Capabilities and Limitations of Neural Networks in Snow Cover
Mapping from Passive Microwave Data
Juan C. Arevalo ( NOAA/CREST Graduate student) jc@ce.ccny.cuny.edu, Hosni Ghedira (Assistant Professor) ghedira@ce.ccny.cuny.edu, Reza Khanbilvardi (Professor) rk@ccny.cuny.edu.
The City College of the City University of New York. Convent Avenue at 140th St, Steinman Hall, New York, NY. 10031.
2. Study Area, Data Acquisition and Remote Sensing Data
1. Introduction
SSM/I Images
Snow-cover parameters are being increasingly used as input to hydrological models.
Having an accurate estimation of the snow cover characteristics during the snowmelt
season is indispensable for an efficient hydrological modeling and for an improved
snowmelt runoff forecasts.
Passive 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 (SWE), depth, and snow condition (wet/dry state). However, the snow
products derived from passive microwave sensors are usually limited by the relatively
low resolution, especially when the purpose is to use this product as input for
hydrological models. Moreover, the accuracy of passive-microwave-based maps is
usually affected by the presence of vegetation.
In this project, we used an adaptive neural network system to generate the spatial
distribution of snow accumulation from multi-channel SSM/I data in the Northern
Midwest of the United States. Five SSM/I channels were used in this experiment (19H,
19V, 22V, 37V, and 85V). The normalized difference vegetation index (NDVI) is used
from the AVHRR to quantify the vegetation dynamic in the snow mapping process. Six
snow days with high snow accumulation have been selected during the 2001/2002
winter season to train and test the neural network system. The snow depths and NDVI
values have been compiled and gridded into 25 km x 25 km grid to match the final
SSM/I resolution. To ensure an accurate selection of training pixels, different
approaches have been tested by varying the selection criteria of snow pixels. The final
results have shown the importance of these selection criterions on the neural network
performance.
3. Data Acquisition: Vegetation
AVHRR Image
The Normalized Difference Vegetation Index (NDVI) has been derived from
the visible and the near-infrared channels of NOAA-AVHRR Sensor over our
study area.
8-Km, 10-days composite
image, January 21-31 1994.
NDVI
48.7
Jan17 19H
Jan18 19H
Jan24 19H
Jan25 19H
Six days have been selected during the
2001/2002 winter season (01/16, 01/17,
01/18, 01/23, 01/24, and 01/25).
The following illustrations show the
channel 19H of SSM/I satellite with 25
km resolution for the six selected days.
Source of Data:
Ground data from National Oceanic and
Atmospheric Administration, NOAA
SSM/I data from DMSP SSM/I Pathfinder daily
EASE-Grid brightness temperatures, January
2002. Boulder, CO: National Snow and Ice Data
Center, NSIDC (Armstrong, R.L., K.W. Knowles,
M.J. Brodzik and M.A. Hardman. 1994,
updated current year).
The study area is located in the Northern Midwest of the United States within 110˚37’48’’W - 102˚02’24’’W
and 48˚42’36’’N - 40˚43’48’’N.. The passive microwave data from the NOAA/NASA Pathfinder Program
Special Sensor Microwave/Imager (SSM/I) Level 3 Equal Area Scalable Earth-Grid (EASE-Grid) Brightness
Temperatures F13 satellite is used in both ascending and descending orbits. These images provide
measurements of the brightness temperature in seven channels with different frequencies and polarizations (19
V, 19 H, 22 V, 37V, 37 H, 85 V, and 85 H).
Jan23 19H
Original gridded data in Northern
Hemisphere
projection
with
coordinates: 119˚44’W - 99˚57’W
and 49˚36’W - 34˚34’W
4. Artificial Neural Network
6. Neural Network Results
8. Snow Cover Maps
The available training data, has been divided into three subsets:
The graph below shows the accuracy variation of 100 neural network trained with different initial configurations. Threshold 0.6 for the
approach 4, which yields the net with the highest accuracy. That net was used to simulate the corresponding snow maps.
