Paramétrisation de la SSA dans MEMLS

Malgré la précision des nouvelles mesures de SSA de la neige, ce dernier paramètre n’a pas été validé pour la modélisation du TR MOP du couvert nival. Suite au développement des instruments IRIS et SWIRcam, un deuxième objectif secondaire de la thèse est de valider et calibrer la SSA de la neige comme paramètre de taille de grains de neige pour la modélisation du signal MOP du cou- vert nival. Des études récentes (Langlois et al., 2010a; Brucker et al., 2011) ont démontré qu’il est possible de minimiser l’erreur de la modélisation du signal MOP provenant de la paramétrisation de la taille des grains de neige en optimisant un facteur multiplicatif à la SSA. Ce travail s’inscrit donc dans le cadre de cette thèse et a été publié dans la revue IEEE Transaction on Geoscience and Remote Sensing(Montpetit et al., 2013) et a aussi fait l’objet d’une présentation à la conférence Eastern Snow Conference 2012à Frost Valley (NY), États-Unis. Ce travail a permis de démontrer qu’il est possible de réduire l’erreur de la modélisation de la TB à 11.6 K en appliquant un facteur multiplicatif de 1.3 sur le paramètre de taille de grains de neige du modèle Microwave Emission Model of Layered Snowpacks(MEMLS) issu de la SSA mesurée à l’aide des instruments IRIS et SWIRcam. L’avantage de ce résultat comparativement aux études antérieures est que les mesures de la SSA avec l’IRIS et la SWIRcam sont plus précis et reproductibles et donc les résultats des simulations avec MEMLS sont eux aussi plus reproductibles et précis. Ces résultats sont présentés dans la section III.A de l’article Montpetit et al. (2013) présentée en section 5.0.2 de cette thèse. L’acquisition, le traitement, l’analyse de données, la production des résultats ainsi que la rédaction et la révision de l’article (Montpetit et al., 2013) ont été réalisé dans le cadre de cette thèse avec l’aide des co-auteurs de l’article.

4 Résultats : Modélisation de l’émission MOP du sol gelé

Une grande incertitude de la modélisation de la TBdu couvert nival est la contribution provenant du sol. Très peu d’études récentes se sont concentrées sur la modélisation du signal MOP du sol gelé dans les régions arctiques. Il a été démontré par Roy et al. (2013) que le sol peut avoir un impact sur le signal MOP même pour des fréquences plus élevées (jusqu’à 37 GHz) pour des couverts de neige moins dense et épais. Wegmüller et Mätzler (1999) ont démontré qu’il est possible de modéliser l’émissivité du sol à ces fréquences mais Roy et al. (2013) ont démontré que ce modèle nécessitait des modifications afin que la modélisation du TR MOP du manteau neigeux soit optimale. Le travail présenté dans l’article Montpetit et al. (2013) de la section 5.0.2 démontre aussi que le

modèle MEMLS. Toutefois, ce travail était incomplet dû au manque de données géophysiques et radiométriques précises du sol. Dans l’optique d’améliorer la modélisation du TR MOP du couvert nival, l’amélioration de la modélisation de l’émissivité MOP du sol gelé s’inscrit donc dans le cadre de cette thèse. Les données utilisées pour cette section du travail ont été acquises par l’équipe neige du CARTEL et des collaborateurs dans la région de la Baie James et Umiujaq (Québec), Canada, durant les hivers 2012-2013 et 2013-2014. Les mesures de la campagne Baie James 2012-2013 ont fait l’objet d’une présentation par affiche à la conférence Eastern Snow Conference 2013 à Huntsville (Ontario), Canada. Les résultats de l’analyse de la modélisation de l’émissivité du signal MOP du sol gelé sont aussi présentés dans un article présentement en révision dans le journal Geoscience and Remote Sensing Letters (Montpetit et al., En Revision). Ce travail démontre qu’il est possible calibrer le modèle de Wegmüller et Mätzler (1999) afin de minimiser l’erreur entre la TB mesurée et simulée à 4.65 K pour des fréquences de 10.7, 19 et 37 GHz en H-Pol et V-Pol. L’acquisition, le traitement et l’analyse des mesures ainsi que la production des résultats, la rédaction et la révision (en cours) de l’article ont été fait dans le cadre de cette thèse avec l’aide des co-auteurs de l’article.

