hydrology
Article
Comparison and Downscale of AMSR2 Soil Moisture
Products with In Situ Measurements from the
SCAN–NRCS Network over Puerto Rico
Jonathan Nuñez-Olivieri 1,2 , Jonathan Muñoz-Barreto 1, *, Rebecca Tirado-Corbalá 3 ,
Tarendra Lakhankar 2 and Andmorgan Fisher 4
1
2
3
4
*
Department of Civil Engineering and Surveying, University of Puerto Rico, Mayagüez, PR 00682, USA;
jnunezo000@citymail.cuny.edu
NOAA-CREST Center, The City College of New York, New York, NY 10031, USA; tlakhankar@ccny.cuny.edu
Agro-Environmental Science Department, University of Puerto Rico, Mayagüez, PR 00682, USA;
rebecca.tirado@upr.edu
Geospatial Research Laboratory, U.S. Army Engineer Research and Development Center,
7701 Telegraph Road, Alexandria, VA 22315, USA; andmorgan.r.fisher@usace.army.mil
Correspondence: jonathan.munoz@upr.edu; Tel.: +1-787-832-4040 (ext. 3499)
Received: 11 September 2017; Accepted: 28 September 2017; Published: 30 September 2017
Abstract: A continuous spatio-temporal database of accurate soil moisture (SM) measurements is
an important asset for agricultural activities, hydrologic studies, and environmental monitoring.
The Advanced Microwave Scanning Radiometer 2 (AMSR2), which was launched in May 2012, has
been providing SM data globally with a revisit period of two days. It is imperative to assess the
quality of this data before performing any application. Since resources of accurate SM measurements
are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing
it with ground-based measurements, as well as perform a downscaling technique to provide a better
description of how the sensor perceives the surface soil moisture as it passes over the island. The
comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation
coefficient. Two downscaling techniques were used, and their performances were studied. The results
revealed that AMSR2 products tend to underestimate soil moisture. This is due to the extreme
heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the
island. This research provides a comprehensive study on the accuracy and potential of the AMSR2
products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.
Keywords: soil moisture; AMSR2; remote sensing; downscale; SCAN-NRCS; passive microwave
1. Introduction
Soil moisture (SM) content is the quantity of water accumulated in soil pores, usually recorded
as percent or volumetric ratio (e.g., cm3 /cm3 ) for different depths (e.g., 5 cm, 10 cm, and 25 cm). SM
plays an important role in water cycles, hydrologic studies, agricultural activities, and environmental
monitoring [1–6]. Hydrologic modeling systems are very sensitive to changes in SM values for
applications involving flood control and drought assessment [1]. In agricultural activities, best
management practices and irrigation control can be optimized with continuous spatio-temporal SM
measurements [7]. Predictions and results for environmental monitoring applications such as climate
change and weather forecasting have a high dependency on the accuracy of the SM data [5,6,8].
The spatio-temporal availability of accurate SM measurements rely on the quality of the instruments,
frequency of retrieval, and management of the data [2]. There are three options for acquiring SM
content: ground-based measurements, modeling predictions, and remotely-sensed estimates [1,3].
Hydrology 2017, 4, 46; doi:10.3390/hydrology4040046
www.mdpi.com/journal/hydrology
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The SM measurements collected in the field, which are usually referred to as in situ measurements,
are commonly retrieved in high or low density networks of point measurements. The density of a
network is determined by the quantity and assembling of instruments, the area of study, and the
budget of the project. There are various instruments and techniques used to obtain ground-based
measurements. Time domain reflectometry (TDR) instruments measure the soil dielectric permittivity,
which is commonly used to quantify SM, since its value increases with the higher presence of water [3,9].
The TDR instruments provide accurate SM measurements for a specific location that can be represented
as a point measure in a map. Another type of instrument used to collect in situ SM measurements is
based on cosmic-ray detection. The COsmic-ray Soil Moisture Observing System (COSMOS) measures
the quantity of fast neutrons above the land surface, which have an inversely correlated relationship
with SM due to kinetic energy loss, and provides an estimate of SM over a horizontal footprint with a
diameter of about 600 m [9]. This kinetic loss happens when fast neutrons collide with hydrogen atoms
found in wet land surfaces [9]. While in situ measuring provides accurate products, the instruments
can be difficult to calibrate and maintain without high technical knowledge, and the availability of an
adequate network that meets the requirements for each individual project is very limited [1].
Soil moisture can be simulated at different spatio-temporal resolutions using modeling systems [3].
