ALOS PALSAR L-band polarimetric SAR data andin situmeasurements for leaf area index assessment

2012 ◽  
Vol 3 (3) ◽  
pp. 221-229 ◽  
Author(s):  
Francis Canisius ◽  
Richard Fernandes
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Polarimetric Sar ◽  
Alos Palsar ◽  
Area Index ◽  
L Band ◽  
Sar Data

scholarly journals Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

2018 ◽  
Vol 10 (9) ◽  
pp. 1355 ◽  
Author(s):  
Luciana Pereira ◽  
Luiz Furtado ◽  
Evlyn Novo ◽  
Sidnei Sant’Anna ◽  
Veraldo Liesenberg ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Floodplain Forest ◽  
Single Frequency ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Area Index ◽  
Ground Biomass ◽  
L Band ◽  
Sar Data

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.

The sensitivity of RADARSAT-2 polarimetric SAR data to corn and soybean leaf area index

2011 ◽  
Vol 37 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Xianfeng Jiao ◽  
Heather McNairn ◽  
Jiali Shang ◽  
Elizabeth Pattey ◽  
Jiangui Liu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Polarimetric Sar ◽  
Area Index ◽  
Soybean Leaf ◽  
Sar Data

Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data

2019 ◽  
Vol 35 (8) ◽  
pp. 905-915 ◽  
Author(s):  
Thota Sivasankar ◽  
Dheeraj Kumar ◽  
Hari Shanker Srivastava ◽  
Parul Patel
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Wheat Leaf ◽  
Sar Data

Analysis of water dynamics in the soil-plant-atmosphere continuum using a multi-sensor approach

2020 ◽  
Author(s):  
David Chaparro ◽  
Thomas Jagdhuber ◽  
Dara Entekhabi ◽  
María Piles ◽  
Anke Fluhrer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Water Content ◽  
Leaf Area ◽  
Optical Depth ◽  
Time Lag ◽  
Water Storage ◽  
Water Dynamics ◽  
Area Index ◽  
L Band

<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>

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
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Rainfall Interception ◽  
Area Index ◽  
L Band ◽  
Shrub Biomass

Biomass estimation based on hyperspectral and SAR data: an experimental study in South Tyrol, Italy

2020 ◽  
Author(s):  
Eugenia Chiarito ◽  
Francesca Cigna ◽  
Giovanni Cuozzo ◽  
Ludovica De Gregorio ◽  
Giacomo Fontanelli ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Biomass Estimation ◽  
High Biomass ◽  
Grassland Vegetation ◽  
Area Index ◽  
South Tyrol ◽  
Field Campaigns ◽  
Sar Data ◽  
Sentinel 2

<p>Grasslands cover almost one third of the world’s terrestrial surface. In Alpine environments grassland vegetation fulfills various key environmental purposes such as water reservoir, slope stabilizer and carbon sink or fodder for livestock. At the same time Alpine regions are more affected by climatic changes than other geographic zones, potentially resulting in earlier green-up phases or an elevated exposure to drought events, hampering the growth and vitality of grassland vegetation. The scope of this study is to build an algorithm capable of biomass estimation using Support Vector Machine approach on hyperspectral and Synthetic Aperture Radar (SAR) data. To that purpose, field campaigns were carried out during 2017 and 2019 in Val Mazia (South Tyrol, Italy), where hyperspectral spectroradiometer samples were collected, as well as leaf area index (LAI), soil moisture, and above ground biomass measurements. Copernicus Sentinel-1 IW SAR backscattering data were used to complete the dataset.</p><p>The spectroradiometer was used to simulate the hyperspectral data of the Italian Space Agency (ASI)’s PRISMA mission, launched on 22 March 2019. Since the number of bands is larger than the number of samples, a prediction approach based on machine learning risks to model noise. The following two solutions were tested and compared: (i) the number of bands was reduced by resampling the data to match specifications of Copernicus Sentinel-2 Multispectral Instrument (MSI), and (ii) the data was simulated using the PROSPECT model, increasing the sample size.</p><p>In the first case correlation R<sup>2</sup> of 0.37 was found. Discrepancies were observed for high biomass values, which could be explained by the small number of samples available shortly before harvest. To mitigate this effect, data were simulated for high biomass based on field average values and standard deviation within each date. R<sup>2</sup> increased to 0.71 in this case, confirming the above mentioned hypothesis regarding the dataset representativeness.</p><p>In the case of PROSPECT model, the parameters were found by iterating each one within ranges defined in the bibliography, until the spectral signatures matched the field observations. The resulting parameters were the input for data simulation. A genetic algorithm feature selection was run to reduce the number of features, discarding those with little or redundant information followed by an SVR model applied to the most sensitive bands resulting in an R<sup>2</sup> of 0.53. These initial results will be used as a basis for future investigations to improve the prediction model, for example by extending the dataset with new field campaigns, including more simulated data at biomass peak, as made with Sentinel-2 resampled dataset, or by adding further input variables, such as leaf area index. Furthermore, the procedure will be performed for fresh biomass and water content estimations.</p><p>The results obtained pave the way for future implementation of the tested algorithms on PRISMA hyperspectral and COSMO-SkyMed X-band SAR data in the future.</p><p>This research is part of the ongoing project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n.2018-37-HH.0.</p>

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