Green-up Date Research Articles

Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data

Vegetation phenology not only describes the life cycle events of periodic plants during the growing season but also acts as an indicator of biological responses to climate change. Satellite monitoring of vegetation phenology can capture the spatial patterns of vegetation dynamics at global scales. However, the existing satellite products of global vegetation phenology still show uncertainties in estimating phenological metrices, especially for dormancy onset. The Second-Generation Global Imager (SGLI) onboard the satellite Global Change Observation Mission—Climate (GCOM-C) that launched in 2017 provides a new opportunity to improve the estimation of global vegetation phenology with a spatial resolution of 250 m. In this study, SGLI land surface reflectance data were employed to estimate the green-up and dormancy dates for different vegetation types based on a relative threshold method, in which a snow-free vegetation index (i.e., the normalized difference greenness index, NDGI) was adopted. The validation results show that there are significant agreements between the trajectories of the SGLI-based NDGI and the near-surface green color coordinate index (GCC) at the PhenoCam sites with different vegetation types. The SGLI-based estimation of the green-up dates slightly outperformed that of the existing MODIS and VIIRS phenology products, with an RMSE and R2 of 11.0 days and 0.71, respectively. In contrast, the estimation of the dormancy dates based on the SGLI data yielded much higher accuracies than the MODIS and VIIRS products, with an RMSE decreased from >23.8 days to 15.6 days, and R2 increased from <0.51 to 0.72. These results suggest that GCOM-C/SGLI data have the potential to generate improved monitoring of global vegetation phenology in the future.

Effects of climatic and cultivar changes on winter wheat phenology in central Lithuania.

It is essential to understand how climate change and varieties affect crop phenology and yields to adapt to future climate change. The aim of this study was to analyse the phenological development trends of three winter wheat cultivars (1990-2020) to identify the most critical meteorological-climatic factors influencing the development and yield of the cultivars and to investigate the heat requirements for each phenological phase to reveal the potential of the different cultivars to adapt to the warming climate. The observed dates of green-up, the beginning of stem elongation, and the grain development advanced significantly, but the timing of maturity changed insignificantly during the period of 1990-2020. The most marked change was related to the shortening of the period from sowing to green-up. The green-up dates were related to the mean temperature of the period after sowing. The occurrence of stem elongation and grain development dates were negatively correlated with the mean temperature in May. Significant correlations were found between temperature and duration from sowing to green-up and positive from stem elongation to grain development. The change of cultivar led to earlier green-up and grain development dates, but cultivar choise had no influence on sowing, stem elongation, and maturity dates from 1990 to 2020. The newer cultivar Skagen was more successful in exploiting increased thermal resources. The heat requirements remained almost unchanged during the vegetative development period, while the heat amount required during the reproductive period increased by about 15%. These findings demonstrate that the choice of crop cultivars with higher thermal requirements may be an appropriate adaptation mean to achieve higher yields in response to climate change, at least in the middlelatitudes.

Impact of Snow Cover Phenology on the Vegetation Green-Up Date on the Tibetan Plateau

Variations in snow cover resulting from global warming inevitably affect alpine vegetation growth on the Tibetan Plateau (TP), but our knowledge of such influences is still limited. Here, we investigated the relationship between snow cover and alpine vegetation during 2003–2020 on the TP using the satellite-derived vegetation green-up date (GUD) and metrics of snow cover phenology, namely the snow cover onset date (SCOD), snow cover end date (SCED), snow cover duration (SCD), and snowmelt onset date (SMOD). In this study, we first analyzed the spatiotemporal changes in the GUD and the snow cover phenology metrics on the TP. Pearson’s correlation, gray relation analysis, and linear regression were then employed to determine the impact of snow cover phenology on the GUD. Overall, with the SCOD, SCED, and SMOD delayed by one day, the GUD was advanced by 0.07 and 0.03 days and was postponed by 0.32 days, respectively, and a one-day extension of the SCD resulted in a 0.04-day advance in the GUD. In addition, the roles of vegetation type, topography, and climate factors (temperature and precipitation) in modulating the relationships between snow cover phenology and the GUD were evaluated. The GUD of alpine steppes was negatively correlated with the SCOD and SCED, contrary to that of the other vegetation types. The GUD of alpine steppes was also more sensitive to snow cover phenology than that of other vegetation types. The increase in elevation generally enhanced the sensitivity of the GUD to snow cover phenology. The GUD showed a stronger negative sensitivity to the SCD in warmer areas and a stronger positive sensitivity to the SMOD in wetter areas. Our findings revealed the essential impact of variation in snow cover phenology on the GUD and indicated the complex interference of environmental factors in the relationship between snow cover and vegetation growth.

Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai–Tibetan Plateau from 2000–2020

As the largest and highest alpine ecoregion in the world, the Qinghai–Tibetan Plateau (QTP) is extremely sensitive to climate change and has experienced extraordinary warming during the past several decades; this has greatly affected various ecosystem processes in this region such as vegetation production and phenological change. Therefore, numerous studies have investigated changes in vegetation dynamics on the QTP using the satellite-derived normalized-difference vegetation index (NDVI) time-series data provided by the Moderate-Resolution Imaging Spectroradiometer (MODIS). However, the highest spatial resolution of only 250 m for the MODIS NDVI product cannot meet the requirement of vegetation monitoring in heterogeneous topographic areas. In this study, therefore, we generated an 8-day and 30 m resolution NDVI dataset from 2000 to 2020 for the QTP through the fusion of 30 m Landsat and 250 m MODIS NDVI time-series data. This dataset, referred to as QTP-NDVI30, was reconstructed by employing all available Landsat 5/7/8 images (>100,000 scenes) and using our recently developed gap-filling and Savitzky–Golay filtering (GF-SG) method. We improved the original GF-SG approach by incorporating a module to process snow contamination when applied to the QTP. QTP-NDVI30 was carefully evaluated in both quantitative assessments and visual inspections. Compared with reference Landsat images during the growing season in 100 randomly selected subregions across the QTP, the reconstructed 30 m NDVI images have an average mean absolute error (MAE) of 0.022 and a spatial structure similarity (SSIM) above 0.094. We compared QTP-NDVI30 with upscaled cloud-free PlanetScope images in some topographic areas and observed consistent spatial variations in NDVI between them (averaged SSIM = 0.874). We further examined an application of QTP-NDVI30 to detect vegetation green-up dates (GUDs) and found that QTP-NDVI30-derived GUD data show general agreement in spatial patterns with the 250 m MODIS GUD data, but provide richer spatial details (e.g., GUD variations at the subpixel scale). QTP-NDVI30 provides an opportunity to monitor vegetation and investigate land-surface processes in the QTP region at fine spatiotemporal scales.

Precipitation versus temperature as phenology controls in drylands.

Cycles of plant growth, termed phenology, are tightly linked to environmental controls. The length of time spent growing, bounded by the start and end of season, is an important determinant of the global carbon, water, and energy balance. Much focus has been given to global warming and consequences for shifts in growing-season length in temperate regions. In conjunction with warming temperatures, altered precipitation regimes are another facet of climate change that have potentially larger consequences than temperature in dryland phenology globally. We experimentally manipulated incoming precipitation in a semiarid grassland for over a decade and recorded plant phenology at the daily scale for 7 years. We found precipitation to have a strong relationship with the timing of grass greenup and senescence but temperature had only a modest effect size on grass greenup. Pre-season drought strongly resulted in delayed grass greenup dates and shorter growing-season lengths. Spring and summer drought corresponded with earlier grass senescence, whereas higher precipitation accumulation over these seasons corresponded with delayed grass senescence. However, extremely wet conditions diluted this effect and caused a plateaued response. Deep-rooted woody shrubs showed few effects of variable precipitation or temperature on phenology and displayed consistent annual phenological timing compared with grasses. Whereas rising temperatures have already elicited phenological consequences and extended growing-season length for mid and high-latitude ecosystems, precipitation change will be the major driver of phenological change in drylands that cover 40% of the land surface with consequences for the global carbon, water, and energy balance.

Satellite-Based Monitoring on Green-Up Date for Optimizing the Rest-Grazing Period in Xilin Gol Grassland

