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
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
An appraisal of the accuracy of operational soil moisture
estimates from SMOS MIRAS using validated in-situ observations
acquired in a Mediterranean environment
George Petropoulos1,*, Gareth Ireland1, Prashant K. Srivastava2,3
and Pavlos Ioannou-Katidis1
1Department
of Geography and Earth Sciences, University of Aberystwyth, UK
Goddard Space Flight Center, Greenbelt, Maryland, USA
3Earth System Science Interdisciplinary Center, University of Maryland,
Maryland, USA
2NASA
Acquiring information on the spatio-temporal variability of soil moisture is of key
importance in extending our capability to understand Earth’s system physical
processes, and is also required in many practical applications. To this end, Earth
Observation (EO) provides today a promising avenue, with a number of products
even distributed at present operationally. Validation of such products at a range
of climate and environmental conditions across continents is a fundamental step
related to their practical use. Various in-situ soil moisture ground observational
networks have been established globally providing suitable data for evaluating
the accuracy of EO-based Soil Moisture products.
This study aimed at evaluating the accuracy of soil moisture estimates provided
from the Soil Moisture and Ocean Salinity Mission (SMOS) global operational
product at test sites from the REMEDHUS International Soil Moisture Network
(ISMN) in Spain. For this purpose validated observations from in-situ ground
observations acquired nearly concurrent to SMOS overpass were utilised. In
overall, results showed a generally reasonable agreement between the SMOS
product and the in-situ soil moisture measurements in the 0-5 cm soil moisture
layer (Root Mean Square Error (RMSE) = 0.116 m3 m-3). A clear improvement in
product accuracy for the overall comparison was shown when days of high Radio
Frequency Interference (RFI) were filtered out (RMSE = 0.110 m3 m-3). Seasonal
analysis showed highest agreement during autumn, followed by summer, winter
and spring seasons. A systematic soil moisture underestimation was also found
for the overall comparison and during the four seasons. In overall, the result
provides supportive evidence of the potential value of this operational product
for meso-scale studies and practical applications.
KEYWORDS: SMOS, soil moisture, operational products, ISMN, REMEDHUS
*Corresponding author. Email: gep9@aber.ac.uk
1. Introduction
Developing an understanding of the interactions and extreme complexities of
Earth's natural processes has been identified today as one of the most urgent and
important research priorities of the global scientific community (Battrick et al., 2006;
Petropoulos et al., 2014). The spatio-temporal distribution of terrestrial soil moisture
is an important parameter that controls several hydrological and atmospheric
2
56
57
58
59
60
61
62
63
64
65
processes, and is a key variable in controlling the energy and mass exchanges at the
land surface/ atmosphere interface (Schlenz et al., 2012; Srivastava et al. 2013a). Thus,
frequent and global soil moisture observations are crucial to many environmental
disciplines and crosscutting scientific applications. For example, soil moisture is an
essential variable in improving meteorological modeling (Nandintsetseg and Shinoda,
2011), hydrological forecasting (Milzow et al., 2011), forecasting of agricultural
prospects (Deutsch et al., 2010), and natural disasters prediction such as flood and
drought (Camici et al., 2011), amongst other bio-geophysical applications. Therefore, it
is very important to accurately monitor and estimate the spatial variations of soil
moisture over various time scales and terrain types (Srivastava et al., 2013a).
66
67
68
69
70
71
72
73
74
75
In view of the importance of information on the spatial distribution of soil
moisture, various methods have been developed for directly measuring this parameter
in the field (see recent methods’ review by Petropoulos et al., 2013). The use of ground
instrumentation has certain advantages, such as a relatively direct measurement,
instrument portability, easy installation operation and maintenance, the ability to
provide measurement at different depths and also the relative maturity of the
methods. Yet, the use of traditional soil moisture observation techniques is very
difficult. This is due to the large spatial and temporal variability of soil moisture, and
its variation within the vertical soil profile (Al-Shrafany et al., 2012a; Al-Shrafany et al.,
2012b; Petropoulos et al., 2013).
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
Advances in Earth Observation (EO) technology over the last two decades or so
have resulted to the development of different methods in extracting information on
the spatio-temporal variation of soil moisture, at different observational scales, using
sensors operating in all regions of the electromagnetic spectrum (Wagner et al., 2007;
Draper et al., 2012; Barrett & Petropoulos, 2013). During the last three decades or so a
number of researchers have shown that near surface soil moisture can be estimated by
optical and thermal infrared (Sandholt et al., 2002; Carlson, 2007; Mallick et al., 2009),
as well as passive and active microwave remote sensing techniques (Jackson, 1993;
Owe et al., 2001; Scott and Bastiaanssen, 2003). Notably, microwave sensors are the
most utilised platform for soil moisture estimation in recent years. This is largely due
to the direct relationship between soil moisture and the soil dielectric constant.
