Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm.

Lanka Karthikeyan,Ming Pan,Eric F Wood,Dasika Nagesh Kumar

doi:10.3390/s20041225

Abstract

Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.

Highlights

Passive microwave satellite sensors measure the thermal emissions from the Earth’s surface in the form of brightness temperatures (TB )
To depict the structural indeterminacy, we present a new solution that simultaneously estimates the Vegetation optical depth (VOD) and soil moisture (SM) using dual-polarized TB information from the radiative transfer model (RTM) scheme
Higher VOD retrievals are obtained through the usage of ΓC,New compared to the other two solutions, resulting in low transmissivity values (Equation (3)), which means that this solution models vegetation much more thickly than the other two solutions

Summary

Introduction

Passive microwave satellite sensors measure the thermal emissions from the Earth’s surface in the form of brightness temperatures (TB ). The retrieval of SM using passive microwave satellite sensors requires an algorithm, which can be used to convert the TB into. Given that a body naturally emits radiation, a landmass covered with a canopy would release thermal emissions primarily from the soil surface, canopy, atmosphere, and cosmic background. These effects are modeled using a radiative transfer model (RTM), which acts as a core component of the retrieval algorithm. A retrieval algorithm should be able to decompose the total TB measured by a satellite into contributions from the components described above

Objectives

Results

Conclusion