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The leaf phenology of seasonally dry tropical forests (SDTFs) is highly seasonal, marked by synchronized flushing of new leaves triggered by the first rains of the wet season. Such phenological transitions may not be accurately detected by remote sensing vegetation indices and derived transition dates (TDs) due to the coarse spatial and temporal resolutions of satellite data. The aim of this study was to compared TDs from PhenoCams and satellite remote sensing (RS) and used the TDs calculated from PhenoCams to select the best thresholds for RS time series and calculate TDs. For this purpose, we assembled cameras in seven sites along an aridity gradient in the Brazilian Caatinga, a region dominated by SDTFs. The leafing patterns were registered during one to three growing seasons from 2017 to 2020. We drew a region of interest (ROI) in the images to calculate the normalized green chromatic coordinate index. We compared the camera data with the NDVI time series (2000–2019) derived from near-infrared (NIR) and red bands from MODIS product data. Using calibrated PhenoCam thresholds reduced the mean absolute error by 5 days for SOS and 34 days for EOS, compared to common thresholds in land surface phenology studies. On average, growing season length (LOS) did not differ significantly among vegetation types, but the driest sites showed the highest interannual variation. This pattern was applied to leaf flushing (SOS) and leaf fall (EOS) as well. We found a positive relationship between the accumulated precipitation and the LOS and between the accumulated precipitation and maximum and minimum temperatures and the vegetation productivity (peak and accumulated NDVI). Our results demonstrated that (A) the fine temporal resolution of phenocamera phenology time series improved the definitions of TDs and thresholds for RS landscape phenology; (b) long-term RS greening responded to the variability in rainfall, adjusting their timing of green-up and green-down, and (C) the amount of rainfall, although not determinant for the length of the growing season, is related to the estimates of vegetation productivity.

Purpose – The purpose of this paper is to further understanding of how new information impacts the market value of financial assets. Design/methodology/approach – The paper uses a Bayesian approach to asset valuation, whereby investors use signals conveyed by new information to update their estimate of a structural valuation parameter. The underlying distributions – i.e. the distribution of the information signal and the prior distribution of the valuation parameter – are allowed to exhibit a degree of kurtosis greater than that of the normal distribution. Findings – The revision in asset value as a function of the realization of the information signal is an S-shaped function (in the local region centred on the zero-surprise level of the signal), if the distribution of the information signal features excess kurtosis; conversely, if the prior of the valuation parameter features excess kurtosis, the revision in asset value is an inverted S-shaped function. Research limitations/implications – The paper generates clear implications with respect to the shape of the function relating the revision in asset value to the realization of the signal only in the local region centred on the zero-surprise level of the signal. Practical implications – The paper helps to shed light on the well-known empirical result that the stock price reaction to earnings' announcements is an S-shaped function, centred on the zero-surprise level of reported earnings. Originality/value – In the financial accounting literature, the paper helps one to understand the role of the distributional assumptions underlying the stock price reaction to earnings' announcements, namely, the role of excess kurtosis both in reported earnings and in the prior of means earnings. [ABSTRACT FROM AUTHOR]