However, the majority of microwave sensors operate within higher frequencies
(>5GHz), whereas generally the protected microwave range of 1-2GHz (L-band) is
more relevant to soil moisture estimation. This is due to its high sensitivity to soil
moisture and the robustness of the sensor to surface roughness, the presence of
vegetation canopies and atmospheric effects (Peischl et al., 2014).
92
93
94
95
96
97
98
99
100
101
102
103
Furthermore, several EO sensors have recently been designed specifically to
sense soil moisture, including the European Space Agency’s Soil Moisture Ocean
Salinity (SMOS) mission, the Meteorological Operational satellite programme (MetOpA) Advanced Scatterometer (ASCAT) mission and NASA’s Soil Moisture Active Passive
(SMAP) mission, planned to launch in 2014. The use of these satellite-derived soil
moisture estimates for scientific and operational geophysical applications is advancing
rapidly due to improvements in sensor technology, and retrieval algorithms, and the
relative usability of the data (Su et al., 2013). However, validation is important for any
satellite–based remote sensing product including soil moisture. This will assist in
appraising not only the actual accuracy of the delivered soil moisture estimates but
also the magnitude and spatial structure of the uncertainties of any new satellite–
based remote sensing product before operational application (Wanders et al., 2012).
Downloaded by [Aberystwyth University] at 08:26 20 March 2014
3
104
105
106
107
108
109
110
111
Numerous studies have been dedicated to assessing the accuracy of the SMOS
soil moisture product by comparing the estimations against ground measurements
from monitoring networks around the world, e.g., in South America (Escorihuela et al.,
2012), Europe (Lacava et al., 2012; Schlenz et al., 2012), Australia (Panciera et al.,
2011) and the United States (Al Bitar et al., 2012; Jackson et al., 2012). However, to our
knowledge, there is limited research focusing in evaluating the accuracy of the product
on a European setting, particularly so in the Mediterranean region where water can be
an important limiting consideration.
112
113
114
115
116
117
In purview of the above, the specific objectives of this study were to: (1)
explore the SMOS soil moisture product accuracy at selected Mediterranean sites
belonging to a global in-situ monitoring network; and (2) investigate the effect of
different parameters that may be influencing the accuracy of the soil moisture
estimates from the operational product, including seasonality and Radio Frequency
Interference (RFI).
118
2. Data and analysis methods
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
In-situ measurements were acquired from selected sites belonging to the
International Soil Moisture Network (ISMN) ground operational network (Dorigo et
al., 2011a). ISMN is an international cooperation to establish and maintain a global insitu soil moisture database. ISMN was developed with the purpose of establishing and
maintaining a database of harmonized global in-situ soil moisture, and promoting
scientific studies on calibration and validation of satellite-based and modelled soil
moisture products (Dorigo et al., 2011b; Petropoulos et al., 2013). Eighteen sites
representative of different biophysical conditions from the ISMN REMEDHUS network
in Spain were utilised in this study (Table 1). These stations are located in a central
semiarid region of the Duero basin that is nearly flat (slopes of less than 10%), with
elevation ranging from 700 to 900 m above sea level. It is a continental semi-arid
Mediterranean climate that has an average annual precipitation of 385 mm and a
mean temperature of 12 °C. The land use is mainly agricultural, with rain-fed cereals
grown in winter and spring, irrigated crops in summer, with the remaining area
inclusive of vineyards and areas of forest and pasture (Zhang et al., 2014). Data from
in-situ observations of soil moisture measured from the top 5 cm of the surface layer
within those sites were collected from selected days during 2011.
138
139
140
141
142
143
144
145
146
147
148
149
150
Satellite soil moisture was performed using the SMOS satellite. SMOS is a part of
ESA's Living Planet Programme. It’smain aim is to provide measurements of the
changes in the land surface wetness and ocean salinity by observing variations in the
natural microwave emission coming up off the surface of the planet. The satellite was
launched in November 2009 to a nearly circular orbit of 763 km with a 6 am ascending
and 6 pm descending equator crossing time. SMOS platform main instrument is the
Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which records
emitted energy from the Earth's surface in the microwave L-band (1.4 GHz). It is
designed to provide near-surface soil moisture estimates with global coverage, a
revisit time of three days at the equator, and nearly daily at the poles, and spatial
resolution of around 40 Km. The target accuracy of the SMOS product is 4% (0.04 m3
m-3), which should be achievable over relatively uniform areas (Panciera et al., 2011).
