Evaluation of Soil Moisture Active Passive (SMAP) soil and vegetation data products for predicting changes in vegetation phonology

Defined as the projected one-sided green leaf area over a unit area of land, the leaf area index (LAI) of a vegetation canopy is a key ecophysiological indicator of plant status since it measures the photosynthetic active area and often subjects to transpiration. It is also an essential parameter required by most of the land surface models. The Soil Moisture Active Passive (SMAP) mission is focused on inferring surface moisture conditions based on a L-band microwave radiation transfer in both soil and plant canopies, though the primary retrieval algorithm assumes a seasonally varying climatology for vegetation opacity. Here we examine the utility of different SMAP retrieval algorithms for predictions of changes in LAI in time (dLAI/dt), including SMAP's dual channel algorithms which estimate vegetation and soil properties simultaneously. In this study, three-year daily precipitation, temperature and shortwave downward radiation flux data from SMAP L4 geophysical forcing fields were obtained at twenty National Ecological Observation Network (NEON) core terrestrial sites. Correspondently, five different options of soil moisture and vegetation opacity time series and LAI data were acquired from SMAP level two data at a daily scale and MODIS level four data at four-day scale, respectively. Linear interpolation and a Savitzky-Golay filter were applied to generate the change of MODIS LAI time series at daily scale. Multiple regression approaches were tested to investigate how much more information SMAP soil and vegetation estimates can provide in predicting dLAI/dt. Our results suggested that soil moisture generally contains more information than the SMAP dual-channel vegetation opacity retrievals for dLAI/dt estimation. Soil moisture observations at night improve estimates of dLAI/dt more than the day time soil moisture observation. However, the vegetation opacity showed an opposite pattern with night observation outperforms than the day time observation. Overall, the option 1 and option 2 of SMAP soil moisture and vegetation opacity observations contain more information than the other options. Our analysis results provide baseline assessment of the use of SMAP soil moisture and vegetation time series for accurately modeling and monitoring drastic change in LAI.

Keywords: SMAP, LAI, time series