4.0.1 Article : In-situ passive microwave parameterization of sub-arctic frozen organic soils Plusieurs applications MOP telles que l’estimation de l’émissivité de surface ou l’analyse du cou- vert nival, nécessitent l’estimation de l’émission provenant du sol. De grandes incertitudes per- sistent dans la modélisation de l’émissivité du sol lorsqu’il est gelé. Dans cet article, une méthode empirique pour estimer l’émission de sols organiques de l’Arctique canadien à 10.7, 19 et 37 GHz est présentée. Cette méthode a été calibrée et validée à l’aide de mesures radiométriques in-situ à des angles d’incidences de 0o _{à 60}o _{de sols du nord-est de l’Arctique canadien. Les valeurs de} permittivités effectives retrouvées donnent une RMSE entre les TB mesurées et simulées de 4.8 K considérant toutes les fréquences. Cette méthode présente un très grand potentiel dans l’amé- lioration de l’estimation de la contribution du sol gelé à la TB du couvert nival. L’originalité de ce travail provient du fait que très peu d’études ont acquis le type de mesures radiométriques (TB multi-angles et multifréquences) pour évaluer l’émission de sols organiques en milieu Arctique. Les tableaux 4.1 et 4.2 montre les résultats principaux de ce travail.

Tableau 4.1 – Permittivité effective (ǫ′

ef f), rugosité de surface effective(σef f) ainsi que le paramètre de dépolarisation (β) retrouvés pour des sols organiques de l’Arctique canadien. — Effective permittivity (ǫef f), effective soil surface roughness (σef f) and depolarisation coefficient

(β) retrieved for organic soils of the canadian Arctic. f (GHz) ǫ′ ef f σef f β 10.7 3.20 0.19 1.08 19 3.45 0.72 37 4.53 0.42

Tableau 4.2 – Biais et RMSE entre lesTB mesurées et simulées pour six canaux pour des sites de sols organiques de l’Arctique canadien. — Bias and RMSE between modeled and measuredTB

for six channels for organic soil sites of the canadian Arctic.

Canal Biais (K) RMSE (K)

11V -1.3 1.6 11H -0.5 4.0 19V -2.2 4.4 19H -1.5 3.9 37V 0.6 5.8 37H 2.2 6.0 Tous -0.5 4.8 34

In-situ passive microwave parameterization of

1

sub-arctic frozen organic soils

2

Benoit Montpetit

∗

, Alain Royer

∗

, Alexandre Roy

∗

and Alexandre Langlois

∗ ∗

Centre

3

d’Application et de Recherche en T´el´ed´etection, Universit´e de Sherbrooke, Sherbrooke,

4

Qu´ebec, Canada

5

Abstract

6

Many applications such as passive microwave emissivity estimates or snow cover analysis need to take into

7

account the soil contribution to the overall signal emission. Soil emission modeling presents large uncertainties

8

when the soil is frozen. In this paper, an empirical retrieval method is presented, specifically for rough frozen soil

9

permittivity estimates at 10.7, 19 and 37 GHz. The method was tested and validated using in-situ passive microwave

10

measurements at incidence angles from 0 to 60o

of sub-arctic frozen organic soils in Northeastern Canada. The

11

retrieved permittivity values give an overall RMSE between the measured and simulated brightness temperatures

12

of 4.57 K for all frequencies combined. The method shows great potential to improve the estimation of the frozen

13

soil contribution to the measured passive microwave brightness temperature.

14

Index Terms

15

Passive Microwave, frozen soil, rough soil, dielectric soil properties, soil reflectivity modeling.

16

I. INTRODUCTION

17

F

OR many years, scientists have studied bare soil reflectivity modeling ( [1]; [2]; [3]; [4]; [5]), partly

18

motivated by the ability to retrieve soil moisture from satellite data; such as the Soil Moisture and

19

Ocean Salinity (SMOS) mission [6]. The future Soil Moisture Active and Passive (SMAP) mission [7]

20

will also study soil moisture from satellite-based data, but will also study the soil state (frozen/thawed) in

21

northern regions [8]. The majority of these studies mainly focus on L-band rather than higher frequencies

22

( [9]; [10]; [5]; [11]; [12]), given the ability of such wavelengths to penetrate vegetation and snow. For

23

cryosphere studies frequencies up to 37 GHz are commonly used [13]. Some studies have shown that, even

24

at these high frequencies, the soil contribution to the emitted signal at the surface of the snowpack has to

25

be estimated ( [14]; [15]). A major issue with the estimation of the soil contribution to the emitted signal

26

at the surface is the estimation of frozen soil permittivity where the real part drastically changes when the

27

soil freezes and the very low imaginary part controls the penetration depth [16]. Different models exist

28

but still need soil in-situ measurements (soil bulk density, volumetric moisture, temperature, etc.) that are

29

complex to extract in remote arctic soils.

