Search Results (7)

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. Full article

As a sensitive indicator for climate change, the spring phenology of alpine grassland on the Qinghai–Tibet Plateau (QTP) has received extensive concern over past decade. It has been demonstrated that temperature and precipitation/snowfall play an important role in driving the green-up in alpine grassland. However, the spatial differences in the temperature and snowfall driven mechanism of alpine grassland green-up onset are still not clear. This manuscript establishes a set of process-based models to investigate the climate variables driving spring phenology and their spatial differences. Specifically, using 500 m three-day composite MODIS NDVI datasets from 2000 to 2015, we first estimated the land surface green-up onset (LSGO) of alpine grassland in the QTP. Further, combining with daily air temperature and precipitation datasets from 2000 to 2015, we built up process-based models for LSGO in 86 meteorological stations in the QTP. The optimum models of the stations separating climate drivers spatially suggest that LSGO in grassland is: (1) controlled by temperature in the north, west and south of the QTP, where the precipitation during late winter and spring is less than 20 mm; (2) driven by the combination of temperature and precipitation in the middle, east and southwest regions with higher precipitation and (3) more likely controlled by both temperature and precipitation in snowfall dominant regions, since the snow-melting process has negative effects on the air temperature. The result dictates that snowfall and rainfall should be concerned separately in the improvement of the spring phenology model of the alpine grassland ecosystem. Full article

Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R² from 0.66 to 0.85) than NDVI (R² from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather. Full article

This study aims to monitor spatiotemporal changes of spring phenology using the green-up start dates based on the accumulated growing degree days (AGDD) and the enhanced vegetation index (EVI), which were deducted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data. The green-up start dates were extracted from the MODIS-derived AGDD and EVI for 30 Mongolian oak (Quercus mongolica Fisch.) stands throughout South Korea. The relationship between green-up day of year needed to reach the AGDD threshold (DoY_AGDD) and air temperature was closely maintained in data in both MODIS image interpretation and from 93 meteorological stations. Leaf green-up dates of Mongolian oak based on the AGDD threshold obtained from the records measured at five meteorological stations during the last century showed the same trend as the result of cherry observed visibly. Extrapolating the results, the spring onset of Mongolian oak and cherry has become earlier (14.5 ± 4.3 and 10.7 ± 3.6 days, respectively) with the rise of air temperature over the last century. The temperature in urban areas was consistently higher than that in the forest and the rural areas and the result was reflected on the vegetation phenology. Our study expanded the scale of the study on spring vegetation phenology spatiotemporally by combining satellite images with meteorological data. We expect our findings could be used to predict long-term changes in ecosystems due to climate change. Full article

Knowledge of how rainfall seasonality affects land surface phenology has important implications on understanding ecosystem resilience to future climate change in the Congo Basin. We studied the impacts of land cover on the response of the canopy greenness cycle (CGC) to the rainy season in the Congo Basin on a yearly basis during 2006–2013. Specifically, we retrieved CGC from the time series of two-band enhanced vegetation index (EVI2) acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI). We then detected yearly onset (ORS) and end (ERS) of the rainy season using a modified Climatological Anomalous Accumulation (CAA) method based on the daily rainfall time series provided by the Tropical Rainfall Measurement Mission. We further examined the timing differences between CGC and the rainy season across different types of land cover, and investigated the relationship between spatial variations in CGC and rainy season timing. Results show that the rainy season in the equatorial Congo Basin was regulated by a distinct bimodal rainfall regime. The spatial variation in the rainy season timing presented distinct latitudinal gradients whereas the variation in CGC timing was relatively small. Moreover, the inter-annual variation in the rainy season timing could exceed 40 days whereas it was predominantly less than 20 days for CGC timing. The response of CGC to the rainy season varied with land cover. The lead time of CGC onset prior to ORS was longer in tropical woodlands and forests, whereas it became relatively short in grasslands and shrublands. Further, the spatial variation in CGC onset had a stronger correlation with that of ORS in grasslands and shrublands than in tropical woodlands and forests. In contrast, the lag of CGC end behind ERS was widespread across the Congo Basin, which was longer in grasslands and shrublands than that in tropical woodlands and forests. However, no significant relationship was identified between spatial variations in ERS and CGC end. Full article

Land surface phenology (LSP), the study of the timing of recurring cycles of changes in the land surface using time-series of satellite sensor-derived vegetation indices, is a valuable tool for monitoring vegetation at global and continental scales. Characterisation of LSP and its spatial variation is required to reveal and predict ongoing changes in Earth system dynamics. This study presents and analyses the LSP of the pan-European continent for the last decade, considering three phenological metrics: onset of greenness (OG), end of senescence (EOS), and length of season (LS). The whole time-series of Multi-temporal Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data at 1 km spatial resolution was used to estimate the phenological metrics. Results show a progressive pattern in phenophases from low to high latitudes. OG dates are distributed widely from the end of December to the end of May. EOS dates range from the end of May to the end of January and the spatial distribution is generally the inverse of that of the OG. Shorter growing seasons (approximately three months) are associated with rainfed croplands in Western Europe, and forests in boreal and mountainous areas. Maximum LS values appear in the Atlantic basin associated with grasslands. The LSP maps presented in this study are supported by the findings of a previous study where OG and EOS estimates were compared to those of the pan-European phenological network at certain locations corresponding to numerous observations of deciduous tree plant species. Moreover, the spatio-temporal pattern of the OG and EOS produced close agreement with the dates of deciduous tree leaf unfolding and autumnal colouring, respectively (pseudo R-squared equal to 0.70 and 0.71 and root mean square error of six days (over 365 days)). Full article

Mapping irrigated areas of a river basin is important in terms of assessing water use and food security. This paper describes an innovative remote sensing based vegetation phenological approach to map irrigated areas and then the differentiates the ground water irrigated areas from the surface water irrigated areas in the Krishna river basin (26,575,200 hectares) in India using MODIS 250 meter every 8-day near continuous time-series data for 2000–2001. Temporal variations in the Normalized Difference Vegetation Index (NDVI) pattern obtained in irrigated classes enabled demarcation between: (a) irrigated surface water double crop, (b) irrigated surface water continuous crop, and (c) irrigated ground water mixed crops. The NDVI patterns were found to be more consistent in areas irrigated with ground water due to the continuity of water supply. Surface water availability, on the other hand, was dependent on canal water release that affected time of crop sowing and growth stages, which was in turn reflected in the NDVI pattern. Double cropped and light irrigation have relatively late onset of greenness, because they use canal water from reservoirs that drain large catchments and take weeks to fill. Minor irrigation and ground water irrigated areas have early onset of greenness because they drain smaller catchments where aquifers and reservoirs fill more quickly. Vegetation phonologies of 9 distinct classes consisting of Irrigated, rainfed, and other land use classes were also derived using MODIS 250 meter near continuous time-series data that were tested and verified using groundtruth data, Google Earth very high resolution (sub-meter to 4 meter) imagery, and state-level census data. Fuzzy classification accuracies for most classes were around 80% with class mixing mainly between various irrigated classes. The areas estimated from MODIS were highly correlated with census data (R-squared value of 0.86). Full article