Evaluation of SSM/I Filtering Algorithm for Snow Cover Identification in Northern New York State Hosni GHEDIRA, Juan Carlos AREVALO, Amir E. AZAR, and Reza KHANBILVARDI
NOAA-CREST, City University of New York, Convent Ave. at 138th street, New York, NY 10031 Telephone: (212) 650 8536, Facsimile: (212) 650 6965, Email:
[email protected] Abstract- Snow-cover parameters are being increasingly used as input to hydrological models. An accurate knowledge of the onset of snow melts and snow water equivalent values are important variables in different hydrological applications such as flooding prediction, reservoir management and agricultural activities. However, the traditional field sampling methods and the ground-based data collection are often very sparse, time consuming, and expensive compared to the coverage provided by remote sensing techniques. Microwave remote sensing techniques have been investigated by numerous researchers using various sensors and have been demonstrated to be effective for monitoring snow pack parameters such as spatial and temporal distribution, snow water equivalent, depth, and snow condition (wet/dry state). Those researches have resulted that the microwave brightness temperature and the microwave backscattering are related to the snow cover structure with different correlation degrees.
In addition to penetrating through clouds, microwaves can penetrate the snow cover and provide information about snowpack properties since the scattered emission due to the penetration is very sensitive to variation in the physical characteristics within the snow cover and snow-ground interface. Furthermore, it has been shown that snow scattering emissions are results of three major components, surface scattering, volume scattering and, subsurface or snow-ground interface scattering [3]. In dry and shallow snow the major effect comes from snow-ground interface [4]. However, in wet and deep snow surface scattering and volume scattering play the major role consecutively. The focus of this paper is to investigate the performance of filtering algorithm for global snow cover identification developed by [2] in northern New York State.
The primary objective of this research is to produce a spatial estimation of snow water equivalent in a timely fashion with sufficient spatial and temporal resolution using multi-source microwave and optical data. The final product of this project will be an additional tool for flood warning and water resource forecasts, which can be an additional input to the actual hydrological models. The contribution of remote sensing snow related information into the advanced hydrologic prediction system (AHPS) operated by NWS/NOAA (with 4 km grid resolution) will be also evaluated. This paper presents the first step of this project: data collection and evaluation.
II. DATA SET AND STUDY AREA
I. INTRODUCTION Snow coverage and depth are two key parameters that are essential to be estimated and applied in a wide range of hydrological applications. Currently, most of snow data used in hydrological applications is obtained through the use of standard and recording rain gauges, seasonal storage precipitation gauges, snow boards, and snow stakes. Direct measurements of snow depth at a single station are generally not very useful in making estimates of distribution over large areas, since the measured depth may be highly unrepresentative because of drifting or blowing [1]. Visible satellite sensors can detect snow cover only during daylight, cloud-free condition and without providing any information of snow depth. In addition, the traditional procedures used to distinguish between clouds and snow cover, require an intensive manual work and continuous human interaction [2].
0-7803-8742-2/04/$20.00 (c) 2004 IEEE
The study area is located in the north of the state of New York between 71°50’-76°25’W (Longitude) and 41°00’44°50’N (Latitude). Brightness temperature data from the current SSM/I (Special Sensor Microwave Imager) sensors on board the DMSP F13 and F14 satellites are used in both ascending and descending orbits. These images provide (twice-a-day) measurements of the brightness temperature (Tb) in different frequencies and polarizations (19 V, 19 H, 22 V, 37V, 37 H, 85 V, and 85 H). Five snow days have selected during 2001/2002 winter (12/21 to 12/25) and a total of 301 ground stations covering the study area have been identified for this experiment. Snow depth measurements have been collected from the National Climatic Data Center (NCDC) through the Cooperative Observer Network for the U.S. snow Monitoring. The snow depths measured during this 5-days period have been compiled and gridded into 25 km x 25 km grid. Additional data from stations located outside the study area have been selected to avoid any extrapolation during the gridding process. The final study area contains 15 x 12 pixels with spatial resolution of 25 km x 25 km. Only pixels having more than one inch of snow are considered as snow pixels. This information will be considered as truth data on validating the decision tree algorithm.
