Watershed-scale-based forecast method for leaf area index data based on the integration of time series MODIS products and meteorological data

Abstract

The leaf area index (LAI) is an important parameter used to characterize land vegetation growth patterns, and LAI forecasts are highly beneficial to natural resource management decisions. However, the LAI can be affected by biophysical, climate, and anthropogenic factors, thus, its future watershed-scale patterns remain difficult to assess. Herein, an object-oriented watershed-scale LAI forecasting (OWLF) method is proposed to solve the forecasting difficulties encountered when using only past meteorological data. The model was constructed with filtered multi-year MODIS LAI time series and climate index variables. Then, the dependence of the LAI on four mean monthly climate index variables was evaluated, namely, latent evapotranspiration, rainfall capacity, average air temperature, and sunlight hours, in the Poyang Lake basin. By combining the climate indexes derived from the past 1–3 months, the model forecasts the subsequent months LAI for different land cover types. The LAI forecasting results derived with a 13-year time series (2000–2012) suggest that the OWLF method can effectively recognize the expected spatial patterns, and the data agreed reasonably well with LAI dynamics and phenological periods. This work offers a promising way to exploit combined satellite and climate index data in novel and more accurate watershed-scale forecasting studies.