Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

Lingjia Gu,Xintong Fan,Xiaofeng Li,Yanlin Wei

doi:10.3390/rs11243037

Abstract

At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.

Highlights

Snow cover is an important factor in determining the Earth’s radiation balance
Combining the observed TBD calculated at 18 and 36 GHz at horizontal polarization (H-POL) from satellite data, the snow depth (SD) based on the statistical lookup tables (LUTs) was obtained and further compared with the retrieval SDs by Chang’s algorithm, Foster’s algorithm, the standard microwave radiation imagery (MWRI) algorithm, and the standard advanced microwave scanning radiometer 2 (AMSR2) algorithm, as well as the measured SDs along the snow course
For MWRI data, the SD retrieval results based on the statistical LUT, Foster’s algorithm, and standard MWRI algorithm are closer to the actual SD, while the retrieval results obtained using Chang’s algorithm had larger errors

Summary

Introduction

Snow cover is an important factor in determining the Earth’s radiation balance. It has a strong impact on the climate system and has an extremely important influence on the global water cycle [1,2]. At the regional and global scales, passive microwave remote sensing is the most efficient way to monitor snow. It can observe snow under most weather conditions and can penetrate snow cover to monitor snow depth (SD) and snow water equivalents (SWEs) [3,4,5]. Passive microwaves can penetrate snow and monitor the height of the snowpack. Passive microwave spaceborne measurements are often used for large-scale estimates of SD and SWE

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Conclusion