A total of 45 SMOS SMC (Soil Moisture Content) product version (v05) image granules
4
151
152
were acquired from the Eoli-SA portal for the Spanish sites at different overpass dates
during 2011.
153
154
155
156
157
158
159
160
161
162
163
164
165
ISMN REMEDHUS in-situ measurements (Martınez-Fernandez and Ceballos,
2005) were then extracted from each of the selected test sites for dates and times
concurrent to the SMOS satellite overpass times, to allow for the direct comparison of
both datasets. The quality of the in-situ data was first evaluated through information
acquired from the in-situ data providers where only quality-passed data such as those
free from drying out, ponding effect, and instrumentation error were selected and
used in further analysis. The pre-processed in-situ soil moisture values that
corresponded to the date/time of the satellite overpass were extracted (Excel
MacroVBA), and assigned to point shapefiles of the test sites (tabular join in ArcMap
10.1). The shapefiles were imported on top of pre-processed SMOS image pixels in the
BEAM VISAT and SMOS toolbox. Using the BEAM correlation tool, the in-situ soil
moisture was matched against the SMOS soil moisture unit of the pixel containing the
site point.
166
167
3. Performance assessment
168
169
170
171
172
173
174
175
To quantify the level of agreement between the SMOS-predicted soil moisture
and the in-situ measurements from the ground observational network, a series of
statistical measures were computed, using the statistical terms suggested by Wilmott
(1982), including the standard deviation () of the observed and modelled values, the
root mean square error (RMSE), the bias, the scatter and the mean absolute error
(MAE). Table 2 summarises the formulas that express the statistical terms used, a
detailed description of which can be found for example in Silk (1979), Burt and Barber
(1996) and Wilmott (1982).
176
177
178
179
180
181
182
183
184
185
Agreement between the reference and the SMOS-predicted soil moisture was
initially examined for all days of comparison. Subsequently, additional comparisons
were performed to assess the effect of RFI on overall product accuracy, where the
methods were repeated for the common days between the in-situ data and the SMOS
satellite data that exhibited low RFI only (<0.2). In addition, agreement between the
soil moisture predicted by SMOS and the in-situ measurements was examined by
season, and the effect of RFI on seasonal results was also examined similar to previous
studies (Albergel et al., 2012; Rötzer et al., 2012, Srivastava et al., 2013b, Sanchez-Ruiz
et al., 2014). RFI fraction in particular was the parameter which was computed from
the N_RFIX and N_RFI_Y as well as the M_AVAO bands following the equation:
186
187
188
189
190
191
192
193
[ N _ RFI _ X  N _ RFI _ Y ]
M _ AVAO
(1)
Where N_RFI_X is the RFI detected in L2 test X polarization (the count of deleted
views), N_RFI_Y is the RFI detected in L2 test Y polarization (again, the count of deleted
views), and M_AVAO is the total number of views available (SMOS ATBD, 2010).
Radiometer signals received in the L- band are particularly susceptible to man-made
RFI. Unwanted emissions from active services operating in neighbouring bands and
unauthorised emissions within the protected passive band cause unnatural and
focalised emission, well above the grounds expected radiation,. Areas affected by RFIs
5
194
195
196
197
198
199
might thus lead to data loss or to underestimation of soil moisture and salinity
retrievals by the SMOS product (Oliva et al., 2012). SMOS images for which it was not
possible to compute RFI fraction, or RFI Fraction was higher than 0.2, were omitted
from further analysis. Analysis was conducted for all days together but also further
analysis was conducted by season given that SMOS images had been acquired at
different times throughout the year.
200
201
202
203
204
205
206
207
208
209
210
211
212
213
Statistical scores for the agreement between the SMOS and in-situ reference
soil moisture measurements from the ISMN REMEDHUS network for all days were
determined and the main results are presented in Table 3 and Figure 1. Generally, as
indicated from the statistical metrics computed for the case of comparisons for all
days, a relatively good agreement between the two compared datasets was reported
(RMSE = 0.116 m3 m-3, bias = -0.068 m3 m-3, scatter = 0.094 m3 m-3). A further
analysis was conducted using only the days for which SMOS images were of low RFI
fraction (i.e. value of <0.2). Evidently, a further improvement in the agreement
between the two compared datasets was observed after filtering out the days of high
RFI fraction (RMSE = 0.110 m3 m-3, bias = -0.064 m3 m-3, scatter = 0.090 m3 m-3).