The quality of the simulation is tied to the accuracy of the in situ data used as the training dataset,
and the right implementation of a validation and optimization process [3,10]. There are a variety
of modeling systems used to simulated soil moisture, such as the SM Accounting, the SM Water
Balance Model, and the variable infiltration capacity (VIC) model [3,11–13]. As an example of
how these models work, the VIC model simulates SM based in correlations between land cover,
SM storage capacity, topography, and precipitation [12,14]. A simulation can be performed for
various spatio-temporal resolutions, depending on the objective of the project [3]. However, the poor
availability of ground-based SM data networks, the uncertainty of forcing data, and the model
structure will affect directly the process of simulation, validation, and optimization of the modeled SM
products [13,15].
Satellite-based microwave observations can retrieve SM estimates at different spatio-temporal
resolutions [1,3]. Passive microwave depends on the physical temperature and surface emissivity of
the earth’s surface. In principle, passive sensors, such as radiometers, measure the thermal emission of
the surface at the microwave wavelength, and translate that energy to brightness temperature [15,16].
The response of the soil to an electromagnetic wave depends on its texture, surface roughness, organic
matter content, iron oxide content, and moisture content [17]. In general, radiative transfer models such
as the tau-omega (τ-ω), use the dielectric constant alongside other characteristics of the soil, such as an
incident angle and brightness temperature, to estimate SM with remote sensing technologies [17–20].
Satellite-based SM products are provided at a global scale by satellite sensors such as the Soil Moisture
Active Passive (SMAP) Mission from NASA (https://smap.jpl.nasa.gov/), the Soil Moisture and Ocean
Salinity (SMOS) from the European Space Agency (http://www.esa.int/Our_Activities/Observing_
the_Earth/SMOS), the Advanced Microwave Scanning Radiometer for the Earth Observing System
(AMSR-E) (https://nsidc.org/data/amsre) from the Japan Aerospace Exploration Agency (JAXA), and
the AMSR-2 also from JAXA (http://suzaku.eorc.jaxa.jp/GCOM_W/). An advantage of the current
satellite-based remotely-sensed SM products is the availability of global scale measurements at a
continuous spatio-temporal resolution [1,7]. However, at the current state, the satellite-based products
are available at coarse spatial resolutions ranging from 3 km to 40 km, which are not useful for small
scale hydrologic modeling and agricultural applications. Also, due to the availability of ground-based
networks, the validation process for satellite-based products is very limited [1,7,21]. Recent studies
have been working on the improvement of the soil moisture estimations of satellite-based products
using different algorithms that relate soil moisture with variables such as vegetation, brightness
temperature, precipitation, and others [21–23].
Hydrology 2017, 4, 46
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1.1. Soil Moisture in Puerto Rico
The spatio-temporal availability and accuracy of SM measurements are key elements to acquiring
the best possible outcomes in applications involving hydrologic modeling, agricultural management,
and weather monitoring. Unfortunately, in Puerto Rico the resources available to retrieve accurate
SM measurements are very limited. Currently, the Soil Climate Analysis Network (SCAN) of
the Natural Resources Conservation Service (NRCS) has eight stations around the center and
western regions of the island retrieving in situ SM content. The data collection is available online
(www.wcc.nrcs.usda.gov), and provides over five years of hourly and daily SM data. Simulated
SM is available from the Geostationary Operational Environmental Satellite—Puerto Rico Water
and Energy Balance (GOES-PRWEB) model (https://pragwater.com/goes-puerto-rico-water-andenergy-balance-goes-web-algorithm/) at 1 km resolution, which uses a water and energy balance
approach to simulate different hydrologic parameters such as surface runoff, stream flow, and SM.
However, the GOES-PRWEB SM product has not been officially validated. Remotely sensed SM data
can be retrieved from the various missions such as SMAP, SMOS, or the Global Change Observation
Mission—Water 1 (GCOM-W1) satellite system. This research is based on data from the Advanced
Microwave Scanning Radiometer 2 (AMSR2), a sensor carried by the GCOM-W1 satellite system.
The AMSR2, which launched in May 2012, is a microwave radiometer that estimates SM worldwide
every two days at a 25 km resolution [6,23]. The SM product can be downloaded from the Earth
Observation Research Center (EORC) of the Japan Aerospace Exploration Agency (JAXA) website
(https://gcom-w1.jaxa.jp), and is available on a daily or monthly basis at 10 km and 25 km resolutions.
1.2. Scope and Objectives
Since resources of accurate SM measurements are very limited in Puerto Rico, the scope of
this research is to assess the quality of the AMSR2 data and perform a downscaling technique to
provide a better description of how the sensor perceives the surface SM as it passes over the island.
The comparison consists of the evaluation of the difference between the satellite-based and the
ground-based measurements by means of the mean error (ME), the root mean squared error (RMSE),
and the correlation coefficient (R). Two downscaling techniques are used, and their performances are
studied. The first technique used to downscale the AMSR2 SM products to a finer downscaled product
consists of a simple linear equation that relates the coarse resolution AMSR2 SM with three parameters
retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS). These three MODIS
parameters are the Normalized Difference of Vegetation Index (NDVI), the Land Surface Temperature
(LST), and the albedo. The second technique is an algorithm that relates weighted values of sand
fraction, elevation, and NDVI with the AMSR2 SM product. The performance of each method was
analyzed with the hopes of providing a better method for the retrieval of SM in Puerto Rico.