The following color images represent two snow-cover maps for each selected day
generated from the artificial neural network output. Each map contains 34 X 30
pixels with spatial resolution of 25 km.
• The first one is the learning set, whish is used for computing and updating the network weights.
• The second subset is the validation set, which is used for stopping the training by monitoring
the validation error during the training process.
• The third subset is the test set that is not used during the training process, and it is only used to
assess the classification accuracy and to compare between different classifiers and different
network configurations.
25 Km – NDVI Image
Jan16 19H
Study Area, Covered by SSM/I
34x30 pixels
Approach 4. Five channels,Tb + standard deviation of NDVI. Threshold 0.6
100
%
5 Channels
90
5 Channels + St Dev NDVI
48.7
80
48.7
70
46.7
No
Cover
age
46.7
60
Network Architecture : [5:10:10:1]
44.7
50
The 190 training pixels have been
set as follow:
42.6
Input layer
2 hidden layers
40
Output layer
30
Learning set
40.7
1110.6
108.8
106.5
104.4
90 pixels
20
102.0
10
Validation set
The AVHRR data was acquired from the
Distributed Active Archive Center (DAAC)
located at Goddard Space Flight Center,
NASA.
45 pixels
0
1
Test set
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21 22
23 24
25 26
27 28 29
30
31 32
33 34
35 36
37 38 39
40
41 42
43 44
45 46
47 48
49
50
51 52
53
54
55
56
57
58
59 60
61 62 63
64
65 66
67 68
69
70
71
72
73
74
75
76
77
78
79
80
81 82
83 84
85 86
87 88 89
90
91 92
93 94
95 96
97 98 99 100
Run
50 pixels
The overall accuracy varies between 60 and 80 %, having a fairly stable pattern. However, some high and low peaks have been
observed; 86 % and 52% being the highest and lowest accuracy. The Kappa coefficient, which assess the agreement in the
classification between the snow and non-snow pixels, has a very irregular pattern.
25 Km – St Dv. NDVI Image
48.7
The original 8 Km NDVI values have
been gridded into a regular grid of 25 Km
over the study area.
2
46.7
42.6
110.6
108.8
106.5
104.4
22 V
102.0
%
90
70
60
37 V
50
40
30
20
85 V
10
0
Latitud
49 [N]
0
0.2
0.4
0.6
0.8
47
46
1
During the simulation process, a continuous range from zero to one will
be produced by the output neuron. We have introduced a threshold value
(between 0 and 1) to decide if the pixel will be classified as snow or nosnow pixel.
The optimal threshold value cannot be identified with certainty without
measuring its effect on the overall accuracy of the neural network
classification. In this project, the threshold value has been varied from 0.2
to 0.8. The effect of the decision threshold on classification accuracy of
each class is illustrated in the following figure:
Threshold
48
Graph shows the pattern and difference from the
truth data; snow/no-snow and the ANN result
The input layer size may vary depending
on the approach used; from 5 to 7 neurons.
Actual snow depth from the truth data and the ANN result
45
1
42
ANN
0.8
0.8
5. Neural Network Approaches
Snow - Snow
Non snow – Non snow
41
Threshold 0.4
Results obtained for the four approaches (number of
input channels): 1. the 5 brightness temperature (Tb)
channels; 2. the 5 Tb plus NDVI; 3. the 5 Tb plus NDVI
and standard deviation, and 4. the 5 Tb plus the
standard deviation of NDVI.