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
A further analysis was conducted examining the agreement between the
compared datasets by season (Table 3 and Figure 2). The lowest RMSE was found
for the comparisons within the autumn period (0.100 m 3 m-3), which also included
the lowest scatter results (0.074 m 3 m-3). However, the higher accuracy of these
herein preliminary results could be associated with the smaller sample size in
comparison to that of the other seasons (Table 3). Thus, a further analysis using a
larger sample for the different seasons could be a step for further investigation.
Agreement was lowest during the spring period, which displayed the highest RMSE,
closely followed by winter (spring/winter = 0.133 m3 m-3/0.119 m3 m-3). Similar to
the results of the all days comparison a systematic soil moisture underestimation
was found for the four seasons (Table 3), especially during the spring months (bias =
-0.088 m3 m-3). All the four seasons exhibited a better product performance when
RFI was filtered out. However, days that exhibited high RFI during the summer
period were unavailable for this study; thus product accuracy results were
comparable for both filtered and unfiltered days. Spring and winter displayed a
decrease in RMSE, with autumn showing the greatest improvement in product
accuracy (RMSE decreased from 0.100 to 0.098m3 m-3). This can be in part
attributed to the possibility of lesser fractional vegetation cover than other seasons.
Scatter and bias also showed an improvement during these periods. Some decrease
in performance could be due to a number of factors such as the retrieved SMOS soil
moisture data is observed at a depth of 0 cm to 5 cm whereas in situ sensors observe
at 5 cm. Thus the corresponding faster and stronger response to wetting and drying
periods at shallower depth could be a plausible explanation for discrepancies in
agreement. Spatial scaling can also constitute a significant issue, where a too small
amount or not representative set of in-situ measurements can easily provoke a bias
when aggregated at larger scales (Lacave et al., 2012; Bircher et al., 2013).
240
With respect to the SMOS soil retrieval target accuracy of 0.04 m 3 m-3, these
3. Results
Downloaded by [Aberystwyth University] at 08:26 20 March 2014
6
241
242
243
initial results comparable with other studies of similar scale test sites in Europe
using in-situ measurements other than those from CarboEurope (Lacava et al., 2012;
Parrens et al., 2012; Schlenz et al., 2012; Bircher et al., 2013).
244
245
246
247
248
249
250
251
252
253
254
255
256
The noticeable increase in the agreement between the SMOS-predicted soil
moisture and the corresponding in-situ measurements after excluding the high RFI
value days is also in agreement to previous analogous studies examining this effect
in similar studies conducted in different environments (Albergel et al., 2011;
Montzka et al., 2011; Zribi et al., 2011). The RFI can be defined as the disturbance
that affects an electromagnetic radiation emitted from an external source (Murray,
2013). This is a major problem in SMOS soil moisture retrieval, which decreases the
efficiency of retrieved soil moisture (Jackson et al., 1999). These disturbances
generally degrade or limit the quality of data and sometime lead to a total loss of
data. Hence, localizing and eliminating point sources of signal contamination,
specifically in the L-band; thus at present on-going challenges in Europe and several
other parts in the world (Kerr et al., 2012; Olivia, et al., 2012; Daganzo-Eusebio et al.,
2013).
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
Results from this study, although perhaps limited by the relatively small and
arbitrarily chosen sample size, suggest that agreement of the soil moisture estimates
are driven to a certain extent by the seasonal cycle. The latter can be potentially
ascribed to the phenological changes of vegetation throughout the year. Spring and
summer theoretically have denser plant canopies compared to autumn and thus
higher signal attenuation hampers satellite sensing, resulting in poorer soil moisture
retrieval. Furthermore, dew has a significant effect on passive microwave
observations by increasing horizontal brightness, and is most prominent during
summer, spring and autumn respectively (de Jeu et al., 2005; Du et al., 2012). In
agreement with other validation studies of SMOS product conducted independently
(Albergel et al., 2012; Sanchez-Ruiz et al., 2014), product accuracy performed poorly
during winter. This should be noted, because, as long as there is no snow or soil frost
during autumn/winter season, the retrieval of soil moisture should work better than
in other seasons due to less sources of attenuation associated with the inactivity of
vegetation during this period (Rötzer et al., 2012; Srivastava et al., 2013c). The effect
of snow and frozen soil could cause a decline in product accuracy and these factors
should be suggested as possible flag information in future applications (Rötzer et al.,
2012). In future, the integration of numerical weather models, meteorological
variables, local hydrological models and information on land cover could also be
utilised to more accurately analyse the effect of seasonality on soil moisture
estimation as already demonstrated in other studies (Srivastava et al., 2013b).