2. Datasets
2.1. AMSR2 Satellite-Based Soil Moisture Data
The GCOM-W1 satellite system was launched in 17 May 2012 to collect geophysical parameters
(i.e., precipitation, sea surface temperature, and soil moisture content) and observe changes in water
circulation [6,22]. The GCOM-W1 system carries the AMSR2 sensor, which retrieves the radiometric
waves emitted from Earth. The brightness temperature data collected by the AMSR2 sensor is used
to estimate SM at a coarse spatial resolution globally with an average temporal resolution of two
days [6,24]. The SM estimates are calculated using the Land Parameter Retrieval Model (LPRM),
which is based on a forward radiative transfer model that retrieves soil moisture and vegetation
optical depth. The SM product provided by the AMSR2 can be retrieved from the Earth Observation
Research Center (EORC) on the Japan Aerospace Exploration Agency (JAXA) website. The data can
be downloaded in Hierarchical Data Format 5 (HDF5) for day and night readings, scene (referring
to all of the measurements taken at half orbit between the North Pole and the South Pole with
Hydrology 2017, 4, 46
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respect to the observation point [25]), or global map (10 km and 25 km resolution), on a daily or
monthly basis. For this research, all of the available data from September 2012 to January 2017 of
the daily global AMSR2 25 km resolution SM estimated values for both ascending and descending
overpasses were retrieved and analyzed. To retrieve the SM values for Puerto Rico, the geographical
coordinates included in the original satellite data were processed using the Euclidean distance method.
The maximum, minimum, mean, and standard deviation for the ascending and descending overpasses
of the AMSR2 25 km resolution SM product over Puerto Rico are presented in Figure 1.
Figure 1. Basic statistics for ascending and descending overpasses Advanced Microwave Scanning
Radiometer 2 (AMSR2) 25 km soil moisture (SM) (cm3 /cm3 ) products from September 2012 to January
2017: (a) maximum; (b) minimum; (c) mean; and (d) standard deviation.
2.2. SCAN–NRCS Ground-Based Soil Moisture Data
The Soil Climate Analysis Network (SCAN) project of the Natural Resources Conservation Service
(NRCS) has been collecting soil moisture and soil temperature all over the United States since 1991.
At present, the project has employed over 200 stations around the United States, mainly in agricultural
areas. The stations are continuously providing ground-based measurements of SM, precipitation,
relative humidity, land surface temperature, solar radiation, wind velocity, wind direction, and
barometric pressure. Eight SCAN–NRCS stations (Figure 2) that are located over the central and
western regions of Puerto Rico have been providing data from 2001 to the present day. The SM data
provided by the SCAN network is measured hourly with TDR instruments at four different depths
(5, 10, 20, and 50 cm). The data is offered daily, or hourly. All of the available surface layer (at a depth
of 5 cm) SM data for each station was retrieved for the analysis from the NRCS-NWCC (National Water
Climate Center) website in comma-separated values (.csv) format.
Hydrology 2017, 4, 46
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Figure
2. Location
Soil
ClimateAnalysis
Analysis Network
Network (SCAN)–Natural
Resources
Co Conservation
Figure
2. Location
of of
allall
Soil
Climate
(SCAN)–Natural
Resources
Service (NRCS) stations over Puerto Rico.
2.3. MODIS Remotely Sensed Parameters
The MODIS sensor is a spectroradiometer that collects data for 36 different spectral bands and
offers its products at a spatial resolution that varies from 500 to 5600 m. The products of MODIS are
leaf area index, fractional photosynthetically active radiation, bidirectional reflectance distribution
function, thermal anomalies and fire, temperature, emissivity, vegetation indices, gross and net
primary productivity, and albedo. Two units of MODIS are currently collecting data: one is aboard
the Terra satellite, and the other is in the Aqua satellite system. These satellite systems are part of
the NASA-centered international Earth Observing System (EOS). Both systems travel in a circular
sun-synchronous polar orbit, which is a setting that allows the systems to go from north to south of
Earth every 99 min (16 orbits per day), and collect data for the entire planet every one or two days.
MODIS products of Albedo, LST, and NDVI at a 1 km resolution were retrieved in HDF4 from the EOS
Data and Information System (EOSDIS) website (https://search.earthdata.nasa.gov/) for Puerto Rico
in order to perform the downscaling of a 25-km resolution SM from AMRS2 to a 1-km resolution SM
product. Albedo, LST, and NDVI parameters samples over Puerto Rico are presented in Figure 3.