40
%
39
95
90
85
38
108
107
106
105
104
103
102
101
100
99
98
Longitud [W]
80
75
70
Threshold
0.6
Overall accuracy
Kappa coefficient
0.4
0.6
Threshold
43
1
ANN
44
109
40.7
110.6
Jan 17
44.7
42.6
40.7
108.8
106.5
106.4
102.0
110.6
48.7
48.7
46.7
46.7
44.7
44.7
42.6
42.6
108.8
106.5
104.4
10.2.0
108.8
106.5
104.4
102.0
108.8
106.5
104.4
102.0
108.8
106.5
104.4
102.0
108.8
106.5
104.4
102.0
108.8
106.5
104.4
102.0
40.7
40.7
110.6
108.8
106.5
104.4
102.0
110.6
48.7
48.7
46.7
46.7
44.7
44.7
42.6
42.6
100
40.7
50
110
No
Snow
42.6
For each vector of five brightness temperatures presented to the input layer, a value equal to one will be assigned in the output layer
if the presented vector correspond to a snow pixel. Otherwise, a value equal to zero will be assigned to the corresponding vector.
19 V
A total of 195 ground stations covering the study area have been identified
for this experiment. The figure below shows the distribution of the ground
stations over the study area (red rectangle).
111
Snow
Effect of the decision threshold on classification
Ground data distribution
112
44.7
19 H
80
113
Jan 16
44.7
The standard deviation values have been
measured during the gridding process to
quantify the vegetation homogeneity for
each pixel.
114
The 5 channels
maps (first
column) represent
the simulation
generated by
simulating the
trained neural
network in the
approach 1,
(threshold = 0.4).
46.7
The other maps (2nd
column) represent
the simulation
results of neural
network trained with
the approach 4 by
using 5 SSM/I
channels plus the
standard deviation
of the NDVI as
input. For this
configuration, the
best performance
was obtained with a
threshold equal to
0.6.
Jan 18
40.7
40.7
110.6
Jan 23
108.8
106.5
104.4
102.0
110.6
48.7
48.7
46.7
46.7
44.7
44.7
42.6
42.6
40.7
40.7
110.6
108.8
106.5
104.4
102.0
110.6
0.4
48.7
48.7
46.7
46.7
44.7
44.7
42.6
42.6
0.2
0.2
0
0
0
0
65
1
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Truth data – snow depth [in]
Truth data
Jan 24
60
Ground snow map
Threshold 6
55
50
%
45
48.7
Only pixels with ground
stations
inside
their
boundaries are considered
for
the
training
and
validation
of
Neural
Network. A total of 165
pixels satisfy this criterion.
46.7
44.7
42.6
95
40
90
35
85
30
80
25
75
20
70
5Tb
NDVI
S-NDVI
StDv
65
60
55
50
Highest Overall accuracy
45
40
Average overall accuracy and standard deviation
Average Kappa coefficient and its standard deviation
35
30
25
20
40.7
110.6
5Tb
108.8
106.5
104.4
102.0
NDVI
S-NDVI
StDv
A total of 100 runs of the neural
network for each approach have
been performed.
The following graphs show for each
approach the average accuracy
and the Kappa coefficient for the
100-runs and their corresponding
standard deviation. These results
have shown that the addition of
the NDVI standard deviation
(homogeneity factor) improves the
snow identification accuracy.
7. Confusion matrices
110.6
As a part of the assessment of the capabilities and limitations of neural networks for snow mapping, the neural network output has
been evaluated with a confusion matrix that was computed for each approach. The overall accuracy and Kappa coefficient were
measured. The following matrices correspond to the net giving the highest accuracy out of 100 runs.
5 Channels
S
NS
S
0.95
0.24
NS
0.05
0.76
Accuracy = 84 Kappa = 68.4
5 Channels + NDVI
S
NS
S
0.96
0.17
NS
0.04
0.83
Accuracy = 90 Kappa = 79.67
40.7
40.7
5 Channels+NDVI+St Dev. NDVI
5 Channels+St Dev. NDVI
S
NS
S
0.88
0.08
NS
0.13
0.92
Accuracy = 90 Kappa = 79.94
S
NS
S
0.80
0.08
NS
0.20
0.92
Accuracy = 86 Kappa = 72.00
Jan 25
108.8
106.5
104.4
102.0
110.6
48.7
48.7
46.7
46.7
44.7
44.7
42.6
42.6
40.7
40.7
110.6
108.6
106.5
104.4
102.0
110.6