Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

Abstract. This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA’s long lasting AMSR-E mission. Additionally three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-Active; CCI-Passive; CCI-Combined). All of these products were quarter degree and daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T). We examined five different eras (1997–2002; 2002–2005; 2005–2008; 2008–2011; 2011–2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the U.S. Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root zone soil moisture had a reasonable high lagged correlation coefficient (r > 0.5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RSME) and calculation based on the NDVI value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI based estimates. Best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. Average Nash-Sutcliffe coefficients (NS) for ARM ranged from −0.1 to 0.3 and were similar to the results obtained from the USCRN network (0.2 to 0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1 to 0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of root mean square error (RMSE; in volumetric soil moisture) ARM values actually outperformed those from other networks (0.02 to 0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher ranging (0.05 to 0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

Download Full-text

Validation of SMAP and AMSR2 satellite soil moisture data over the Critical Zone Observatory in central Ganga plains, North India using ground-based observations

10.5194/egusphere-egu21-4667 ◽

2021 ◽

Author(s):

Saroj Dash ◽

Rajiv Sinha

Keyword(s):

Soil Moisture ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Critical Zone ◽

North India ◽

Mean Square ◽

Critical Zone Observatory ◽

Level 3

Soil moisture (SM) products derived from the passive satellite missions have been extensively used in various hydrological and environmental processes. However, validation of the satellite derived product is crucial for its reliability in several applications. In this study, we present a comprehensive validation of the descending SM product from Soil Moisture Active Passive (SMAP) Enhanced Level-3 (L3) radiometer (SMAP L3-Version 3) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) Level-3 (Version 1), over the newly established Critical Zone Observatory (CZO) within the Ganga basin, North India. The AMSR2 soil moisture product used here, has been derived using the Land Parameter Retrieval Model (LPRM) algorithm. Four SM derived products from SMAP (L-band) and AMSR2 (C1- and C2- and X-band) are validated against the in-situ observations collected from 21 SM monitoring locations distributed over the CZO within a period from September 2017 to December 2019, for a total of 62 days. Since the remotely sensed SM product has a coarser spatial resolution (here 9 km for SMAP and 10 km for AMSR2), the assessment has been carried out for the temporal variation of the measured values. Four statistical metrics such as bias, root mean square error (RMSE), unbiased root-mean-square error (ubRMSE) and the correlation coefficient (R) have been used here for the evaluation. The SMAP Level-3 products are found to show a satisfactory correlation (R>0.6) compared to the other three SM product. Both the SMAP L3 and the AMSR2 C2 SM shows a negative bias, -0.05 m3/m3 and -0.04 m3/m3 respectively whereas these values are found to be 0.04 m3/m3 and 0.06 m3/m3 for C1 and X bands of AMSR2, respectively. Furthermore, the RMSE between the SMAP L3 and in-situ data is 0.07 m3/m3, which is slightly underperformed when considering the required accuracy of SMAP. This is possibly due to variation in the sampling depth along with the sampling day distribution over CZO. The AMSR2 SM products (C1-, C2- and X-bands) are found to have a higher RMSE than SMAP L3, ranging from 0.08-0.1 m3/m3. In addition, the ubRMSE for all remotely sensed soil moisture product range from 0.06-0.08 m3/m3 with the lowest value for the SMAP L3 and AMSR2 C1. The results in this study can be used further for relevant hydrological modelling along with evaluating various downscaling strategies towards improving the coarser resolution satellite soil moisture.

Download Full-text

Triple Collocation-Based Assessment of Satellite Soil Moisture Products with In Situ Measurements in China: Understanding the Error Sources

Remote Sensing ◽

10.3390/rs12142275 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2275 ◽

Cited By ~ 1

Author(s):

Xiaotao Wu ◽

Guihua Lu ◽

Zhiyong Wu ◽

Hai He ◽

Tracy Scanlon ◽

...