Ecological degradation has occurred in global grasslands and has impaired their ecosystem services severely, so ecological conservation of grasslands should be focused more on the effectiveness of management measures. The green-up process decides the year-round forage yield and ecological conditions of grasslands. Adopting rest-grazing during the green-up process can guarantee a successful green-up, thus realizing more economic benefits without grassland degradation. Therefore, studies should pay more attention to whether the green-up process is really covered by the rest-grazing period or not. We analyze the spatiotemporal variations and the stability of the annual green-up date in Xilin Gol Grassland from 2000 to 2018 based on MODIS time series images and compare the green-up date with the rest-grazing period to assess the effectiveness of the rest-grazing policy. The results show that the green-up date of Xilin Gol Grassland had advanced 15 days on average because of the increasing trend of both temperature and precipitation during 2000~2018. The green-up date is mostly 120~130 d in the east, about 10 days earlier than the west (130~140 d) and 20 days earlier than in the central areas (140~150 d), also because of the spatial variations of temperature and precipitation. The coefficient of variation (CV) of the green-up date showed a significant negative correlation with precipitation, so the green-up date is more unstable in the arid areas. The rest-grazing period started more than 45 days earlier than the green-up date and failed to cover it in several years, which occurred more frequently in southern counties. The average green-up date appeared after rest-grazing started in over 98% of areas, and the time gap is 15~45 days in 88% of areas, which not only could not avoid grassland degradation effectively but also increased herdsmen’s life burden. This study aims to accurately grasp the temporal and spatial variations of the green-up date in order to provide references for adjusting a more proper rest-grazing period, thus promoting ecological conservation and sustainable development of animal husbandry.

Spatial and Temporal Evolution of Sowing and the Onset of the Rainy Season in a Region of Large Agricultural Expansion in Brazil

In order to assist in high-yield agricultural management in multiple cropping systems, it is essential to understand the link between the rainy season onset and crops sowing dates, since it considerably affects the management, yield and output. We built crop calendars derived from remote sensing products and investigated the link between sowing dates and the onset of the rainy season in irrigated and rainfed agriculture in Western Bahia, a new and important agricultural frontier in Brazilian Cerrado. Crop sowing dates were obtained from green-up dates from 2001 to 2019. Rainy season onset dates were determined using CHIRPS daily precipitation data. Results indicate that sowing occurs from 26 October to 15 November and the rainy season starts from 17 to 27 October. Rainfed sowing dates are strongly correlated to rainy season onset and are particularly affected in years where rains are delayed. Sowing dates in irrigated pixels occur up to 25 days earlier than rainfed and are not correlated to rainy season onset. Irrigated farms are sowing earlier and in a shorter window than rainfed, with a stronger resilience in years where rains are delayed, and have adapted their sowing operation towards a more intensive agriculture and efficient water use during the rainy season.

Evaluation of Vegetation Indexes and Green-Up Date Extraction Methods on the Tibetan Plateau

The vegetation green-up date (GUD) of the Tibetan Plateau (TP) is highly sensitive to climate change. Accurate estimation of GUD is essential for understanding the dynamics and stability of terrestrial ecosystems and their interactions with climate. The GUD is usually determined from a time-series of vegetation indices (VIs). The adoption of different VIs and GUD extraction methods can lead to different GUDs. However, our knowledge of the uncertainty in these GUDs on TP is still limited. In this study, we evaluated the performance of different VIs and GUD extraction methods on TP from 2003 to 2020. The GUDs were determined from six Moderate Resolution Imaging Spectroradiometer (MODIS) derived VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), phenology index (PI), normalized difference phenology index (NDPI), and normalized difference greenness index (NDGI). Four extraction methods (βmax, CCRmax, G20, and RCmax) were applied individually to each VI to determine GUD. The GUDs obtained from all VIs showed similar patterns of early green-up in the eastern and late green-up in the western plateau, and similar trend of GUD advancement in the eastern and postponement in the western plateau. The accuracy of the derived GUDs was evaluated by comparison with ground-observed GUDs from 19 agrometeorological stations. Our results show that two snow-free VIs, NDGI and NDPI, had better performance in GUD extraction than the snow-calibrated conventional VIs, NDVI and EVI. Among all the VIs, NDGI gave the highest GUD accuracy when combined with the four extraction methods. Based on NDGI, the GUD extracted by the CCRmax method was found to have the highest consistency (r = 0.62, p < 0.01, RMSE = 11 days, bias = −3.84 days) with ground observations. The NDGI also showed the highest accuracy for preseason snow-covered site-years (r = 0.71, p < 0.01, RMSE = 10.69 days, bias = −4.05 days), indicating its optimal resistance to snow cover influence. In comparison, NDII and PI hardly captured GUD. NDII was seriously affected by preseason snow cover, as indicated by the negative correlation coefficient (r = −0.34, p < 0.1), high RMSE and bias (RMSE = 50.23 days, bias = −24.25 days).

