Abstract
The time lag between anomalies in precipitation and vegetation activity plays a critical role in early drought detection as agricultural droughts are caused by precipitation shortages. The aim of this study is to explore a new approach to estimate the time lag between a forcing (precipitation) and a response (NDVI) signal in the frequency domain by applying cross-spectral analysis. We prepared anomaly time series of image data on TRMM3B42 precipitation (accumulated over antecedent durations of 10, 60, and 150 days) and NDVI, reconstructed and interpolated MOD13A2 and MYD13A2 to daily interval using a Fourier series method to model time series affected by gaps and outliers (iHANTS) for a dry and a wet year in a drought-prone area in the northeast region of China. Then, the cross-spectral analysis was applied pixel-wise and only the phase lag of the annual component of the forcing and response signal was extracted. The 10-day antecedent precipitation was retained as the best representation of forcing. The estimated phase lag was interpreted using maps of land cover and of available soil water-holding capacity and applied to investigate the difference in phenology responses between a wet and dry year. In both the wet and dry year, we measured consistent phase lags across land cover types. In the wet year with above-average precipitation, the phase lag was rather similar for all land cover types, i.e., 7.6 days for closed to open grassland and 14.5 days for open needle-leaved deciduous or evergreen forest. In the dry year, the phase lag increased by 7.0 days on average, but with specific response signals for the different land cover types. Interpreting the phase lag against the soil water-holding capacity, we observed a slightly higher phase lag in the dry year for soils with a higher water-holding capacity. The accuracy of the estimated phase lag was assessed through Monte Carlo simulations and presented reliable estimates for the annual component.
Highlights
Every climate zone experiences droughts with impacts varying across regions [1,2]
We have evaluated the time series of accumulated precipitation procedure is illustrated by the intermediate results in Figure 7 using the time series extracted from a described in pixel
This study indicates that vegetation activity responds to precipitation anomalies with a time lag of 9.0 days for a wet year (2008) and 16.0 days for a dry year (2009)
Summary
Every climate zone experiences droughts with impacts varying across regions [1,2]. Drought affects ecosystems and multiple sectors of society, whereas different climate and regional characteristics influence the severity of droughts [3,4]. Since the 1960s, drought-detection systems have been developed to detect and measure the emergence, probability of occurrence and severity of droughts [8,9]. Warning Systems (DEWS) started with the development of drought indices based on meteorological observations. Due to the rapid advancement in earth-observation techniques since the 1970s, a series of remote sensing-derived products can capture most features of the hydrological cycle [10,11,12,13]
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