278
279
280
281
282
283
284
285
286
287
288
4. Conclusions
SMOS is the latest major satellite remote sensing mission utilised for soil
moisture estimation from space. However, as with any operational product, before
using it for various applications its accuracy should be assessed and validated for
different application areas, so that data providers and users can understand the
uncertainties associated with the data. SMOS data may have different performance
results dependent on terrain type, seasonality and geographical locations. This study
explores the performance of SMOS (v05) data in comparison to in-situ observations
from the ISMN REMEDHUS network to understand and validate its accuracy in a
7
289
typical Mediterranean setting in 2011.
290
291
292
It has been found that the direct comparison of SMOS operational product with
in-situ observations indicated a moderate performance of the product within these
sites. The main findings of this study can be summarised as follows:
293
294
295
296
297
(1) the overall comparisons showed a generally reasonable agreement
between the SMOS product and the in-situ measurements of SMC in the 0-5 cm soil
moisture layer. The results were largely comparable to previous analogous validation
studies of the product, suggesting SMOS soil moisture products potential value at
meso-scale studies (Table 3 and Figure 1).
298
299
300
301
(2) a seasonal cycle is observed in regards to the agreement between the in-situ
observed and product soil moisture estimations, where the autumn period exhibited
lowest error. Winter and spring displayed highest error possibly ascribed to snow/soil
frost and high vegetation attenuation respectively (Table 3 and Figure 2).
302
303
304
(3) as expected, in agreement with other studies, after filtering out the RFI
contaminated pixels results were much improved as indicated by the superior product
accuracy for days of RFI below 0.2 (Table 3).
305
306
307
308
309
310
Hence, eliminating sources of RFI pollution is an on-going challenge in Europe.
The result of this study emphasises how essential it is to validate the magnitude and
spatial structure of the uncertainties of any new satellite–based EO product before its
use in operational applications. Work is ongoing in expanding the SMOS SMC
operational product validation at a wide range of settings, including inter-comparisons
to other similar products.
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
Acknowledgements
Dr. Petropoulos gratefully acknowledges the European Space Agency (ESA) for providing the
financial means that assisted to this study materialization. Authors wish to thank ISMN and
the site managers for the provision of the in-situ data used in this study. The views expressed
here are those of the authors solely and do not constitute a statement of policy, decision, or
position on behalf of NASA or the authors’ affiliated institutions.
Funding
This work has been funded by the European Space Agency (ESA) Support to Science Element
(STSE) PROgRESSIon project (under contract STSE-TEBM-EOPG-TN-08-0005).
References
Al Bitar, A., D. L., Y. H. Kerr, O. Merlin, P. Richaume, A. Sahoo, and E. F. Wood. 2012. "Evaluation of
SMOS soil moisture products over continental US using the SCAN/SNOTEL network."
Geoscience and Remote Sensing, IEEE Transactions on ,50(5): 1572-1586.
Albergel, C., E. Zakharova, J. C. Calvet, M. Zribi, M. Pardé, J. P. Wigneron, N. Novello, Y. Kerr, A.,
Mialon, and N. E. D. Fritz. 2011. “A first assessment of the SMOS data in southwestern France
using in situ and airborne soil moisture estimates: The CAROLS airborne campaign.” Remote
sensing of environment, 115(10): 2718-2728.
Albergel, C., P. de Rosnay, C. Gruhier, J. Muñoz-Sabater, S. Hasenauer, L. Isaksen, Y. Kerr, and W.
Wagner. 2012. “Evaluation of remotely sensed and modelled soil moisture products using
global ground-based in situ observations.” Remote Sensing of Environment, 118: 215-226.
Al-Shrafany, D., M. Rico-Ramirez, and D. Han. 2012a. “Calibration of Roughness Parameters using
8
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
Rainfall Runoff Water Balance for Satellite Soil Moisture Retrieval.” Journal of Hydrologic
Engineering, 17: 704-714.
Al-Shrafany, D., M. Rico-Ramirez, D. Han, and M. Bray. 2012b. “Comparative Assessment of Soil
Moisture Estimation from Land Surface Model and Satellite Remote Sensing based on
Catchment Water Balance.” Meteorological Applications, in press.
Antón, M., D. Bortoli, M. J. Costa, P. S. Kulkarni, A. F. Domingues, D. Barriopedro, A. Serrano,
and A. M. Silva. 2011. “Temporal and Spatial Variabilities of Total Ozone Column over
Portugal.” Remote Sensing of Environment, 115: 855–863.
