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

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.

A Dynamic Snow Depth Inversion Algorithm Derived From AMSR2 Passive Microwave Brightness Temperature Data and Snow Characteristics in Northeast China

“…Northeast China is an important commodity grain base, and it is also one of the main snow-covered areas in the world [46]. At present, traditional empirical and semi-empirical algorithms are based on a fixed snow density and snow particle size, they calibrate the relationship between SD and TBD on limited dataset.…”

“…The snow correlation length is used to describe the scattering of snow layers in the MEMLS model [44], it is defined as the inverse of the derivative of the autocorrelation function of snow structure at zero displacements [21]. In this study, according to Davis and Dozier [45], the correlation length was related to snow grain size and snow density, it could be calculated by Equation (2) and Equation (3), which was described in [21], [44]- [46].…”

“…where c P is the correlation length,  is snow density, i  represents the ice density (917kg/m3) and S is the ice surface area per unit volume, a is the side length of the cubic box, and n is the number of particles along with its one. According to the field measurements, the snow particle is similar to the pillar, thus, we used the measured snow grain size as the long side length and the short side length was randomly selected to be between two thirds and one time the long side length, it was assumed that these numbers (1000) were homogeneously distributed within a cubic box, and the volume of the cubic box could be calculated according to snow density and the volume of particles [21], [46].…”

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

“…These include applications that are similar to snow monitoring using inversion algorithms [101,226], but also applications that combine SWE/SD with other fields such as meteorology by looking at snowstorms [215], or temperature prediction [155], or geophysics by analyzing droughts [97]. In addition, there were some studies that looked at the role that snow crystal size plays in the estimation of SWE/SD [177,227]. As the inversion algorithms are designed for data collected by PMW sensors, they are not applicable for GRACE/GRACE-FO data.…”