The boreal forest is a major contributor to the global climate system, therefore, reducing uncertainties in how the forest will respond to a changing climate is critical. One source of uncertainty is the timing and drivers of the spring transition. Remote sensing can provide important information on this transition, but persistent foliage greenness, seasonal snow cover, and a high prevalence of mixed forest stands (both deciduous and evergreen species) complicate interpretation of these signals. We collected tower-based remotely sensed data (reflectance-based vegetation indices and Solar-Induced Chlorophyll Fluorescence [SIF]), stem radius measurements, gross primary productivity, and environmental conditions in a boreal mixed forest stand. Evaluation of this data set shows a two-phased spring transition. The first phase is the reactivation of photosynthesis and transpiration in evergreens, marked by an increase in relative SIF, and is triggered by thawed stems, warm air temperatures, and increased available soil moisture. The second phase is a reduction in bulk photoprotective pigments in evergreens, marked by an increase in the Chlorophyll-Carotenoid Index. Deciduous leaf-out occurs during this phase, marked by an increase in all remotely sensed metrics. The second phase is controlled by soil thaw. Our results demonstrate that remote sensing metrics can be used to detect specific physiological changes in boreal tree species during the spring transition. The two-phased transition explains inconsistencies in remote sensing estimates of the timing and drivers of spring recovery. Our results imply that satellite-based observations will improve by using a combination of vegetation indices and SIF, along with species distribution information.

Log-transforming the dependent variable of a regression model, though convenient and frequently used, is accompanied by an under-prediction problem. We found that this underprediction can reach up to 20%, which is significant in studies that aim to estimate annual budgets. The fundamental reason for this problem is simply that the log-function is concave, and it has nothing to do with whether the dependent variable has a log-normal distribution or not. Using field-observed data of soil CO2 emission, soil temperature and soil moisture in a saturated-specification of a regression model for predicting emissions, we revealed that the under-predictions of the log-transformed approach were pervasive and systematically biased. The key determinant of the problem's severity was the coefficient of variation in the dependent variable that differed among different combinations of the values of the explanatory factors. By applying a parsimonious (Gaussian-Gamma) specification of the regression model to data from four different ecosystems, we found that this under-prediction problem was serious to various extents, and that for a relatively weak explanatory factor, the log-transformed approach is prone to yield a physically nonsensical estimated coefficient. Finally, we showed and concluded that the problem can be avoided by switching to the nonlinear approach, which does not require the assumption of homoscedasticity for the error term in computing the standard errors of the estimated coefficients.

The ongoing deforestation process in Amazonia has led to intensified forest fires in the region, particularly in Brazil, after more than a decade of effective forest conservation policy. This study aims to investigate the recovery of two mature sub‐montane ombrophile Amazonian forests affected by fire in terms of energy, water and carbon fluxes utilizing remote sensing (MODIS) and climate reanalysis data (GLDAS). These two forest plots, mainly composed of Manilkara spp. (Maçaranduba), Protium spp. (Breu) (∼30 m), Bertholletia excelsa (Castanheira) and Dinizia excelsa Ducke (Angelim‐Pedra) (∼50 m), occupy areas of 100.5 and 122.1 km2 and were subject to fire on the same day, on September 12, 2010. The fire significantly increased land surface temperature (0.8°C) and air temperature (1.2°C) in the forests over a 3 years interval. However, the forests showed an ability to recover their original states in terms of coupling between the carbon and water cycles comparing the 3‐year periods before and after the fires. Results from a wavelet analysis showed an intensification in annual and seasonal fluctuations, and in some cases (e.g., daily net radiation and evapotrasnspiration) sub‐annual fluctuation. We interpreted these changes to be consistent with overall intensification of the coupling of energy balance components and drivers imposed by climate and solar cycle seasonality, as well as faster time scale changes, consistent with a shift toward greater forest openness and consequent reduction in the interception of incoming solar radiation by the canopy.