30

In this paper, we present a simple method to retrieve passive microwave soil properties from bright-

31

ness temperature measurements using the semi-empirical model developed by Wegm¨uller & M¨atzler [3]

32

(hereafter referred to as WM99) and validate these properties using an independent dataset taken at the

33

calibration site and another site in northern Qu´ebec (see Section II). We first describe the study sites and

34

the geophysical and radiometric measurements. Then, the models and the optimization method will be

35

detailed. Finally, the optimization and validation results will be presented and discussed.

36

II. STUDY SITES AND MEASUREMENTS

37

The soil and radiometric data for this study were first collected in the James Bay area, Qu´ebec (53o26’N,

38

76o46’W, 186 m a.s.l.) during three intensive measurement periods (IMP) in January (8th to 12th, IMP1),

39

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. XX, NO. X, JULY 2014 2 0 10 20 30 40 50 60 235 240 245 250 255 260 θ (o) Tb (K) 10.7V Meas. 10.7V Sim. 10.7H Meas. 10.7H Sim. 19V Meas. 19V Sim. 19H Meas. 19H Sim. 37V Meas. 37V Sim. 37H Meas. 37H Sim.

Fig. 1. Typical angular T b measurements (points) acquired at the BJCal site and the resulting simulations (lines) using the WM99 model with the optimized parameters of Table I at 10.7 (black circles and lines), 19 (light gray squares and lines) and 37 (dark gray triangles and lines) GHz at V-Pol (filled shapes and full lines) and H-Pol (empty shapes and dotted lines). T_soilef f= −5 ± 2.5o_C.

February (12th to 17th, IMP2) and March (19thto 23rd, IMP3) of 2013. More data was also acquired near

40

Umiujaq, Qu´ebec (56o_{33’N, 76}o_{30’W, 74 m a.s.l.) during one intensive period in January (21}st _{to 28}th _of 41

2014). The bare soil measurements were done in clearings with minimal influence from the environment

42

(topography, vegetation, etc.) to the measured microwave brightness temperature (T b). A total of 8 sites

43

were selected in this study where the soil composition mainly consisted of organic matter.

44

The soil T b measurements were acquired using surface-based radiometers on a mobile sled at 10.7,

45

19 and 37 GHz in both horizontal (H-Pol) and vertical (V-Pol) polarizations. Calibrations were done on

46

a regular basis throughout the winter season using a warm and cold target as described by Asmus and

47

Grant [17]. The downwelling T bskywere estimated with an atmospheric absorption microwave model [18]

48

implemented in the Helsinki University of Technology (HUT, [19]) model, using the 29 atmospheric layers

49

above surface of the North American Regional Reanalysis (NARR) data. The measured Tb at frequency

50

f and polarization p can then be described by:

51 T bsoil(f, p) = (1 − Γf,p)T ef f soil + Γf,pT b ef f sky(f, p) (1)

whereΓf,p is the rough soil reflectivity at polarization p and T ef f

soil is the effective soil physical temperature. 52

Temperature profiles were taken using a Traceable 2000 digital temperature probe with an accuracy of

53

0.1 o_{C for depths of 0 to 10 cm with an interval of 2 cm. Since the soil is considered a homogeneous}

54

medium for this study, the effective soil temperature was estimated to be the averaged temperature over

55

the first 5 cm.

