Unsupervised segmentation of spaceborne passive radar images

Eom, K.B.

doi:10.1016/s0167-8655(99)00016-1

Cited by 5 publications

(1 citation statement)

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“…Integrating passive radar for large-scale mapping is mostly limited to soil moisture 46 or surface temperature. 47 Ferrazzoli et al 48 demonstrated their sensitivity toward vegetation biomass because of the water content plants and also Eom mentions their potential to detect changes in vegetation, 49 but none of these studies created spatial predictions from these data. Only Liu et al 50 mapped terrestrial biomass between 1993 and 2012 at the global scale based on space-borne passive radar and report an estimated decrease of 0.07 pg C∕yr, mostly resulting from deforestation in the tropics, but also because of changes in savannas and shrublands.…”

Section: Introductionmentioning

confidence: 99%

Above-ground biomass estimates based on active and passive microwave sensor imagery in low-biomass savanna ecosystems

Braun

Wagner

Hochschild

2018

J. Appl. Rem. Sens.

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Although many studies exist on the estimation and monitoring of above-ground biomass (AGB) of forest ecosystems by methods of remote sensing, very little research has been carried out for ecosystems of low primary production, such as grasslands, steppes, or savannas. Our study intends to approach this gap and investigates the correlation between space-borne radar information and AGB at the scale of 10 tons per hectare and below. Additionally, we introduce the integration of passive brightness temperature as an additional covariate for biomass estimation, based on the hypothesis that it contains information complementary to microwave backscatter of the active sensors. Our findings show that large-scale estimates of AGB can be conducted for grasslands and savannas at high accuracy (R 2 up to 0.52). Additionally, we found that the integration of passive radar can increase the quality of AGB estimates in terms of explained variance for selected cases. We hope that these indications are a starting point for more integrated approaches toward biomass estimations based on Earth observation methods.

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