Barrett, B., and G. P. Petropoulos. 2013. “Satellite Remote Sensing of Surface Soil Moisture.”
Chapter 4, pages 85-120, in “Remote Sensing of Energy Fluxes and Soil Moisture Content ", by
G.P. Petropoulos, Taylor Francis, ISBN: 978-1-4665-0578-0.
Battrick, B. 2006. “The Changing Earth. New Scientific Challenges for ESA’s Living Planet
Programme.” ESA SP-1304. ESA Publications Division, ESTEC, The Netherlands, 2006.
Bircher, S., N. Skou, and Y. H. Kerr. 2013. “Validation of SMOS L1C and L2 products and important
parameters of the retrieval algorithm in the Skjern River catchment, Western Denmark.”
Geoscience and Remote Sensing, IEEE Transactions on, 51(5): 2969-2985.
Burt, J. E., and G. M. Barber. 1996. “Elementary statistics for geographers.” Longman Ed., 504.
Camici, S., A. Tarpanelli, L. Brocca, F. Melone, and T. Moramarco. 2011. “Design soil moisture
estimation by comparing continuous and storm-based rainfall-runoff modeling.” Water
Resources Research, 47 (5): W05527.
Carlson, T. 2007. “An overview of the" triangle method" for estimating surface evapotranspiration
and soil moisture from satellite imagery.” Sensors, 7 (8): 1612-1629.
Daganzo-Eusebio, E., R. Oliva, Y. H. Kerr, S. Nieto, P. Richaume, and S. M. Mecklenburg. 2013.
“SMOS Radiometer in the 1400--1427-MHz Passive Band: Impact of the RFI Environment and
Approach to Its Mitigation and Cancellation.” Geoscience and Remote Sensing, IEEE
Transactions on, 51 (10).
De Jeu, R., B. Heusinkveld, S. Groot, P. de Rosnay, T. Holmes, and M. Owe. 2005. “The effect of dew
on passive L–band microwave observations.” In Geophysical Research Abstracts, 7.
Deutsch, E., E. Bork, and W. Willms. 2010. “Soil moisture and plant growth responses to litter and
defoliation impacts in Parkland grasslands.” Agriculture, Ecosystems & Environment, 135 (12): 1-9.
Dorigo, W. A., W. Wagner, R. Hohensinn, S. Hahn, C. Paulik, M. Drusch, S. Mecklenburg et al.
2011a.“The International Soil Moisture Network: A data hosting facility for global in situ soil
moisture measurements.” Hydrol. Earth Syst. Sci., 15, 1675–1698, doi:10.5194/hess-15-16752011.
Dorigo, W., P. Van Oevelen, W. Wagner, M. Drusch, S. Mecklenburg, A. Robock, and T. Jackson.
2011b. “A new international network for in situ soil moisture data.” Eos Trans., AGU, 92, 141–
142.
Draper, C., R. Reichle, R. de Jeu, V. Naeimi, R. Parinussa, and W. Wagner. 2013. “Estimating root
mean square errors in remotely sensed soil moisture over continental scale domains.”
Remote Sensing of Environment, 137: 288-298.
Du, J., T. J., Jackson, R. Bindlish, M. H. Cosh, L. Li, B. K. Hornbuckle,, and E. D. Kabela. 2012. “Effect of
dew on aircraft-based passive microwave observations over an agricultural domain.” Journal
of Applied Remote Sensing, 6(1).
Escorihuela, M. J., A. Escorihuela, P. Richaume, and Y. H. 2012. “SMOS calibration and validation
over the Salar de Uyuni.” In Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE
International (pp. 1124-1126). IEEE
Jackson, T. J. 1993. “Measuring surface soil moisture using passive microwave remote sensing.”
Hydrological processes, 7 (2): 139-152.
Jackson, T. J., D. M. Le Vine, A. Y. Hsu, A. Oldak, P. J. Starks, C. T. Swift, J. D. Isham, and M. Haken.
1999. “Soil moisture mapping at regional scales using microwave radiometry: The Southern
Great Plains Hydrology Experiment.” Geoscience and Remote Sensing, IEEE Transactions on,
37 (5): 2136-2151.
Jackson, T. J., R. Bindlish, M. H. Cosh, T. Zhao, P. J. Starks, D. D. Bosch, M. Seyfried et al. 2012.
