Land surface phenology (LSP), the study of seasonal dynamics of vegetation analysing phenological metrics -phenometrics- derived from vegetation indices time series (VI), has emerged as an important research focus in recent decades as LSP patterns have been considered as an important ecological indicator for understanding the functioning of terrestrial ecosystems. LSP from high-spatial-resolution satellite imagery in ecosystems with significant heterogeneity of plant species, such as Macaronesian ecosystems, are needed for a better understanding on how these ecosystems function. The objective of this study was to monitor LSP dynamics of representative species of the Canary Islands: Olea Cerasiformis, Pistacia atlantica, Juniperus turbinata, Pinus canariensis, Myrica Faya and Erica arborea. NDVI (Normalised Difference Vegetation Index) Sentinel-2 time series at a spatial and temporal resolution of 10 meters and 5 days were generated for the 2018-2020 period. Atmospheric disturbances and noise were reduced using a double-logistic function. SOS (start of the growing season), EOS (end of the growing season) and LOS (length of the growing season) were extracted using a threshold-based method. Thermophilus species, such as Olea Cerasiformis and Pistacia atlantica had the SOS in October-November and the EOS between June and July. Juniperus turbinata showed double seasonality in La Palma, being the first growing season between November-December and April-May and the second growing season between May-June and September-October. Growing season of Pinus canariensis started in September-October and ended in April-June, nevertheless a double seasonality was observed in some locations of Pinus canariensis, probably associated to the understory. Subtropical laurel forest composed by different plant species, such as Myrica Faya and Erica arborea, did not show a clear seasonality. The species-specific LSP patterns for the Canary Islands can contribute to stablishing a baseline to monitor future impacts of climate change in Macaronesian biogeographical region.
The launch of the Copernicus Sentinel-2 mission offered new insights for the management of the European Common Agrarian Policy (CAP). Sentinel-2 provides information at a spatial and temporal resolution of 10 m and 5 days, respectively. However, this unprecedented time series of high resolution satellite imagery requires from approaches to extract meaningful agronomical information and reduce dimensionality. This could be the case of land surface phenology, which consists in estimating key phenometrics related to agronomical events from time series of vegetation indices (VIs). Knowing the dynamics of crop phenology is essential for the correct monitoring of CAP.
We used EVI2 (Enhanced Vegetation Index 2) time series of Sentinel-2 data for the period 2018-2020. EVI2 is a VI widely used as an indicator of plant vigour, that avoids saturation in regions with high biomass. Double Logistic smoothing method was used to fill the gaps caused by the lack of images due to cloud presence or sensor failures. We selected plots of durum and common wheat, sorghum, barley and triticale according to the Geographical Information System for the CAP (GISCAP-CAP) declarations in Andalusia, Spain. The phenometrics extracted were start of the season (SOS), middle of the season (MOS), end of the season (EOS), their respective values of EVI2, and length of the season (LOS) (EOS-SOS). The aim of this study is to characterise the phenology of different winter cereals, through the extraction of phenometrics, and to evaluate whether these latter measures can serve to distinguish them. Results show that the response is quite similar between all of them, except sorghum. Common wheat shows the earliest SOS, followed by barley, durum wheat, triticale and sorghum. Common wheat shows the earliest EOS, followed by durum wheat, barley, triticale and sorghum.
Vegetation phenology, the study of the timing of biological cycles of plants and their relation with environmental factors, is considered an important ecological indicator of climate change. Different ecological processes, such as water and carbon cycles, energy fluxes or species interactions, can be altered by changes in the phenological patterns of plants. Furthermore, these changes could also have important consequences on economy (e. g. agriculture, forestry). Iberian Peninsula is one of the regions with the greatest diversity of ecosystems in European continent. It is therefore an excellent natural laboratory for monitoring vegetation dynamics. The goal of this study was to monitor the vegetation phenology dynamics across ecoregions of the Iberian Peninsula for the period between 2001 and 2017. 782 8-day composites of NDVI (Normalised Difference Vegetation Index) images were produced from the surface reflectance product MOD09Q1 at a spatial resolution of 250 meters. A Savitzky-Golay filter was applied to smooth the NDVI time series and a threshold-based method was used to extract three phenometrics: the start of the growing season (SOS), the end of growing season (EOS) and the length of the growing season (LOS). Results of this research showed how both SOS and EOS are significantly different between the northern and southern ecoregions. Cantabrian mixed forests and Pyrenees conifer and mixed forests presented the latest SOS and EOS. Iberian conifer forests, Northwest Iberian montane forests and Northeastern Spain and Southern France Mediterranean forests showed the highest internal variability in the phenological dates, which may be related with the behaviour of different land covers (e. g., phenology of natural vegetation vs. crop phenology) and the altitude effects on climatic conditions (e. g. increases in precipitation, decrease of the temperature). Phenological patterns of the Iberian ecoregions could contribute to improve the understanding of the potential environmental effects of climate change.
