Analysis of water dynamics in the soil-plant-atmosphere continuum using a multi-sensor approach
Authors: Chaparro David , Jagdhuber Thomas , Entekhabi Dara , Piles María , Fluhrer Anke , Feldman Andrew , Jonard François , Vall-llossera Mercè 
Published: March 2020

&lt;p&gt;Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses.&lt;/p&gt;&lt;p&gt;Our study spans from April 2015 to March 2019 and is structured in two parts:&lt;/p&gt;&lt;p&gt;Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (&lt;em&gt;mg&lt;/em&gt;) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and &lt;em&gt;mg&lt;/em&gt; independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed.&lt;/p&gt;&lt;p&gt;Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.&lt;/p&gt;&lt;p&gt;[2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.&lt;/p&gt;&lt;p&gt;[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens.&amp;#160;2019,&amp;#160;11(20), 2353&lt;/p&gt;&lt;p&gt;[4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.&lt;/p&gt;&lt;p&gt;[5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.&lt;/p&gt;