56

Among the 8 sites analyzed, a James Bay site, measured on February 13th 2013, was considered for

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model calibration purposes (hereafter referred to as the BJcal site) since it was the only site where a wide

58

range of incidence angle (0o _{to 60}o_{) was measured with the surface-based radiometer. The site consisted}

59

of a bare soil area where the snow was removed (20 m long by 5 m wide) to eliminate any possible

60

contribution coming from the snow walls around the bare soil surface. Measurements over the 20 m long

61

transects were taken in order to establish the variability of the soil T b measurements. The averaged soil

62

temperature over the first 5 cm was -5 ± 2.5 oC. The same site was revisited during the winter IMPs and

other sites were measured during the 2013 winter campaign for validation purposes (hereafter referred to

64

as BJval sites). The BJval sites were also clearings and soil temperatures varied from -13oC to -5oC. Three

65

other validation sites were measured during the 2014 winter campaign in Umiujaq (hereafter referred to

66

as UMIval sites). The UMIval sites were clearings and soil temperatures varied from -17o _{C to -10}o_C.

67

III. METHOD

68

The WM99 model describes the rough soil reflectivity at a frequency f and polarization p (Γf,p) by

69

its smooth Fresnel reflectivity in H-Pol (ΓF resnel

f,p ) which depends on the incidence angle (θ) and the

70

real part of the soil permittivity (ǫ′_{), weighted by an attenuation factor that depends on the standard}

71

deviation in height of the surface (soil roughness, σ), the measured wavenumber (k) and a polarization

72

ratio dependency factor. As shown in [14], a modification to this model was applied for this study using

73

a β factor to take into account the frequency dependency of the polarization reflectivity ratio. The WM99

74

model for incidence angle lower than 60o _{is therefore described by:}

75 Γf,H(θ, ǫ′f, σ) = Γ F resnel f,H (θ, ǫ′f) exp(−(kσ) √ −0.1 cos θ_{) (2)} Γf,V(θ, ǫ′f, σ) = Γf,H(θ, ǫ′f, σ) cos βf_{θ (3)}

The first part of the optimization process consisted in obtaining the soil permittivity (ǫ′

f) for each 76

frequency and surface roughness ( σ) that minimized the error between simulated and measured T b in

77

H-pol using Eq. 2 for all the incidence angles between 0o and 60o. The optimization algorithm used is

78

the least squared error minimization algorithm of Levenberg-Marquardt ( [20], hereafter referred to as

79

LM63). Afterwards, using the soil properties of the first step, the βf factor was determined with Eq. 3

80

using the LM63 algorithm to minimize the error between the V-pol T b measurements and the simulations.

81

Once the optimized parameters were obtained, these retrieved parameters were inserted in the WM99 and

82

the soil T b were modeled and compared to the measured T b of the BJval sites. Finally, these parameters

83

were validated against T b measurements acquired at another northern sites (UMIval sites).

84

IV. RESULTS

85

Table I shows the optimization results using the LM63 algorithm at H-Pol and V-Pol. The final RMSE

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obtained for each optimization is given. Since the soil roughness is considered as a geophysical parameter

87

specific to the soil, it can be considered frequency independent ( [3]; [21]) and a different soil permittivity

88

per frequency was obtained with the LM63 optimization on H-Pol measurements. It should be noted that

89

since no soil surface roughness measurements were acquired, the surface roughness value (σ) obtained in

90

Table I is an effective parameter retrieved through model inversion and should not be considered as a true

91

geophysical parameter. An optimization per frequency was done for the βf factor in V-Pol to determine

92

if the βf factor was frequency dependent. Given the different values of βf obtained, it can be concluded

93

that the βf factor is frequency dependent as mentioned by [14].

94

TABLE I

TABLE1: FROZEN SOIL PERMITTIVITY AND ROUGHNESS(EQ. 2)AND THEβfFACTOR(EQ. 3)RETRIEVED THROUGH THELM63 OPTIMIZATION PROCESS USING THEH-POLT bANDV-POLT bRESPECTIVELY,AT10.7, 19AND37 GHZ IN THEWM99. THE

RESULTINGRMSEOF THE OPTIMIZATION PROCESS IS ALSO GIVEN.

