The Influence of Thermal Properties and Canopy- Intercepted Snow on Passive Microwave Transmissivity of a Scots Pine

Li, Qinghuan; Kelly, Richard; Leppänen, Leena; Vehviläinen, Juho; Kontu, Anna; Lemmetyinen, Juha; Pulliainen, Jouni

doi:10.1109/tgrs.2019.2899345

Cited by 23 publications

(35 citation statements)

References 52 publications

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“…Thus, the use of 89 GHz channels can greatly improve depth retrieval for barren land (Jiang et al, 2014). The mixed-pixel problem is the dominant limitation on snow depth estimation accuracy (Derksen et al, 2005;Kelly et al, 2009;Jiang et al, 2014;Roy et al, 2014;Cai et al, 2017;Li et al, 2017;Li et al, 2019). Satellite TB usually represents several land cover types due to coarse footprints (tens of km).…”

Section: Methodsmentioning

confidence: 99%

Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach

Yang¹,

Jiang²,

Luojus³

et al. 2019

Preprint

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Abstract. Snow depth data time series are valuable for climatological and hydrological applications. Passive microwave (PMW) sensors are advantageous for estimating spatially and temporally continuous snow depth. However, PMW estimate accuracy has several problems, which results in poor performances of traditional snow depth estimation algorithms. Machine learning (ML) is a common method used in many research fields, and its early application in remote sensing is promising. In this study, we propose a new and accurate approach based on the ML technique to estimate real-time snow depth and reconstruct historical snow depth from 1987–2018. First, we trained the random forest (RF) model with advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures (TB) at 10.65, 18.7, 36.5 and 89 GHz, land cover fraction (forest, shrub, grass, farm and barren), geolocation (latitude and longitude) and station observation from 2014–2015. Then, the trained RF model was used to retrieve a reference dataset with 2012–2018 AMSR2 TB data as the accurate snow depth. With this reference snow depth dataset, we developed the pixel-based algorithm for the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMI/S). Finally, the pixel-based method was used to reconstruct a consistent 31-year daily snow depth dataset for 1987–2018. We validated the trained RF model using the weather station observations and AMSR2 TB during 2012–2013. The results showed that the RF model root mean square error (RMSE) and bias were 4.5 cm and 0.04 cm, respectively. The pixel-based algorithm’s accuracy was evaluated against the field sampling experiments dataset (January–March, 2018) and station observations in 2017–2018, and the RMSEs were 2.0 cm and 5.1 cm, respectively. The pixel-based method performs better than the previous regression method fitted in China (RMSEs are 4.7 cm and 8.4 cm, respectively). The high accuracy of the pixel-based method can be attributed to the spatial dynamic retrieval coefficients and accurate snow depth estimates of the RF model. Additionally, the 1987–2018 long-term snow depth dataset was analyzed in terms of temporal and spatial variations. On the spatial scale, daily maximum snow depth tends to occur in Xinjiang and the Himalayas during 1992–2018. However, the daily mean snow depth in Northeast China is the largest. For the temporal characteristics, the February mean snow depth is the thickest during snowy winter seasons. Interestingly, the January mean snow depth represents the annual mean snow depth, which plays an important role in snow depth prediction and hydrological management. In conclusion, through step-by-step validation using in situ observations, our pixel-based approach is available in real-time snow depth retrievals and historical data reconstruction.

