Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
Fig 7
Mean seasonal variation of NEE residuals for LSTM, LSTMperm, LSTMmsc, and LSTMannual models for (a) deciduous and (b) evergreen forests. NEE residuals = [NEE observedi,j − mean(NEE observedi)] − [NEE predictedi,j − mean(NEE predictedi)], where i is a unique Fluxnet site and j is a monthly observation. Residual estimates have been calculated based on the mean ensemble ±sd of the 50 model runs. LSTM = LSTM model using the full depth of the Landsat time series and climate data; LSTMperm = LSTM model but the temporal patterns of both the predictive and the target variables were randomly permuted while instantaneous relationships between predictive and target variables were kept; LSTMmsc = LSTM model but the Landsat time series for each band were replaced by their mean seasonal cycle, while using the actual values of air temperature (Tair), precipitation (P), global radiation (Rg), and vapor pressure deficit (VPD); LSTMannual = LSTM model but the Landsat time series for each band were replaced by their annual mean, while using the actual values of Tair, P, Rg, and VPD, RF = Random Forest model using the actual values of the Landsat time series and climate data. Months for the sites located in the Southern hemisphere have been adjusted to match the seasonal cycle of the sites in the Northern hemisphere.