(a)
(b)
(c)
Figure 3. Moderate Resolution Imaging Spectroradiometer (MODIS) product at 1 km resolution over
Figure 3. Moderate Resolution Imaging Spectroradiometer (MODIS) product at 1 km resolution over
Puerto Rico: (a) Albedo; (b) Land Surface Temperature (LST) (K), and (c) Normalized Difference of
Vegetation Index (NDVI).
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3. Methods
Puerto Rico consists of an archipelago with a land surface area of 9104 km2 . The island is mostly
mountainous, and the elevation at the highest point is 1338 m (4390 feet) above sea level. In Puerto
Rico, the soil type, land use, soil temperature, daily precipitation, impervious areas, and density
of vegetation varies noticeably by the kilometer. Therefore, it is expected that any average of a
25-km resolution SM estimate cannot be representative of the area of coverage. The methodology
of this project is meant to assess the quality of the AMSR2 products over Puerto Rico, and enhance
the satellite-based SM product by downscaling the coarse resolution AMSR2 SM product to a finer
resolution SM product.
3.1. Comparison of AMSR2 with SCAN–NRCS Soil Moisture Products
The comparison between the coarse resolution AMSR2 SM products and the SCAN–NRCS SM
data was performed for all of the data available during September 2012 to January 2017 for both the
ascending (1148 days of data) and the descending (1080 days of data) overpasses, which take place
around 1:30 p.m. and 1:30 a.m., respectively. Since the ground-based SM is offered hourly, average
values of the SCAN SM measurements between 1–2 p.m. and 1–2 a.m. were computed and compared
with the AMSR2 SM products. To assess the quality of the AMSR2 SM products in terms of each
SCAN–NRCS station, the location of each ground-based station was matched with the closest AMSR2
pixel centroid (Figure 4). To evaluate only the AMSR2 SM values that would better describe the
area where the respective SCAN-NRCS station is located, the SM data was filtered by selecting the
days when the soil moisture difference would not exceed ±0.15. The average distance between each
SCAN–NRCS station and the AMSR2 25 km pixel centroid, and the percent of pixels inside the ±0.15
difference range, were reported to analyze how these parameters affected the correlation between the
remotely sensed and in situ SM observations.
Figure 4. A sample for the matching of the SCAN–NRCS station with the closest AMSR2 SM pixel.
The AMSR2 SM values were analyzed by calculating the mean error (ME), the root mean square
error (RMSE), and the correlation coefficient (R) for each station. The ME (Equation (1)) is the
summation of the difference between the ground based and satellite based SM divided by the total of
days analyzed. The mean error is calculated as follows:
∑ N θ -θ
∑N
[θ − θi,S ]
∑
∑N
i = 1 ei
= i = 1 i,G
(1)
ME =
N
N
θ
θ
N
where the error is (ei ), the ground-based SM is θi,G , the satellite-based SM is θi,S , and N represents the
number of days analyzed. It is important to note that the number of days analyzed changes with each
station depending on the availability of the data. The RMSE is given by Equation (2):
Hydrology 2017, 4, 46
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RMSE =
s
2
∑N
i = 1 ei
=
N
s
2
∑N
i = 1 [θi,G − θi,S ]
N
(2)
The RMSE represents the absolute accuracy of the AMSR2 SM compared with the SCAN–NRCS
in situ SM. The R (Equation (3)) measures the relationship in variability between the observed
measurements and the estimated values [26], and is calculated by:
N
N
N ∑N
i = 1 (θi,G × θi,S ) − ∑i = 1 θi,G × ∑i = 1 θi,S
R = s
2
2
N
2 − ∑N
2 − ∑N
N ∑N
θ
θ
×
N
θ
θ
∑
i = 1 i,G
i = 1 i,G
i = 1 i,S
i = 1 i,S
(3)
The correlation coefficient calculated as shown in the previous equation ranges from 0 to 1, where
0 is a poor estimate, and 1 is the best estimate. A value of R ≥0.70 is considered as a satisfactory
estimation.
3.2. Downscaling the AMSR2 with Albedo, LST, and NDVI
Since the AMSR2 product does not provide representative data for SM measurements over
Puerto Rico, two downscaling techniques were tested to refine the 25 km resolution product to a
1 km resolution product. The following approaches were performed with hopes of enhancing the
satellite-based continuous spatio-temporal SM measurements over Puerto Rico. The downscaling
technique selected has been previously executed by Ray et al. [12] and first published by
Chauhan et al. [27]. The technique suggests that remotely sensed SM retrieved from the AMSRE
system at a 25 km resolution can be downscaled to 1 km resolution using a simple linear equation.
The equation is based on parameters calculated with a regression model, which is itself based on three
physical properties of 1 km resolution retrieved from MODIS. The three physical properties are the
albedo, the LST, and the NDVI. Equation (4) presents the downscaling approach with these three
MODIS parameters:
i = nj = nk = n
θs =
∑ ∑ ∑
aijk Vi Tj Ak .