Characterizing Spatial Patterns of the Response Rate of Vegetation Green-Up Dates to Land Surface Temperature in Beijing, China (2001–2019)

The phenology indicator of vegetation green-up dates (GUD) is prone to being affected by changes in temperature. However, the influencing degree of urbanization-induced temperature warming on vegetation GUDs among different vegetation species along the urban-rural gradient remains inadequately described. In this study, based on the long-term (2001–2019) satellite-derived vegetation GUDs and nighttime land surface temperature (LST) of forests, grasslands, and croplands along the urban-rural gradient with Beijing (China) as a case study area, the responses of vegetation GUDs to temperature changes were quantitatively analyzed, taking into account the vegetation types and distances away from the urban domain. The results show that (1) long-term GUDs and LST are significantly negatively correlated, characterized by a weaker significant correlation near the urban area when compared with its surrounding areas, with the greatest absolute linear correlation coefficients (r) happening at rings 32 km (rmax = −0.93, forests), 20 km and 48 km (rmax = −0.83, grasslands), and 34 km (rmax = −0.82, croplands), respectively; (2) the magnitude of change in GUDs over the past 19 year (2001–2019) are significantly positively correlated with these in LST near the urban area, demonstrating a distance-decay trend, with the greatest advance in GUDs occurring at the ring nearest the urban area, by about 20 days (forests), 24.5 days (grasslands), and 15.6 days (croplands), respectively; (3) the spatial pattern of the response rate of GUDs change to LST change (days K−1) also showed a declining trend with distance, with GUD advanced by 6.8 days K−1 (forests), 7.5 days K−1 (grasslands), and 4.9 days K−1 (croplands) at the closest ring to the urban, decreasing to about 2.3 days K−1 (48 km), 4.1 days K−1 (18 km), and 1 day K−1 (18 km), respectively, indicating a notable influence of temperature warming on vegetation GUDs near the urban domains.

Species differences in the green-up date of typical vegetation in Inner Mongolia and climate-driven mechanism based on process-based phenology models

Different species within the same community may exhibit distinct phenological responses to climate change, so it is necessary to study species differences in the green-up date among abundant species within a wide area, and a suitable phenology model should be introduced to explain the associated climate-driven mechanism. Although various models have been developed, very few studies have aimed to compare their efficiency and robustness, and the relative contributions of climate driving factors have not been sufficiently examined. We analyzed phenology data for 12 species across 17 stations in Inner Mongolia and found that essential spatiotemporal and interspecies differences existed in the green-up date. Five process-based models were established for each species and their performance was comprehensively evaluated. The two-phase models (sequential model, parallel model, unified model and unified model combined with precipitation driving) generally performed better than the one-phase model (thermal time model), and the model considering precipitation performed the best, which indicates that it is necessary to introduce the chilling effect and precipitation driving effect to improve the model accuracy in arid environments. We proposed a method to estimate the contribution rates of various climate driving factors, and significant differences in the relative demand for the various climate driving factors among different species were clearly revealed. The results indicated that for natural vegetation in Inner Mongolia, the need for the chilling and temperature driving is relatively high, and the precipitation driving is very important for herbaceous vegetation, which leads to considerable spatial and interspecies differences in green-up date. We demonstrated the feasibility of quantitatively evaluating the contributions of different climate driving factors with a process-based model, and the contradiction in phenological changes among different studies may eventually be clarified.

Increasing Interspecific Difference of Alpine Herb Phenology on the Eastern Qinghai-Tibet Plateau.

The phenology of alpine grassland on the Qinghai–Tibet Plateau (QTP) is critical to regional climate change through climate–vegetation feedback. Although many studies have examined QTP vegetation dynamics and their climate sensitivities, the interspecific difference in the phenology response to climate change between alpine species is poorly understood. Here, we used a 30-year (1989–2018) record of in situ phenological observation for five typical alpine herbs (Elymus nutans, Kobresia pygmaea, Plantago asiatica, Puccinellia tenuiflora, and Scirpus distigmaticus) and associated climatic records at Henan Station in the eastern QTP to examine the species-level difference in spring and autumn phenology and then quantify their climate sensitivities. Our results show that with significantly warming, the green-up dates of herbs were insignificantly shifted, while the brown-off dates in four out of the five herbs were significantly delayed. Meanwhile, the interspecific difference in brown-off dates significantly increased at a rate of 0.62 days/annual from 1989 to 2016, which was three times larger than that in green-up dates (0.20 days/annual). These diverse rates were attributed to the different climate controls on spring and autumn phenology. In particular, green-up dates in most herbs were sensitive to mean surface temperature, while brown-off dates were sensitive to the night surface temperature. Furthermore, brown-off dates are less sensitive to the warming in high ecological niche (with higher herb height and aboveground biomass) herbs than low niche herbs (with lower herb height and aboveground biomass). The increased phenology interspecific difference highlights the complex responses of herbs to future climate change even under the same alpine environment and indicates a potential alternation in the plants community of alpine QTP, which may further influence the regional climate–vegetation feedback.

