Retrieving forest soil moisture from SMAP observations considering a microwave polarization difference index (MPDI) to τ-ω model

IF 5.7 Q1 ENVIRONMENTAL SCIENCES
Chang-Hwan Park , Thomas Jagdhuber , Andreas Colliander , Aaron Berg , Michael H. Cosh , Johan Lee , Kyung-On Boo
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Abstract

Estimating soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. The soil moisture retrieved from the Soil Moisture Active Passive (SMAP) measurements tends to be consistently overestimated, sometimes exceeding the saturation level of mineral soils. Therefore, the retrieved soil moisture cannot detect or monitor climate extremes, such as floods and droughts for forests, natural resource management, and climate change research. We hypothesize that the main issue is that the scattering albedo (ω) and the optical depth (τ) are parameterized solely with NDVI (Normalized Difference Vegetation Index), neglecting the polarization characteristics from vegetation structure. This study proposes a weighting factor between scattering and optical thickness, a function of MPDI (Microwave Polarization Difference Index), and applies it to both parameters simultaneously to increase the scattering effect and decrease the attenuation effect in high MPDI. The validation results based on the Climate Reference Network revealed that considering MPDI is critical in reducing soil moisture overestimation errors and obtaining more accurate soil moisture over forested regions. This results in correlation improving from 0.36 to 0.44, a decrease in ubRMSE from 0.179 to 0.125 cm³cm³, and bias lowering from 0.127 to 0.060 cm³cm³ in comparison with the SMAP measurements over forested regions.

考虑微波极化差异指数(MPDI)至-ω模型,从SMAP观测数据中读取森林土壤湿度
根据微波亮度温度估算植被茂密地区的土壤湿度极具挑战性。从土壤水分主动被动(SMAP)测量中获取的土壤水分往往一直被高估,有时甚至超过矿质土壤的饱和度。因此,检索到的土壤水分无法探测或监测极端气候,如森林、自然资源管理和气候变化研究中的洪水和干旱。我们认为,主要问题在于散射反照率(ω)和光学深度(τ)仅以归一化植被指数(NDVI)为参数,忽略了植被结构的偏振特性。本研究提出了一个介于散射和光学厚度之间的加权系数,即 MPDI(微波极化差指数)函数,并同时应用于这两个参数,以增加散射效应,减少高 MPDI 时的衰减效应。基于气候参考网络的验证结果表明,考虑 MPDI 对减少土壤水分高估误差和获得更准确的森林地区土壤水分至关重要。这使得相关性从 0.36 提高到 0.44,ubRMSE 从 0.179 降低到 0.125 cm³cm-³,偏差从 0.127 降低到 0.060 cm³cm-³。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
12.20
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