Frequency Soil Soil RMSE RMSE

(GHz) Permittivity Rougness T b(H-Pol) βf T b(V-Pol)

(cm) (K) (K)

10.7 3.20 1.08 0.93

19 3.45 0.19 1.23 0.72 0.75

37 4.53 0.42 0.43

Figure 1 presents the typical angular T b measurements acquired at the James Bay site (BJcal). The

95

results of the modeling using the WM99 model and the parameters of Table I are also given in Figure

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. XX, NO. X, JULY 2014 4 220 225 230 235 240 245 250 255 260 265 220 225 230 235 240 245 250 255 260 265 Tb Measured (K) Tb Simulated (K) 10.7 GHz V−Pol 10.7 GHz H−Pol 19 GHz V−Pol 19 GHz H−Pol 37 GHz V−Pol 37 GHz H−Pol RMSE(10.7V)= 1.58 K RMSE(10.7H)= 4.00 K RMSE(19V)= 4.42 K RMSE(19H)= 3.92 K RMSE(37V)= 5.78 K RMSE(37H)= 5.95 K RMSE(All)= 4.65 K

Fig. 2. Validation results between modeled and measured T b for a frozen bare soil using the WM99 model and the parameters of Table I at 10.7, 19 and 37 GHz at both H-Pol and V-Pol for the James Bay sites (BJcal and BJval) (black) and the Umiujaq sites (UMIval) (gray). The RMSE of the different channels are given. The solid black line corresponds to the 1:1 line.

1. Figure 1 shows the decrease in T b with increasing incidence angle. This means that the reflectivity

97

increases with incidence angle (Eq. 1). This result is similar to what was previously obtained by [3]. It

98

also shows that the decrease is stronger in H-Pol compared to V-Pol. This can be explained by a stronger

99

increase of the surface reflectivity with incidence angle in H-Pol compared to V-Pol.

100

Figure 2 shows the results of comparison between the simulations using the WM99 model optimized

101

with the parameters of Table I against the measured T b of the James Bay sites (BJcal and BJval sites)

102

acquired throughout the 2013 winter, and the Umiujaq sites (UMIval). The measured and simulated T b

103

are, in general, in agreement with a RMSE ranging from 1.6 to 6.0 K (overall RMSE = 4.65 K). There is

104

some scatter especially at 19 and 37 GHz (overall R2

=0.64) which could be explained by the roughness

105

variability (see discussion below) and the uncertainty in the downwelling T b where the surrounding

106

environment can be slightly more important than the estimated T bsky.

107

The RMSE obtained at James Bay (4.58 K) is similar to the one of the Umiujaq sites (4.49 K) which

108

suggests that the optimization process could be applied to other sites characterized by similar northern

109

organic frozen soil composition. Table II summarizes the errors obtained between the simulations and the

110

measurements for both the James Bay and Umiujaq campaigns and two campaigns combined. The mean

111

bias appears relatively small at -0.45 K. The overall RMSE is comparable to the measured T b variability

112

over a 20 m transect which was of 5.18 K, 4.41 K and 6.79 K at 37, 19 and 10.7 GHz respectively. This

113

variability can be explained by the surface roughness variability. As a matter of fact, over this transect,

114

keeping the same permittivity as obtained above (Table I) and fitting an optimized roughness value for

115

each site, over a range of 0 to 2 cm, reduces the RMSE from 6.07 K to 0.37 K. The range of surface

116

roughness obtained in this optimization process was 0.01 ≤ σ ≤ 2 cm, 0.36 ≤ σ ≤ 2 cm and 0.01 ≤ σ ≤

117

0.23 cm at 10.7, 19 and 37 GHz respectively.

118

It is difficult to compare the real part of the permittivity values (ǫ′

f) obtained in this study to previous 119

studies because of all the factors and conditions (soil types, soil ice fraction, soil temperatures, frequencies,

120

etc.) that influence these values. This information is not always available. As reference, from 1 to 100 GHz

121

and at a temperature of 0 o_{C, ǫ}′

f of pure ice is constant at ≈ 3.2 (with a weak temperature dependence),

122

where ǫ′

f of free water decreases following a power function from 90 to 6 [16] (according to [22], we

123

get ǫ′

f of pure water at 0 oC of 85.9, 38.3, 20.5 and 10.2 at 1.4, 11, 19 and 37 GHz respectively).

TABLE II

MEAN BIASES ANDRMSEBETWEEN SIMULATED AND MEASUREDT bAT10.7, 19AND37 GHZ AT BOTHH-POL ANDV-POL FOR THE JAMESBAY ANDUMIUJAQ MEASUREMENT CAMPAIGNS. THE OVERALL MEAN BIAS ANDRMSEARE ALSO GIVEN.