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Section: Methodsmentioning

confidence: 99%

Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach

Yang¹,

Jiang²,

Luojus³

et al. 2019

Preprint

Self Cite

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“…For forested snow-covered areas, the biases are close to zero due to the influence of the forest canopy, rather than the snowpack [47][48][49]. Thus, for snow-free areas, the SSMIS tends to yield higher Tb than SSM/I because of high emissions at 91 GHz compared with 85 GHz (Figure 3, left bottom).…”

Section: Comparison Between Ssm/i and Ssmis Brightness Temperaturementioning

confidence: 97%

“…because the higher frequency, 91 GHz, has stronger scattering from snowpack than 85 GHz, resulting in low Tb for the SSMIS sensor compared with SSM/I [45,46]. For forested snow-covered areas, the biases are close to zero due to the influence of the forest canopy, rather than the snowpack [47][48][49]. Thus, for snow-free areas, the SSMIS tends to yield higher Tb than SSM/I because of high emissions at 91 GHz compared with 85 GHz (Figure 3, left bottom).…”

Section: Comparison Between Ssm/i and Ssmis Brightness Temperaturementioning

confidence: 99%

“…Forest, indeed, attenuates the radiation emitted by the underlying snowpack and emits its own radiation toward the satellite-based radiometer. Therefore, the satellite observation is mainly associated with the emissions from the forest canopy [47][48][49]. However, the satellite observation in Xinjiang is plagued by a number of challenges, including the distinguishing of scattering materials, snow microphysical properties, and liquid water within snowpack.…”

Section: Comparison Between Ssm/i and Ssmis Snow Depthmentioning

confidence: 99%

“…Although PMW remote sensing has many advantages in monitoring snow cover, accurate retrieval of snow depth or SWE has been challenging due to many factors [5,12,16,[45][46][47][48][49]. One of the major challenges is the poor spatial resolution of instruments [58,59].…”

Section: The Biases In Different Climatological Snow Classesmentioning

confidence: 99%

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The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

et al. 2019

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The long-term variations in snow depth are important in hydrological, meteorological, and ecological implications and climatological studies. The series of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments onboard the Defense Meteorological Satellite Program (DMSP) platforms has provided a consistent 30+ year data record of global observations that is well-suited for the estimation of snow cover, snow depth, and snow water equivalent (SWE). To maximize the use of this continuous microwave observation dataset in long-term snow analysis and obtain an objective result, consistency among the SSM/I and SSMIS sensors is required. In this paper, we evaluated the consistency between the SSM/I and SSMIS concerning the observed brightness temperature (Tb) and the retrieved snow cover area and snow depth from January 2007 to December 2008, where the F13 SSM/I and the F17 SSMIS overlapped. Results showed that Tb bias at 19 GHz spans from −2 to −3 K in snow winter seasons, and from −4 to −5 K in non-snow seasons. There is a slight Tb bias at 37 GHz from −2 to 2 K, regardless of season. For 85 (91) GHz, the bias presents some uncertainty from the scattering effect of the snowpack and atmospheric emission. The overall consistency between SSM/I and SSMIS with respect to snow cover detection is between 80% and 100%, which will result in a maximum snow cover area difference of 25 × 10 4 km 2 in China. The inconsistency in Tb between SSM/I and SSMIS can result in a −2 and −0.67 cm snow depth bias for the dual-channel and multichannel algorithms, respectively. SSMIS tends to yield lower snow depth estimates than SSM/I. Moreover, there are notable bias differences between SSM/I-and SSMIS-estimated snow depths in the tundra and taiga snow classes. Our results indicate the importance of considering the Tb bias in microwave snow cover detection and snow depth retrieval and point out that, due to the sensitivity of bias to seasons, it is better to do the intercalibration with a focus on snow-covered winter seasons. Otherwise, the bias in summer will disturb the calibration coefficients and introduce more error into the snow retrievals if the seasonal difference is not carefully evaluated and separated.

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L-Band response to freeze/thaw in a boreal forest stand from ground- and tower-based radiometer observations

Roy

Toose

Mavrovic

et al. 2020

Remote Sensing of Environment

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The Influence of Thermal Properties and Canopy- Intercepted Snow on Passive Microwave Transmissivity of a Scots Pine

Cited by 23 publications

References 52 publications

Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach

Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach

The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

L-Band response to freeze/thaw in a boreal forest stand from ground- and tower-based radiometer observations

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