(4)
i=0j=0k=0
By establishing the number of explanatory variables n equal to 1, the equation yields to:
θs = a000 + a100 A + a010 T + a001 V + a110 TA + a101 VA + a011 VT
(5)
where the aijk parameters are the connection between the fine resolution downscaled SM and the
coarse resolution satellite-based SM products, A is the albedo, T is the LST, and V is the NDVI. These
parameters are calculated with a multiple linear regression model that compares the aggregated values
of the physical properties with the coarse resolution SM estimate. Each parameter was upscaled to
match the 25 km resolution of AMSR2. The upscale was performed as follows:
V25 km =
∑ni = 1 ∑m
∑ni = 1 ∑m
∑ni = 1 ∑m
j = 1 Tij
j = 1 Aij
j = 1 Vij
, T25 km =
, A25 km =
mn
mn
mn
(6)
where 25 km is the resolution at which the physical parameters will be upscaled, m is the value for the
ith column of the 1 km resolution grid inside the 25 km resolution, and n is the value for the jth row of
the 1 km resolution grid inside the 25 km resolution.
3.3. Downscaling the AMSR2 with Sand Fraction, Elevation, and NDVI
As another approach for the downscaling, an algorithm was implemented to refine the AMSR2
product by distributing the coarse resolution SM product into a finer resolution product using weighted
control parameters. These control parameters are sand fraction (SAND), elevation (ELEV), and NDVI,
Hydrology 2017, 4, 46
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and were retrieved at a resolution of 1 km. The process to calculate a weighted value of each parameter
in location ij inside each AMSR2 pixel consists simply of dividing each ij value by its average, as shown
in the following equations:
SANDwij =
SANDij
SAND
, ELEVwij =
ELEVij
ELEV
NDVIij
, NDVIwij =
(7)
NDVI
These weighted values are then used to calculate the SM at each ij location by:
1
θij = θ ×
3
1
1
+
+ NDVIwij
SANDwij
ELEVwij
!
(8)
where θ is the 25 km AMSR2 SM, and θij is the downscaled AMSR2 at a 1 km resolution for location ij.
After performing both downscales, the results were compared with the SCAN–NRCS ground-based
SM, following the same evaluation methodology previously mentioned.
4. Results and Discussion
4.1. Comparison of AMSR2 with SCAN–NRCS Soil Moisture Products
All of the available data from September 2012 to January 2017 of AMSR2 SM products at
a resolution of 25 km for both ascending and descending overpasses were compared with each
SCAN–NRCS station over Puerto Rico. For the analysis, the maximum, minimum, mean, and standard
deviation were calculated for each station, and all of the AMSR2 pixels that were matched by distance,
as previously explained. The ascending and descending statistics results are shown in Tables 1
and 2, respectively.
Table 1. Basic statistics for each SCAN–NRCS station and the ascending AMSR2 SM products.
Ascending
Maximum
Minimum
Mean
Std. Deviation
Station
NRCS
AMSR2
NRCS
AMSR2
NRCS
AMSR2
NRCS
AMSR2
Adjuntas
Cabo Rojo
Corozal
Guánica
Isabela
Juana Díaz
Maricao
Mayagüez
0.615
0.357
0.571
0.357
0.499
0.512
0.415
0.585
0.600
0.599
0.542
0.600
0.587
0.599
0.599
0.587
0.123
0.108
0.120
0.007
0.120
0.116
0.081
0.454
0.009
0.009
0.008
0.009
0.009
0.010
0.009
0.009
0.382
0.173
0.365
0.071
0.313
0.216
0.199
0.552
0.169
0.137
0.119
0.181
0.122
0.161
0.137
0.134
0.142
0.051
0.122
0.060
0.083
0.071
0.080
0.024
0.137
0.138
0.078
0.140
0.103
0.136
0.138
0.111
Table 2. Basic statistics for each SCAN–NRCS station and the descending AMSR2 SM products.
Descending
Maximum
Minimum
Mean
Std. Deviation
Station
NRCS
AMSR2
NRCS
AMSR2
NRCS
AMSR2
NRCS
AMSR2
Adjuntas
Cabo Rojo
Corozal
Guánica
Isabela
Juana Díaz
Maricao
Mayagüez
0.633
0.348
0.564
0.322
0.478
0.512
0.398
0.584
0.577
0.598
0.581
0.576
0.584
0.586
0.598
0.577
0.119
0.109
0.126
0.003
0.116
0.110
0.082
0.454
0.021
0.010
0.016
0.022
0.012
0.016
0.010
0.025
0.379
0.168
0.372
0.069
0.313
0.213
0.202
0.551
0.164
0.140
0.135
0.169
0.154
0.163
0.144
0.157
0.139
0.051
0.119
0.058
0.085
0.075
0.081
0.024
0.109
0.097
0.091
0.110
0.103
0.111
0.105
0.104
The maximum values of both overpasses of the AMSR2 SM are very congruent to the NRCS
stations located in mountainous areas (Adjuntas, Corozal, and Isabela), where high humidity and
Hydrology 2017, 4, 46
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frequent precipitation events are common. The minimum values of both overpasses of the AMSR2 SM
underestimated the SM with respect to the NRCS readings for all of the stations, except for Guánica.