"Validation of Soil Moisture and Ocean Salinity (SMOS) soil moisture over watershed
9
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
networks in the US." Geoscience and Remote Sensing, IEEE Transactions on, 50 (5): 15301543.
Kerr, Y. H., P. Waldteufel, P. Richaume, J. P. Wigneron, P. Ferrazzoli, A. Mahmoodi, A. Al Bitar, et al.
2012. “The SMOS soil moisture retrieval algorithm.” Geoscience and Remote Sensing, IEEE
Transactions on, 50(5): 1384-1403.
Lacava, T., P. Matgen, L. Brocca, M. Bittelli, N. Pergola, T. Moramarco, and V. Tramutoli. 2012. “A
First Assessment of the SMOS Soil Moisture Product With In Situ and Modeled Data in Italy
and Luxembourg.” IEEE Transactions on Geoscience and Remote Sensing, 50 (5): 1612.
Langematz. 2011. “The Brewer–Dobson Circulation and Total Ozone from Seasonal
to Decadal Time Scales.” Atmospheric Chemistry and Physics 11: 11221–11235.
Mallick, K., B. K. Bhattacharya, and N. Patel. 2009. “Estimating volumetric surface moisture
content for cropped soils using a soil wetness index based on surface temperature and NDVI.”
Agricultural and Forest Meteorology, 149 (8): 1327-1342.
Mart´ınez-Fernandez, J. and A. Ceballos. 2005. “Mean soil moisture estimation using temporal
stability analysis.” J. Hydrol., 312: 28–38.
Milzow, C., P. Krogh, and P. Bauer-Gottwein. 2011. “Combining satellite radar altimetry, SAR
surface soil moisture and GRACE total storage changes for hydrological model calibration in a
large poorly gauged catchment.” Hydrol Earth Syst Sci, 15: 1729-1743.
Montzka, C., H. Bogena, L. Weihermüller, F. Jonard, M. Dimitrov, C. Bouzinac, J. Kainulainen, et al.
2011. “Radio brightness validation on different spatial scales during the SMOS validation
campaign 2010 in the Rur catchment, Germany.” In Geoscience and Remote Sensing
Symposium (IGARSS), 2011 IEEE International (pp. 3760-3763). IEEE.
Murray, D. 2013. “Using RF recording techniques to resolve interference problems.” In:
AUTOTESTCON, 2013 IEEE, 2013. IEEE, 1-6.
Nandintsetseg, B., and M. Shinoda. 2011. “Seasonal change of soil moisture in Mongolia: its
climatology and modelling.” International Journal of Climatology, 31 (8): 1143-1152.
Oliva, R., E. Daganzo, Y. H. Kerr, S. Mecklenburg, S. Nieto, P. Richaume, and C. Gruhier. 2012.
“SMOS radio frequency interference scenario: Status and actions taken to improve the RFI
environment in the 1400–1427-MHz passive band.” Geoscience and Remote Sensing, IEEE
Transactions on, 50(5): 1427-1439.
Owe, M., R. de Jeu, and J. Walker. 2001 “A methodology for surface soil moisture and
vegetation optical depth retrieval using the microwave polarization difference index.”
Geoscience and Remote Sensing, IEEE Transactions on, 39 (8): 1643-1654.
Panciera, R., J. P. Walker, J. Kalma and E. Kim. 2011. “A proposed extension to the soil moisture
and ocean salinity level 2 algorithm for mixed forest and moderate vegetation pixels.”
Remote Sensing of Environment, 115(12): 3343-3354.
Parrens, M., E. Zakharova, S. Lafont, J. C. Calvet, Y. Kerr, W. Wagner, and J. P. Wigneron. 2012.
“Comparing soil moisture retrievals from SMOS and ASCAT over France.” Hydrology &
Earth System Sciences, 16(2).
Peischl, S., J. P. Walker, N. Ye, D. Ryu, and Y. Kerr. 2014. “Sensitivity of multi-parameter soil
moisture retrievals to incidence angle configuration.” Remote Sensing of Environment,
143: 64-72.
Petropoulos G. P., H. Griffiths, W. Dorigo, A. Xaver., and A. Gruber. 2013. “Surface Soil Moisture
Estimation: Significance, Controls and Conventional Measurement Techniques” Chapter 2,
pages 29-48, in “Remote Sensing of Energy Fluxes and Soil Moisture Content ", by G.P.
Petropoulos, Taylor and Francis, ISBN: 978-1-4665-0578-0.
Petropoulos, G. P., H. M. Griffiths, T. N. Carlson, P. Ioannou-Katidis, and T. Holt. 2014.
