Application of L-band SAR for mapping tundra shrub biomass, leaf area index, and rainfall interception

2022 ◽  
Vol 268 ◽  
pp. 112747
Author(s):  
Qianyu Chang ◽  
Simon Zwieback ◽  
Ben DeVries ◽  
Aaron Berg
2020 ◽  
Author(s):  
David Chaparro ◽  
Thomas Jagdhuber ◽  
Dara Entekhabi ◽  
María Piles ◽  
Anke Fluhrer ◽  
...  

<p>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.</p><p>Our study spans from April 2015 to March 2019 and is structured in two parts:</p><p>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 (<em>mg</em>) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and <em>mg</em> 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.</p><p>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.</p><p> </p><p>References</p><p>[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.</p><p>[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.</p><p>[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens. 2019, 11(20), 2353</p><p>[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/.</p><p>[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.</p>


2018 ◽  
Vol 10 (9) ◽  
pp. 1355 ◽  
Author(s):  
Luciana Pereira ◽  
Luiz Furtado ◽  
Evlyn Novo ◽  
Sidnei Sant’Anna ◽  
Veraldo Liesenberg ◽  
...  

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.


2020 ◽  
Author(s):  
feng zhong ◽  
Brecht Martens ◽  
Albert van Dijk ◽  
Liliang Ren ◽  
Shanhu Jiang ◽  
...  

<p>The evaporation of rainfall intercepted by canopies back into the atmosphere – often referred to as rainfall interception loss – is a significant component of terrestrial evaporation in many ecosystems. The physical process of rainfall interception loss can usually be broken down into three phases: (1) wetting up of the canopy, (2) saturated canopy conditions, and (3) drying out after rainfall has ceased. During each of these phases, the process is affected by many factors, including rainfall characteristics, such as gross rainfall, rainfall intensity and rainfall duration, vegetation characteristics such as canopy height, leaf area and the orientation of branches and leaves, and meteorological conditions such as temperature, wind speed and relative humidity. The Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al. 2011) estimates terrestrial evaporation, including forest rainfall interception loss, at the global scale mostly from satellite data. However, the model estimation of interception loss has not been updated since its release almost 10 years ago (Miralles et al. 2010).</p><p>In this regard, improving the estimation of interception loss in the model remains a priority. In GLEAM, rainfall interception is estimated using the revised Gash analytical model by Valente et al. (1997), in which the canopy storage and mean wet canopy evaporation rate are both considered constants in both space and time. In addition, only tall-canopy interception is considered. Here we explore the potential of the modified Gash's model by Van Dijk and Bruijnzeel (2001), which uses time variant canopy storage and evaporation functions dependent on leaf area index, for its application at global scales. In addition, due to its dependency on leaf area index, the model is applied to the estimation of rainfall interception loss of low vegetation types such as shrubs and grasses. An extensive meta-analysis of previous interception loss field campaigns provides an extensive archive of data to parameterize and/or validate model estimates over multiple ecosystem types. This presentation provides a general overview of the challenges in rainfall interception loss modelling at global scales and the first results of the global benchmarking of the Valente et al. (1997) and the Van Dijk and Bruijnzeel (2001) formulations against <em>in situ</em> data.</p><p><strong>References </strong></p><p>Miralles D G, Gash J H, Holmes T R H, et al. Global canopy interception from satellite observations[J]. Journal of Geophysical Research: Atmospheres, 2010, 115(D16).</p><p>Miralles D G, Holmes T R H, De Jeu R A M, et al. Global land-surface evaporation estimated from satellite-based observations[J]. Earth Syst. Sci., 2011, 15(2): 453–469.</p><p>Valente F, David J S, Gash J H C. Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models[J]. Journal of Hydrology, 1997, 190(1-2): 141-162.</p><p>Van Dijk A, Bruijnzeel L A. Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description[J]. Journal of Hydrology, 2001, 247(3-4): 230-238.</p>


2015 ◽  
Vol 170 ◽  
pp. 77-89 ◽  
Author(s):  
Mehdi Hosseini ◽  
Heather McNairn ◽  
Amine Merzouki ◽  
Anna Pacheco

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