A generalized model to predict large-scale crop yields integrating satellite-based vegetation index time series and phenology metrics

Convenient and reliable large-scale crop yield prediction is needed when formulating administrative plans and ensuring food security, especially under changing climate and international conditions. In this study, we explored Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices- and phenology-based yield prediction generalization model taking the US Corn Belt as an example. We calculated the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2) time series, which were adjusted using greenup dates derived from the Land Cover Dynamics product MCD12Q2. Based on the adjusted VI (NDVI, EVI2) time series, the VI change rate (dVI) time series was calculated, which represents crop growth rate. The first step was to cluster the adjusted VI and dVI time series, called ‘greenup groups’, according to corresponding greenup dates with a five-day interval. Then in different greenup groups, we constructed empirical univariate models with VI having the maximum correlation with crop yield, and multivariate models with VI and dVI, which were also used to construct the generalized model. After clustering, the days with maximum VI correlation gradually decreased as greenup days increasing, and the univariate VI model and multivariate VI and dVI model performances in different groups improved. The generalized models with specific VI and dVI variables in each group predicted yields of corn and soybean with R2 values mainly ranging from 0.55 to 0.75 and 0.55 to 0.70, while RMSE mainly ranging from 1000 to 1500 kg/ha and 300 to 400 kg/ha for both NDVI and EVI2 from 2008 to 2018 with leave-one-year-out cross-validation for all groups. The model using MODIS data was convenient and scalable with limited data requirements and date-determined variables after greenup, and offered a generalized method to predict crop yields at a large scale before harvest with good performance.

Change in the elevational pattern of vegetation greenup date across the Tianshan Mountains in Central Asia during 2001–2020

Variations in land surface phenology (LSP) along elevation gradients strongly impact human life and wildlife species distribution across the Tianshan Mountains (TM) in arid and semiarid Central Asia. However, changes in the elevational patterns of LSP in recent decades have not been well understood for the TM. Here, we characterized changes in vegetation greenup date (GUD) and its elevational pattern in the TM and five subregions during 2001–2020, with Moderate Resolution Imaging Spectroradiometer (MODIS) time series of the enhanced vegetation index (EVI). Impacts of land surface temperature (LST) and precipitation on GUD changes were also examined. The results show that GUD changes across the TM were mostly nonsignificant (P > 0.05). Approximately 13.4% of the region experienced significant advance in GUD. Furthermore, GUD at low and middle elevations (approximately 1000–2500 m) showed greater proportions of significantly earlier trends. This elevation dependence of GUD changes led to altered elevational patterns of GUD. First, most GUD isolines showed shifts toward higher elevations, and the GUD isolines of day of year (DOY) 110 and 120 located at low and middle elevations exhibited greater mean elevational shifts than those of others for most subregions. Specifically, the mean elevation of the DOY 110 isolines moved from approximately 1325 m to 2126 m in a subregion. Second, increased GUD elevation gradients were observed in several subregions. The spatial pattern of GUD trends may be primarily caused by the increased LST in April, particularly nighttime LST. The results provide information for rangeland management in the context of rangeland degradation across the TM.

Sensitivity of Green-Up Date to Meteorological Indicators in Hulun Buir Grasslands of China

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future.

Species Difference in the Green-Up Date of Typical Vegetation in Inner Mongolia and Climate Driving Mechanism Based on Process-Based Phenology Models

Species Difference in the Green-Up Date of Typical Vegetation in Inner Mongolia and Climate Driving Mechanism Based on Process-Based Phenology Models

Contrasting Roles of Background Climatic Conditions between Non-Growing and Growing Seasons in Determining Spatial Variation in Temperature Sensitivity of Green-Up Date on Tibetan Plateau

Contrasting Roles of Background Climatic Conditions between Non-Growing and Growing Seasons in Determining Spatial Variation in Temperature Sensitivity of Green-Up Date on Tibetan Plateau

Greenup dates change across a temperate forest-grassland ecotone in northeastern China driven by spring temperature and tree cover