Quantifying wheat’s production is essential to support food security management. It can be achieved with empirical models developed with the information provided by vegetation indices (VI). This work evaluated the performance of different time series of VI for the predictive modelling of wheat production and yield in Spain comparing two sources of cropland masks: wheat mask using Common Agricultural Policy declarations (CAP), and arable land from Corine Land Cover (CLC). Both sources were used to analyse the improvement derived from considering specific wheat masks. The wheat production and yield were modelled using time series of MODIS NDVI and EVI2 (2006 to 2016) from weekly surface reflectance products (MOD09Q1 v6) at 250 meters. The sum of VI values of one month after the maximum was used as this period is related with yield and production. VI indicators were filtered and aggregated to NUTS-3 level. The cropland masks were obtained either by combining the parcel boundaries with the CAP wheat reports, or from the CLC arable land category of 2006 and 2012 maps. Production (t) and yield (t ha-1) estimates were obtained from official statistics. Subsequently, different regression analyses were carried to build an overall model and single models for some NUTS2.
Models using CAP wheat masks outperformed those of CLC, predicting more accurately production than yield. The best performance for production models using CAP was that of EVI2 in Castille and Leon (R2=96% and Normalized Relative Error (NRE)=14.72%) and the best for CLC that of EVI2 in Spain (R2=55% and NRE=58.01%). Regarding yield modelling, CAP with EVI2 in Aragon was the best (R2=81% and NRE=10.57%) as well as CLC with EVI2 in Spain overall model (R2=50% and NRE=22.34%). The findings of this work suggest that the use of specific crop masks is of paramount importance for the predictive modeling of crop production.
Land surface phenology (LSP) can improve the characterisation of forest areas and their change processes. The aim of this work was: i) to characterise the temporal dynamics in Mediterranean Pinus forests, and ii) to evaluate the potential of LSP for species discrimination. The different experiments were based on 679 mono-specific plots for the 5 native species on the Iberian Peninsula: P. sylvestris, P. pinea, P. halepensis, P. nigra and P. pinaster. The entire MODIS NDVI time series (2000–2016) of the MOD13Q1 product was used to characterise phenology. The following phenological parameters were extracted: the start, end and median days of the season, and the length of the season in days, as well as the base value, maximum value, amplitude and integrated value. Multi-temporal metrics were calculated to synthesise the inter-annual variability of the phenological parameters. The species were discriminated by the application of Random Forest (RF) classifiers from different subsets of variables: model 1) NDVI-smoothed time series, model 2) multi-temporal metrics of the phenological parameters, and model 3) multi-temporal metrics and the auxiliary physical variables (altitude, slope, aspect and distance to the coastline). Model 3 was the best, with an overall accuracy of 82%, a kappa coefficient of 0.77 and whose most important variables were: elevation, coast distance, and the end and start days of the growing season. The species that presented the largest errors was P. nigra, (kappa= 0.45), having locations with a similar behaviour to P. sylvestris or P. pinaster.
The study of the interaction between vegetation development and climate factors is paramount for the management of protected natural areas. Data provided by remote-sensing satellites and derivative products, such as vegetation indices, permit the extraction of basic information regarding the functioning of vegetation masses and their interaction with certain environmental factors. This paper carries out an approach regarding the behaviour of radiation intercepted by aquatic macrophytes present in the Doñana National Park marshland, represented by the plant association Bolboschoenetum maritimi. Based on MODIS NDVI (Normalised Difference Vegetation Index) data, the temporal dynamics of these vegetation masses were studied over a 16-year period (2000–2015), as was their typical annual behaviour, thereby deriving different indicators for seasonal dynamics (NDVI-I, RREL, MAX, MIN, MMAX and MMIN), which help to understand the basic functional characteristics for this type of vegetation. Afterwards, different regression analyses were performed between precipitation and the indicators derived from the NDVI time series. The obtained results indicated that the examined association has a strong dependence on the marshland's flooding processes, requiring a minimum annual precipitation volume (350 mm/year) for proper flooding and vegetation growth development. Furthermore, a strong correlation (r2 =0.70; <;0.05) was found between seasonal nature of the vegetation masses, measured via RREL, and precipitation, as well as slightly weaker relationships between precipitation and other indicators, such as the maximum and minimum annual NDVI (r2 =0.43 and r2 =0.61; p<0.05 and p<0.05, respectively).
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