In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level.

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data.

Improving neural network classification of indigenous forest in New Zealand with phenological features.

Accurate and up-to-date land cover maps inform and support effective management and policy decisions. Describing phenological changes in spectral response using time-series data may help to distinguish vegetation types, thereby allowing for more specificity within vegetation classification. In this research, we test this by classifying indigenous forest vegetation in New Zealand, using PlanetScope (PS) and Sentinel-2 (S-2) satellite time-series data. The study was undertaken in a podocarp forest in New Zealand's central north island, which was classified into nine land cover classes. Phenological features, based on S-2 imagery, were extracted, including the enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI2) and normalised difference vegetation index (NDVI). Google Earth Engine (GEE) harmonic analysis and TIMESAT double logistic fitting function were used to extract phenological features. Pixel-based classifications were performed using a Neural Network on six different scenarios. The accuracy of the classification scenarios was determined and the importance score for each feature was evaluated. Using only the fused PS and S-2 bands, the land cover in the study area was classified with 90.1% accuracy. Adding phenological features increased the classification accuracy to 93.1%. When combined with VIs, texture features, and a digital terrain model, the addition of phenological features increased the classification accuracy to 96.6%. Including GEE-generated phenological features resulted in better classification accuracies than TIMESAT features. In terms of feature importance evaluation, EVI2- and NDVI-generated phenological features all had high scores; the effectiveness of EVI features could potentially have been limited by the quality of the blue band. The results demonstrate that it is possible to produce a more accurate classification of New Zealand's native vegetation by using phenological features. This method offers important cost-savings as the platforms for phenological analysis are free to use.

The confounding effect of snow cover on assessing spring phenology from space: A new look at trends on the Tibetan Plateau.

The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a "climate change hot-spot". Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during 2003-2016 for the Tibetan Plateau. We evaluated satellite-based SOS with field observations and gross primary production (GPP) from eddy covariance for both snow-free and snow covered sites. SOS derived from SIF was highly correlated with field data (R2 = 0.83) and also the normalized difference phenology index (NDPI) performed well for both snow free (R2 = 0.77) and snow covered sites (R2 = 0.73). On the contrary, normalized difference vegetation index (NDVI) correlates only weakly with field data (R2 = 0.35 for snow free and R2 = 0.15 for snow covered sites). We further found that an earlier end of the snow season caused an earlier estimate of SOS for the Tibetan Plateau from NDVI as compared to NDPI. Our research therefore adds new evidence to the ongoing debate supporting the view that the claimed advance in land surface SOS over the Tibetan Plateau is an artifact from snow cover changes. These findings improve our understanding of the impact of snow on land surface phenology in alpine ecosystems, which can further improve remote sensing based land surface phenology assessments in snow-influenced ecosystems.