Channel James Bay Umiujaq All

Bias (K) RMSE (K) Bias (K) RMSE (K) Bias (K) RMSE (K) 10.7V -1.36 3.15 -0.73 3.40 -1.26 1.58 10.7H -0.10 3.77 -2.83 5.03 -0.53 4.00 19V -2.19 4.31 -2.24 4.97 -2.20 4.42 19H -1.34 3.57 -2.50 5.44 -1.52 3.92 37V 1.36 6.01 -3.26 4.38 0.63 5.78 37H 2.97 6.27 -2.02 3.83 2.18 5.95 10.7 -0.73 3.47 -1.78 4.29 -0.89 3.62 19 -1.76 3.95 -2.37 5.21 -1.86 4.18 37 2.17 6.14 -2.64 4.12 1.41 5.87 All -0.11 4.67 -2.26 4.56 -0.45 4.65

Rautiainen et al. [23] showed that at L-band the soil permittivity varied from 3.3 to 3.8 in frozen soils

125

having temperature ranging between -6 o_{C to -3.7} o_{C but measured in-situ permittivity values of 6.3 to}

126

7.1 for a frozen wetland site in Northern Finland [24]. Schwank et al. [25] showed that for L-band, the

127

frozen soil permittivity varies from 3.5 to 4.5, while Hallikainen et al. [26] showed that it varies from 5

128

up to 8 in the 10 to 18 GHz frequency range. Puilliainen et al. [27] used a fixed value of 6 to simulate

129

the frozen soil T b at 19 and 37 GHz. Finally, Mironov et al. [28] developed a temperature dependent

130

permittivity model and showed that the permittivity could vary from 3 to 4.5 in the 10 to 16 GHz range

131

for a frozen soil at -25oC. Here, we obtained values of 3.20 at 10.7 GHz, 3.45 at 19 GHz and 4.53 at 37

132

GHz for soils between -13oC to -5oC (the first 5 cm). This is in agreement with these different studies

133

where for lower frequencies, we have a lower soil permittivity. The retrieved values of this study might

134

have been different if another rough bare soil reflectivity model was used (e.g. [2], [21]) but such models

135

had too many parameters to inverse in the optimization process to be an efficient method of retrieving

136

effective soil parameters.

137

As mentioned above, one of the main difficulties in the retrieval process is the combined effect of

138

permittivity and the surface roughness. Figure 3 shows the sensitivity of the mean measured-simulated

139

T b (V & H) RMSE on the soil permittivity and the surface roughness. The simulations conditions are

140

those of the BJcal site.

141 ε’ (10.7 GHz) 5 4.5 4 3.5 3 2.5 2 ε’ (19 GHz) 5 4.5 4 3.5 3 2.5 2 2 4 6 8 10 12 14 16 18 20 ε’ (37 GHz) σ (cm) 0 0.25 0.5 0.75 1 5 4.5 4 3.5 3 2.5 2 RMSE (K)

Fig. 3. Sensitivity test of the T b RMSE (in K, color scale) on the soil permittivity (ǫ′_{) and the surface roughness (σ) on the BJcal site} measurements (Figure 1), and for 3 frequencies.

Figure 3 shows that the sensitivity to the surface roughness is weaker than for the permittivity. Note

142

that the pattern for 1 ≤ σ ≤ 2 cm (not shown) can be extrapolated from the one around σ=1 cm, and

143

that for σ=0 cm, a discontinuity results from the abrupt change from the Fresnel reflectivity to the rough

144

surface reflectivity (Eq. 2). For σ < 1 cm, it appears that it exist an ensemble of (ǫ′_{, σ) couples that} 145

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. XX, NO. X, JULY 2014 6

provides an ensemble of solutions with low RMSE (dark blue in Fig. 3). However, when combining the

146

three frequencies showing different behaviour for the lowest RMSE values, the range where the minimum

147

RMSE combining all three frequencies occurs for a limited range of σ, 0.1 < σ < 0.23 cm. The optimized

Dans le document Analyse de la modélisation de l'émission multi-fréquences micro-onde des sols et de la neige, incluant les croutes de glace à l'aide du modèle Microwave Emission Model of Layered Snowpacks (MEMLS). (Page 52-188)