This exception takes effect because the NRCS station in Guánica is located in an area known as the
“Bosque Seco”, which directly translates to “Dry Forest”, and is known to be one of the most arid
regions with low humidity and a low frequency of precipitation events in Puerto Rico. The mean
values and standard deviations for AMSR2 readings with respect to the NRCS stations during both
overpasses are low and very similar, revealing a consistent tendency of all the AMSR2 readings over
PR to underestimate SM.
The percent of instances where the error was inside the range of ±0.15 (%), ME, RMSR, R, and the
mean distance from station to centroid (Distance) are shown in Tables 3 and 4. Overall, AMSR2 SM
products for both overpasses tended to underestimate for all of the SCAN–NRCS stations, except for
the station in Guánica, which is one of the most arid regions of the island.
Table 3. Basic statistics for each SCAN–NRCS station and the ascending AMSR2 SM products.
Station
(%)
Distance (km)
ME
RMSE
R
Adjuntas
Cabo Rojo
Corozal
Guánica
Isabela
Juana Díaz
Maricao
Mayagüez
32
74
27
67
30
68
71
4
9.0
24.9
9.6
18.8
12.6
10.6
11.9
18.3
0.074
0.079
0.075
0.063
0.085
0.077
0.081
0.089
0.081
0.087
0.086
0.075
0.095
0.087
0.090
0.103
0.823
0.273
0.777
0.470
0.776
0.490
0.590
0.559
Table 4. Basic statistics for each SCAN–NRCS station and the descending AMSR2 SM products.
Station
(%)
Distance (km)
ME
RMSE
R
Adjuntas
Cabo Rojo
Corozal
Guánica
Isabela
Juana Díaz
Maricao
Mayagüez
33
87
27
70
40
73
80
3
6.1
21.4
10.3
16.1
8.8
10.7
8.0
14.6
0.076
0.064
0.069
0.065
0.083
0.072
0.067
0.056
0.087
0.076
0.080
0.077
0.093
0.083
0.078
0.075
0.765
0.356
0.829
0.421
0.758
0.552
0.630
0.735
The percent of error inside the ±0.15 range (%) and the mean distance of the nearest available
AMSR2 pixel centroid with respect to each NRCS station (distance) readings both are influencing the
values of ME, RMSE, and R. Lower % values decreases ME and RMSE, while increasing R.
If the distance is over 12 km ME and RMSE increases, R decreases. High R values were found in
areas where the mean values of the NRCS SM readings in humid areas were equal to or higher than
0.30, the % was equal to or less than 40%, and the distance was less than 12 km. This high correlation
reveals that when the AMSR2 is not underestimating SM, it provides a moderately good estimate
within the range of the pixel, if the mean SM in the area is equal or higher than 0.30. Low R values
were found in arid areas, where the mean SM was lower than 0.30, the lower distance increased the R
up to 0.630.
4.2. Downscale of AMSR2 with Albedo, LST, and NDVI
The AMSR2 25 km resolution SM product was downscaled to 1 km resolution with a simple linear
equation involving MODIS products for Albedo, NDVI, and LST. The average resultant downscaling
equation for all AMSR2 data is shown in the following equation.
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θs = 0.1503 + 1.33 × 10−4 A + 4.70 × 10−5 T − 1.06 × 10−6 V − 4.28 × 10−7 TA + 7.31 × 10−10 VA − 2.31 × 10−9 VT
where θs is the downscaled SM, A is the albedo, T is the land surface temperature, V is the normalized
difference of vegetation index, and the rest are the interactions between A, T, and V (TA, VA, and
VT). The regression model achieved a moderate correlation of 0.6102 and an overall RMSE of 0.0050,
which indicated that the model was a good fit with the AMSR2 estimates. To reinforce this statement,
R-squared was calculated, and the F-statistic test was performed. The R-squared was 0.037, and the
p-value of the F-statistic test was less than 10−4−. These results reinforce the statement that the model is
providing a good fit. Table 5 shows that all of the p-values are below the significant level (5%), meaning
that the null hypothesis is rejected for each coefficient, and all of the terms in the linear equation
are significant.
Table 5. Calculated values and T-statistics of the regression coefficients.