Changes in climate and land cover are potential drivers of land surface phenology changes. Here, we investigate whether subpixel percent tree cover (PTC) change is an important driver of trends in satellite derived vegetation spring greenup date (GUD) across the Hulunbuir temperate forest-grassland ecotone in northeastern China. GUD was estimated using the MODIS-derived enhanced vegetation index time series during 2001–2020 with a spatial resolution of 500 m. To understand the influential mechanisms of PTC on GUD, we examined relationships between the spatial variations in GUD and PTC at multiple spatio-temporal extents. Forested pixels with greater PTC were found to have generally earlier GUDs for all forest types. The GUD of forests was also generally earlier than that of grassland. On the other hand, we observed approximately 23.7% and 1.2% significantly earlier and later trends in GUD across the region, respectively. Meanwhile, widespread increases in preseason land surface temperature (LST) and PTC were detected. Both increases in LST and PTC contributed to the earlier GUD in the forested region. Specifically, we found negative correlations (Spearman correlation coefficient -0.17 to -0.55) between the change slopes of GUD and PTC in every forest and grassland type. The results highlight the important impacts of subpixel PTC on GUD variations, and improve the understanding of ecosystem changes under the effects of climate and human activities (e.g., afforestation) over the Hulunbuir temperate forest-grassland ecotone.

Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.

Vegetation green-up date is more sensitive to permafrost degradation than climate change in spring across the northern permafrost region.

Global climate change substantially influences vegetation spring phenology, that is, green-up date (GUD), in the northern permafrost region. Changes in GUD regulate ecosystem carbon uptake, further feeding back to local and regional climate systems. Extant studies mainly focused on the direct effects of climate factors, such as temperature, precipitation, and insolation; however, the responses of GUD to permafrost degradation caused by warming (i.e., indirect effects) remain elusive yet. In this study, we examined the impacts of permafrost degradation on GUD by analyzing the long-term trend of satellite-based GUD in relation to permafrost degradation measured by the start of thaw (SOT) and active layer thickness (ALT). We found significant trends of advancing GUD, SOT, and thickening ALT (p<0.05), with a spatially averaged slope of -2.1days decade-1 , -4.1days decade-1 , and +1.1cm decade-1 , respectively. Using partial correlation analyses, we found more than half of the regions with significantly negative correlations between spring temperature and GUD became nonsignificant after considering permafrost degradation. GUD exhibits dominant-positive (37.6% vs. 0.6%) and dominant-negative (1.8% vs. 35.1%) responses to SOT and ALT, respectively. Earlier SOT and thicker ALT would enhance soil water availability, thus alleviating water stress for vegetation green-up. Based on sensitivity analyses, permafrost degradation was the dominant factor controlling GUD variations in 41.7% of the regions, whereas only 19.6% of the regions were dominated by other climatic factors (i.e., temperature, precipitation, and insolation). Our results indicate that GUDs were more sensitive to permafrost degradation than direct climate change in spring among different vegetation types, especially in high latitudes. This study reveals the significant impacts of permafrost degradation on vegetation GUD and highlights the importance of permafrost status in better understanding spring phenological responses to future climate change.

Using remote sensing to monitor the spring phenology of Acadia National Park across elevational gradients

AbstractGreenup dates and their responses to elevation and temperature variations across the mountains of Acadia National Park are monitored using remote sensing data, including Landsat 8 surface reflectances (at a 30‐m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500‐m spatial resolution), during the 2013–2016 growing seasons. The 30‐m resolution provides a better scale for studying the phenology variation across elevational gradients than the 500‐m resolution, as greenup dates monitored at 30‐m scale have better agreement with leaf‐out dates recorded in the field alongside the north–south‐oriented hiking trails on three of the park’s tallest mountains (466 m, 418 m, and 380 m), and can provide landcover‐specific analysis. The spring phenology responses to temperature and elevation vary among different spatial scales. Greenup dates of Acadia National Park monitored at 30‐m scale show a weak advancing trend with higher spring temperature, while greenup dates monitored at 500 m show a weak delaying trend. The species mix within landcover at 30‐m scale could weaken the advancing trend detected at field observation level. The landcover mix and elevation variation within 500‐m scale could alter the spring phenology response to spring temperature variation. Greenup dates monitored at both 30‐m and 500‐m scales vary among different elevational zones, aspects, landcovers, and years. However, the relationship between greenup dates and elevation is rather weak.