Understanding ecosystem light use efficiency (LUE) of salt marshes, tidal wetlands with high salinity at the terrestrial-aquatic interface, is important to effectively estimate ecosystem productivity through remote sensing techniques. These salt marshes have high soil carbon burial rates, making them important ecosystems for studying blue carbon dynamics (Chmura et al., 2003;Mcleod et al., 2011). Juncus roemerianus and Spartina alterniflora are two dominate species found within salt marshes of the eastern United States across large latitudinal ranges: 25°-42° and 30°-50°, respectively (Eleuterius, 1976;Smart, 1982). These two species' spatial distributions in coastal zones are driven by biophysical gradients including elevation, salinity,

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Tidal marshes are among the most productive ecosystems, and an important carbon (C) sink in the global carbon cycle (Bianchi, 2006). The average C burial rate of coastal salt marshes is as much as 1,713 g C m −2 yr −1 in sediment, ∼35 times higher than in terrestrial forests (McLeod et al., 2011), which provides the key scientific motivation to understand salt marsh productivity across space and time. Salt marshes experience periodic tidal flooding, which affects plant production (Hawman et al., 2021;O'Connell et al., 2021) and photosynthetic rates (Kathilankal et al., 2011). However, only a handful of studies have been conducted to understand photosynthetic behavior under flooded conditions within tidal marshes (Duarte et al., 2005;Kathilankal et al., 2008). Pezeshki et al. (1993) showed that a congener, Spartina patens, had a 46% reduction in rates of photosynthesis and 18% reduction in carbon assimilation under hypoxic (flooded) conditions in microcosm experiments. Kathilankal et al. (2008) used field measurements during tidal flooding to demonstrate a 66% reduction in photosynthetic activity of the submerged salt marsh plant,

This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, China. Traditional analysis of vegetation response to climate change uses minimum, maximum, average or cumulative measurements; focuses on a whole region instead of fine-scale regional or ecological variations; or adopts generic analysis techniques. We innovatively integrate Empirical Mode Decomposition (EMD) and Redundancy Analysis (RDA) to overcome the weakness of traditional approaches. The EMD filtered trend surfaces reveal clear patterns of Enhanced Vegetation Index (EVI), precipitation, and temperature changes in both time and space. The filtered data decrease noises and cyclic fluctuations in the original data and are more suitable for examining linear relationship than the original data. RDA is further applied to reveal partial effect of precipitation and temperature, and their joint effect on primary productivity. The main findings are as follows: (1) We need to examine relationships between the trends of change of the variables of interest when investigating long-term relationships among them. (2) Long-term trend of change of precipitation or temperature can become a critical factor influencing primary productivity depending on local environments. (3) Synchronization (joint effect) of precipitation and temperature in growing season is critically important to primary productivity in the study area. (4) Partial and joint effects of precipitation and temperature on primary productivity vary over different precipitation zones and different types of vegetation. The method developed in this paper is applicable to ecosystem research in other regions.

Climate change is a global phenomenon but is modified by regional and local environmental conditions. Moreover, climate change exhibits remarkable cyclical oscillations and disturbances, which often mask and distort the long-term trends of climate change we would like to identify. Inspired by recent advancements in data mining, we experimented with empirical mode decomposition (EMD) technique to extract long-term change trends from climate data. We applied GIS elevation model to construct 3D EMD trend surface to visualize spatial variations of climate change over regions and biomes. We then computed various time-series similarity measures and plot them to examine spatial patterns across meteorological stations. We conducted a case study in Inner Mongolia based on daily records of precipitation and temperature at 45 meteorological stations from 1959 to 2010. The EMD curves effectively illustrated the long-term trends of climate change. The EMD 3D surfaces revealed regional variations of climate change, while the EMD similarity plots disclosed cross-station deviations. In brief, the change trends of temperature were significantly different from those of precipitation. Noticeable regional patterns and local disturbances of the changes in both temperature and precipitation were identified. The trends of change were modified by regional and local topographies and land covers.