 2) in the North of France, beech trees were budding earlier (5.6 days) than oak trees, 3) the interannual variability of budburst was significantly lower for beech than for oak trees. The comparison with ground phenological observations found in the literature showed the spatio-temporal coherence of the satellite-derived phenology. A good correlation was also found between the satellite-derived budburst and the budburst timing predicted from air temperatures using the thermal time model. These tests provided a first validation of the method we developed to monitor the phenological cycle of deciduous forests with NOAA/AVHHR data. The limitations of the method and the perspectives for modeling temperate deciduous ecosystems are finally discussed.