Coefficient
a000
a100
a010
a001
a110
a101
a011
Value
0.15
1.33 × 10−4 −
4.70 × 10−5 −
−6 −
−1.06
− × 10
−
−4.28 × 10 7 −
−
7.31 × 10−10
−
−3.20 × 10−9
−
−
Squared Error
T-Statistic
P (%)
0.0053
3.66 × 10−5−
1.84 × 10−5−
6.67 × 10−7−
1.01 × 10−7−
1.89 × 10−9
−
2.31 × 10−9
49.12
2.98
1.97
−−1.66
−3.24
−
−0.12
−0.11
0.0014
0.1432
0.1276
0.1113
0.1354
0.2927
0.1160
−
Figure 5 shows a sample for the downscaled AMSR2 SM values over PR using MODIS parameters
Albedo, LST, and NDVI.
Figure 5. Sample of downscaled AMSR2 SM (cm3 /cm3 ) values over PR using MODIS parameters.
The following table (Table 6) presents the basic statistics for the comparison between the downscaled
product using the MODIS parameters and the SCAN–NRCS SM measurements. In contrast with the
comparison of the SCAN–NRCS measurements with the raw AMSR2 SM products, the downscaled
product achieved a higher percentage of SM estimates that behave similar to the SCAN–NRCS data.
In terms of R, the correlation slightly decreased. A potential solution to the decrease of correlation
could be the optimization of the downscaling technique.
Hydrology 2017, 4, 46
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Table 6. Basic statistics for the comparison of each SCAN–NRCS station and the downscaled AMSR2
SM products using Albedo, NDVI, and LST.
Ascending
Descending
Station
(%)
ME
RMSE
R
(%)
ME
RMSE
R
Adjuntas
Cabo Rojo
Corozal
Guánica
Isabela
Juana Díaz
Maricao
Mayagüez
34
84
35
76
42
77
86
2
0.076
0.063
0.071
0.070
0.079
0.070
0.066
0.106
0.088
0.075
0.082
0.081
0.091
0.081
0.077
0.112
0.607
0.243
0.731
0.455
0.643
0.390
0.529
0.665
33
88
31
81
42
80
88
2
0.074
0.054
0.069
0.076
0.079
0.062
0.055
0.074
0.085
0.066
0.081
0.085
0.090
0.074
0.066
0.085
0.725
0.253
0.755
0.604
0.661
0.424
0.634
0.807
4.3. Downscale of AMSR2 with Sand Fraction, Elevation, and NDVI
This approach for the downscaling of AMSR2 was performed with an algorithm that used the
weighted control parameters sand fraction, elevation, and the Normalized Difference of Vegetation
Index. Figure 6 shows a sample 25 km pixel of the AMSR2 SM (left), the downscaled SM using
MODIS parameters (center), and the downscaled SM using physical parameters (right) during (a) high
humidity (SM ≥ 0.60), (b) moderate humidity (60 > SM > 0.25), and (c) low humidity (SM ≤ 0.20).
(a)
(b)
(c)
3) value (left), AMSR2 SM downscaled using MODIS (middle),
Figure 6. AMSR2 25 km SM (cm3/cm
Figure 6. AMSR2 25 km SM (cm3 /cm3 ) value (left), AMSR2 SM downscaled using MODIS (middle),
and AMSR2 SM downscaled using physical parameters for (a) high humidity; (b) moderate humidity;
and (c) low humidity.
Hydrology 2017, 4, 46
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When the sand fraction is low, the value of the void fraction is higher, which provides more space
for water to infiltrate, and decreases the SM. With changes in elevations, the lowest values accumulate
moisture, providing higher SM values. The NDVI provides an estimation of the vegetation density
in an area. When the NDVI value is high, more precipitation is intercepted, and more moisture is
absorbed by the vegetation, which decreases the SM in the area.
The previous table (Table 7) presents the basic statistics for the comparison between the
downscaled SM product using physical parameters and the SCAN–NRCS SM measurements.
The outcome of the downscale in terms of correlation and percent of SM values that behaved similar to
the SCAN–NRCS measurements was analogous to the results from the first downscaling. The most
significant outcome from this downscale technique is the improvement of SM estimation of the
Mayagüez SCAN–NRCS station. This improvement happens due to the consideration of the changes
in topography, sand fraction, and NDVI in the algorithm, since this particular station is located at the
bottom of a hill in an area where precipitation events are frequent.
Table 7. Basic statistics for the comparison of each SCAN–NRCS station and the downscaled AMSR2
SM products using sand fraction, elevation, and NDVI.