3677
III. METHODOLOGY A filtering algorithm for global snow cover identification was applied and evaluated over the study area. The algorithm consists of a decision tree (Fig 1) which establishes sensitive thresholds to filter out precipitation, warm desert, cold desert and frozen surfaces. As illustrated in Fig. 1, this filtering algorithm uses five of the seven channels of SSM/I (19V, 19H, 22V, 37V, and 85H). In order to assess the performance of the decision tree in identifying snow pixels, confusion matrices were calculated for each day (2 images per day on ascending and descending orbits). The confusion matrix of Table 1 illustrates clearly the well performance of the decision tree in identifying the non-snow pixels where the accuracy was varying between 79% and 99% for both ascending and descending modes. However, the performance in identifying snow pixels was very poor with an accuracy of 2% for December 23rd.
Scattering Materials or
Tb19V – Tb37V > 0
[2]
[3] [4]
No Classification
The scattering material represents the signatures of snow using vertically polarized antenna temperatures
YES
Precipitation
Tb22V ≥ 258, or 258 ≥ Tb22v ≥ 254 and Tb22V – Tb85V ≤ 2
YES
or Tb22V ≥ 165 + 0.49 Tb85V
Precipitation Pixel
NO
Cold Desert
Tb19V - Tb19H ≥ 18, and Tb19V - Tb37V ≤ 10, and Tb37V – Tb85V ≤ 10
REFERENCES [1]
NO
Tb22V – Tb85V > 0
Viessman W. and Lewis G., “Introduction to Hydrology” forth edition 1995, Harper Collins. Grody N. and Basist N., “Global Identification of Snowcover Using SSM/I Measurements”, IEEE Transaction on Geosciences and Remote sensing, Vol. 34, No. 1, January 1996. Ulaby F. , Moore R., and Fung A.,” Microwave remote sensing active and passive”, vol 1 1981, Artech House. De Sève, D., M. Bernier, J.-P. Fortin, and A. Walker, Preliminary analysis of snow microwave radiometry using the SSM/I passive-microwave data: the case of La Grande River watershed (Quebec), Ann. Glaciol., 25, 353-361, 1997
YES
Cold Desert
NO
Frozen Ground
Tb19V - Tb19H ≥ 8, and Tb19V - Tb37V ≤ 2, and Tb22V – Tb85V ≤ 6
YES
Frozen Ground
NO
Snow Cover
Figure 1. Decision Tree (Adapted from Grody and Basist, 1996 [2])
TABLE 1: CONFUSION MATRICES
Ascending Orbit
Descending Orbit
Dec 21, 2001 S NS S 0.07 0.29 NS 0.71 0.93 Accuracy = 73
S NS
S NS 0.11 0.29 0.71 0.89 Accuracy = 70
0-7803-8742-2/04/$20.00 (c) 2004 IEEE
Dec 22, 2001 S NS S 0.21 0.43 NS 0.57 0.79 Accuracy = 66
S NS
S NS 0.21 0.49 0.51 0.79 Accuracy = 67
Dec 23, 2001 S NS S 0.01 0.02 NS 0.98 0.99 Accuracy = 71
S NS
S NS 0.14 0.45 0.55 0.86 Accuracy = 74
3678
Dec 24, 2001 S NS S 0.15 0.38 NS 0.62 0.85 Accuracy = 71
S NS
S NS 0.10 0.21 0.79 0.90 Accuracy = 69
Dec 25, 2001 S NS S 0.19 0.68 NS 0.32 0.81 Accuracy = 78
S NS
S NS 0.22 0.56 0.44 0.78 Accuracy = 73
Dec 21, 2001
Dec 22, 2001
Dec 23, 2001
Dec 24, 2001
Decision Tree Results (Ascending Mode)
Decision Tree Results (Descending Mode)
Ground Stations Data
Figure 2: Comparison between the decision tree results and the gridded data
0-7803-8742-2/04/$20.00 (c) 2004 IEEE
3679
Dec 25, 2001