“SimSphere Model Sensitivity Analysis Towards Establishing its Use for Deriving Key
Parameters Characterising Land Surface Interactions.” Geoscientific Model Development,
doi:10.5194/gmdd-6-1-2013, [in press].
Pinori, S., R. Crapolicchio, and S. Mecklenburg. 2008. “Preparing the ESA-SMOS (soil moisture
and ocean salinity) mission-overview of the user data products and data distribution
strategy.” In: Microwave Radiometry and Remote Sensing of the Environment, 2008.
MICRORAD 2008, 2008. IEEE, 1-4.
Rotzer, K., C. Montzka, H. Bogena, W. Wagner, R. Kidd, and H. Vereecken. 2012. “Time series
10
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
analysis of SMOS and ASCAT: Soil moisture product validation in the Rur and Erft
catchments.” In Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE
International (pp. 3716-3719). IEEE.
Sánchez-Ruiz, S., M. Piles, N. Sánchez, J. Martínez-Fernández, M. Vall-llossera, and A. Camps.
2014. “Combining SMOS with visible and near/shortwave/thermal infrared satellite data
for high resolution soil moisture estimates.” Journal of Hydrology.
Sandholt, I., K. Rasmussen, and J. Andersen. 2002. “A simple interpretation of the surface
temperature/vegetation index space for assessment of surface moisture status.” Remote
Sensing of Environment, 79 (2): 213-224.
Schlenz, F., J. T. Dall'Amico,, A. Loew, and W. Mauser. 2012. "Uncertainty assessment of the
SMOS validation in the upper Danube catchment." Geosciences and Remote Sensing, IEEE
Transactions on, 50(5): 1517-1529.
Scott, C. A., and W. G. M. Bastiaanssen. 2003. “Mapping root zone soil moisture using remotely
sensed optical imagery.” Journal of irrigation and drainage engineering, 129: 326.
Silk, J. 1979. “Statistical concepts in geography.” 275.
SMOS Algorithm Theroetical Basis Document (ATBD) for the SMOS Level 2 soil Moisture
Processor Development Continuation Project. Issue 3.3, June 16 th, 2010. Available from:
http://earth.esa.int/pub/ESA_DOC/SO-TN-ARR-L2PP-0037_ATBD_v3_3.pdf [Accessed on
May 21st, 2014).
Srivastava, P. K., D. Han, M. Rico-Ramirez, and T. Islam. 2013b. “Machine Learning Techniques
for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for
Hydrological Application.” Water Resources Management, 27 (8): 3127-3144.
Srivastava, P. K., D. Han, M. Rico-Ramirez, and T. Islam. 2013c. “Appraisal of SMOS soil
moisture at a catchment scale in a temperate maritime climate." Journal of Hydrology,
498: 292-304.
Srivastava, P. K., D. Han, M. Rico-Ramirez, D. Al-Shrafany, and T. Islam. 2013a. “Data Fusion
Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land
Surface Model.” Water Resources Management, 27 (15): 5069-5087.
Su, C-H,, D. Ryu, R. I. Young, A. W. Western, and W. Wagner. 2013. "Inter-comparison of
microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast
Australia." Remote Sensing of Environment 134: 1-11.
Wagner, W., V. Naeimi, K. Scipal, R. de Jeu, and J. Martínez-Fernández. 2007. “Soil moisture
from operational meteorological satellites.” Hydrogeology Journal, 15 (1): 121-131.
Wanders, N., D. Karssenberg, M. Bierkens, R. Parinussa, R. de Jeu, J. van Dam, and S. de Jong.
2012. “Observation uncertainty of satellite soil moisture products determined with
physically-based modeling.” Remote Sensing of Environment, 127: 341-356.
Weber, M., S. Dikty, J. P. Burrows, H. Garny, M. Dameris, A. Kubin, J. Abalichin, and U.
Wilmott, C. J. 1992. “Some comments on the evaluation of model performance.” Bullettin
American Meteorological Society 63 (11): 1309–1313.
Zhang, D., R. Tang, W. Zhao, B. Tang, H. Wu, K. Shao, and Z. L. Li. 2014. “Surface Soil Water
Content Estimation from Thermal Remote Sensing based on the Temporal Variation of
Land Surface Temperature.” Remote Sensing, 6(4): 3170-3187.
Zribi, M., M. Parde, J. Boutin, P. Fanise, D. Hauser, M. Dechambre, Y. Kerr, et al. 2011. “CAROLS:
a new airborne L-band radiometer for ocean surface and land observations.” Sensors, 11:
719-742.