Ascending
Descending
Station
(%)
ME
RMSE
R
(%)
ME
RMSE
R
Adjuntas
Cabo Rojo
Corozal
Guánica
Isabela
Juana Díaz
Maricao
Mayagüez
32
73
37
72
42
71
77
29
0.080
0.072
0.082
0.075
0.084
0.074
0.064
0.088
0.091
0.083
0.092
0.085
0.093
0.085
0.075
0.095
0.820
0.423
0.777
0.378
0.672
0.389
0.516
0.965
22
74
40
79
41
71
88
55
0.079
0.074
0.081
0.079
0.081
0.071
0.072
0.087
0.091
0.083
0.090
0.086
0.090
0.081
0.083
0.091
0.786
0.348
0.699
0.341
0.688
0.410
0.205
0.945
5. Conclusions
The comparison of AMSR2 SM products at 25 km resolution with the SCAN–NRCS stations
revealed that the AMSR2 spatial coverage of 25 km does not provide a good estimate of SM in
Puerto Rico. The overall results of the validation show that about 49% of the AMSR2 SM estimates
behave similar to the SCAN–NRCS SM measurements, with an overall correlation of 0.613. The low
correlation between the coarse resolution estimate and the ground-based measurements is due to
changes in land use, vegetation density, topography, soil properties, and precipitation in Puerto Rico
(Figures A1–A5). As well, in order to have a more robust validation, additional field data is needed
inside the 25 km pixels.
The downscaling method using MODIS parameters provided a better fit with the AMSR2
data. The resulting SM from the linear equation that relates Albedo, LST, and NDVI from
MODIS with the SM from AMSR2 showed a tendency to increase SM where a precipitation event
occurred. This enhancement increased the percentage of AMSR2 estimates that behave similar to the
ground-based measurements to 55%. To expand the coverage of the AMSR2, a daily average SM value
was assumed for the area that was left out of the 25 km grids. This assumption led to the decrease
in correlation, and can be modified in future research to assess this issue. The second approach, in
which a weighted value of physical parameters was used, provided a better projection of how the SM
is distributed throughout the pixel area, increasing the percentage of AMSR2 estimates that behave
similar to the ground-based measurements to 56%. By considering sand fraction, elevation, and NDVI
as variables to enhance SM estimations of the AMSR2, a significant change was observed where the R
in the Mayagüez station increased from 0.647 to 0.955, and the percentage of AMSR2 estimates that
behave similar to the ground-based measurements increased from 4% to 42%.
Hydrology 2017, 4, 46
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Future research is needed to improve the downscaled products for both methodologies, such
as: exploring how the addition of other variables such as precipitation would affect the downscaled
product, a comparison of how different vegetation indexes such as the simple ratio infrared index, or
the soil-adjusted vegetation index influences the quality of the downscaled products. Other future
research will include the optimization of the downscaling techniques through a sensitivity analysis,
and the incorporation of a dense network of sensors placed inside the area of coverage of one AMSR2
pixel, in order to provide a better validation of the AMSR2 and the downscaled SM products in
Puerto Rico.
Acknowledgments: This study was supported by Puerto Rico Water Resources and Environmental Research
Institute operating under Section 104 of the Water Research and Development Act of 1984 (P.L. 98-242), University
of Puerto Rico and NOAA grant NA14NES4320003 (Cooperative Institute for Climate and Satellites -CICS) at
the University of Maryland/ESSIC. The statements contained within the manuscript/research article are not the
opinions of the funding agency or the United States government but reflect the author’s opinions.
Author Contributions: J.N.O. and J.M.-B. were responsible for the data analysis, writing the manuscript, and
communicating with the journal. R.T.-C., T.L., and A.F. were responsible for supervising the analysis, structure of
the manuscript, and providing key components for discussion on results and conclusions.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
The following section contains Puerto Rico maps for Land Cover, MODIS NDVI, Elevation,
Soil Types, and Precipitation with their respective references.
Figure A1.
A1. Land
Land Use
Use map
map for
for Puerto
Puerto Rico
Rico (data
(data retrieved
retrievedfrom
fromhttp://www2.pr.gov).
http://www2.pr.gov).
Figure
FigureA2.
A2.MODIS
MODISNDVI
NDVImap
mapfor
forPuerto
PuertoRico
Rico(data
(datafrom
fromhttps://search.earthdata.nasa.gov/search).
https://search.earthdata.nasa.gov/search).
Figure
Hydrology 2017, 4, 46
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)
Figure A3. Elevation Data map for Puerto Rico (DEM data from https://gdg.sc.egov.usda.gov/).
Figure A3. Elevation Data map for Puerto Rico (DEM data from https://gdg.sc.egov.usda.gov/).
Figure A4. Soil Orders map for Puerto Rico (map retrieved from the NRCS Caribbean Area USDA
Figure A4. Soil Orders map for Puerto Rico (map retrieved from the NRCS Caribbean Area USDA
website https://www.nrcs.usda.gov/Internet/FSE_MEDIA/nrcseprd955406.jpg).
Figure A5. Precipitation data for Puerto Rico (data retrieved from https://pragwater.com/tag/goes-prweb/).
Figure
A5. Precipitation data for Puerto Rico (data retrieved from https://pragwater.com/tag/goesprweb/).
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