Abstract

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.

Highlights

  • The remote sensing community aims to develop soil moisture products at various resolutions, as soil moisture finds application from local and regional to global scales [1]

  • A comprehensive sensitivity analysis to study radiative transfer model using spatial maps is conducted for various field campaigns in multiple hydroclimates

  • The primary contribution of this work is to demonstrate how the sensitivity to the spatial maps of land surface variables change under various hydroclimates (Arizona, Oklahoma, Iowa, and Winnipeg) and evolving scales (0.8 km, 1.6 km, 3.2 km, 6.4 km, and 12.8 km) for a given extent

Read more

Summary

Introduction

The remote sensing community aims to develop soil moisture products at various resolutions, as soil moisture finds application from local (e.g., crop water management) and regional (e.g., flood and drought forecasting) to global scales (e.g., meteorology, climate dynamics) [1]. The active or passive sensors used for estimating soil moisture come with their own challenges such as accuracy, Radio Frequency Interference (RFI), high sensitivity to vegetation/roughness, spatio-temporal coverage, big data challenges, etc., [2,3]. Several scaling algorithms have been developed in the past using data fusion techniques integrating information from various scales (point/airborne/satellite), platforms (MODIS/LANDSAT/AVHRR etc.), sensors (active/passive), frequencies (P, L, C, and X), and land surface variables (surface temperature, NDVI, etc.) [8,9,10,11,12]. For downscaling/upscaling of either soil moisture or TB, it first calls for understanding the propagation of errors/uncertainties through the scale and heterogeneity of land surface variables

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call