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.
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.
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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