کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4458913 1621268 2013 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evaluation of polarimetric Radarsat-2 SAR data for development of soil moisture retrieval algorithms over a chronosequence of black spruce boreal forests
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
Evaluation of polarimetric Radarsat-2 SAR data for development of soil moisture retrieval algorithms over a chronosequence of black spruce boreal forests
چکیده انگلیسی

C-band Radarsat-2 data were used to develop multi-parameter algorithms to retrieve soil moisture across a chronosequence (recently burned, shrubby regrowth and mature forest) of fire-disturbed Alaskan boreal black spruce forests using polarized backscatter intensity, polarimetric decomposition and discriminator parameters as independent variables. To account for complex interactions of surface roughness, biomass and soil moisture, multiple polarimetric variables were evaluated empirically in algorithm development using a time series dataset that included the wet and dry extreme conditions. Results indicate that polarimetric discriminators, such as maximum degree of polarization, fractional polarization or coefficient of variation, may be instrumental in accounting for the type of scattering occurring or the complexity or heterogeneity of the distributed target. Using the maximum degree of polarization combined with one or more variables on scattered intensity (e.g. C-HV, C-HH, or maximum scattering intensity) improved multi-linear moisture retrieval algorithms by 27–33% over the single, dual and quad polarized backscatter intensity algorithms. For all sites combined, algorithms were developed with low SE (7.0% volumetric soil moisture), moderately high R2 (0.77) and low RMSE (6.7% volumetric soil moisture). However, the best models were produced by separating the recently burned forests (strongest algorithm: R2 0.94, SE 5.9, RMSE 7.4) from the shrubby and mature forests (best algorithm: R2 0.85, SE 4.7, RMSE 7.3). Results are limited to sites with biomass below 3 kg/m2.


► Radar polarimetry is investigated to improve soil moisture estimation algorithms.
► Focus is on a chronosequence of recently burned to mature black spruce forests.
► Moisture from dry, moderate and wet conditions is captured in the seasonal dataset.
► Optimal models developed included the maximum degree of polarization discriminator.
► Models show polarimetric discriminators improve moisture prediction by 27–33%.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 132, 15 May 2013, Pages 71–85
نویسندگان
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