Keyword(s):

Soil Moisture ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Land Surface ◽

Surface Model ◽

Mean Square ◽

Error Sources

With the increasing utilization of satellite-based soil moisture products, a primary challenge is knowing their accuracy and robustness. This study presents a comprehensive assessment over China of three widely used global satellite soil moisture products, i.e., Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (CCI) Soil Moisture, Soil Moisture and Ocean Salinity (SMOS). In situ soil moisture from 1682 stations and Variable Infiltration Capacity (VIC) model are used to evaluate the performance of SMAP_L3, ESA_CCI_SM_COMBINED, SMOS_CATDS_L3 from 31 March 2015 to 3 June 2018. The Triple Collocation (TC) approach is used to minimize the uncertainty (e.g., scale issue) during the validation process. The TC analysis is conducted using three triplets, i.e., [SMAP-Insitu-VIC], [CCI-Insitu-VIC], [SMOS-Insitu-VIC]. In general, SMAP is the most reliable product, reflecting the main spatiotemporal characteristics of soil moisture, while SMOS has the lowest accuracy. The results demonstrate that the overall root mean square error of SMAP, CCI, SMOS is 0.040, 0.028, 0.107 m3m−3, respectively. The overall temporal correlation coefficient of SMAP, CCI, SMOS is 0.68, 0.65, 0.38, respectively. The overall fractional root mean square error of SMAP, CCI, SMOS is 0.707, 0.750, 0.897, respectively. In irrigated areas, the accuracy of CCI is reduced due to the land surface model (which does not consider irrigation) used for the rescaling of the CCI_COMBINED soil moisture product during the merging process, while SMAP and SMOS preserve the irrigation signal. The quality of SMOS is most strongly impacted by land surface temperature, vegetation, and soil texture, while the quality of CCI is the least affected by these factors. With the increase of Radio Frequency Interference, the accuracy of SMOS decreases dramatically, followed by SMAP and CCI. Higher representativeness error of in situ stations is noted in regions with higher topographic complexity. This study helps to provide a guideline for the application of satellite soil moisture products in scientific research and gives some references (e.g., modify data algorithm according to the main error sources) for improving the data quality.

Download Full-text

Evaluation of Sentinel-3A Wave Height Observations Near the Coast of Southwest England

Remote Sensing ◽

10.3390/rs11242998 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2998 ◽

Cited By ~ 8

Author(s):

Francesco Nencioli ◽

Graham D. Quartly

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Wave Height ◽

Significant Wave Height ◽

Mean Square ◽

Significant Wave ◽

In Situ Observations ◽

Regression Slopes

Due to the smaller ground footprint and higher spatial resolution of the Synthetic Aperture Radar (SAR) mode, altimeter observations from the Sentinel-3 satellites are expected to be overall more accurate in coastal areas than conventional nadir altimetry. The performance of Sentinel-3A in the coastal region of southwest England was assessed by comparing SAR mode observations of significant wave height against those of Pseudo Low Resolution Mode (PLRM). Sentinel-3A observations were evaluated against in-situ observations from a network of 17 coastal wave buoys, which provided continuous time-series of hourly values of significant wave height, period and direction. As the buoys are evenly distributed along the coast of southwest England, they are representative of a broad range of morphological configurations and swell conditions against which to assess Sentinel-3 SAR observations. The analysis indicates that SAR observations outperform PLRM within 15 km from the coast. Within that region, regression slopes between SAR and buoy observations are close to the 1:1 relation, and the average root mean square error between the two is 0.46 ± 0.14 m. On the other hand, regression slopes for PLRM observations rapidly deviate from the 1:1 relation, while the average root mean square error increases to 0.84 ± 0.45 m. The analysis did not identify any dependence of the bias between SAR and in-situ observation on the swell period or direction. The validation is based on a synergistic approach which combines satellite and in-situ observations with innovative use of numerical wave model output to help inform the choice of comparison regions. Such an approach could be successfully applied in future studies to assess the performance of SAR observations over other combinations of coastal regions and altimeters.

Download Full-text

Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the Great Lakes Region

Remote Sensing ◽

10.3390/rs12223785 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3785

Author(s):

Xiaoyong Xu

Keyword(s):

Soil Moisture ◽

Great Lakes ◽

Root Zone ◽

Evaluation Studies ◽

Reference Network ◽

Great Lakes Region ◽

Sparse Network ◽

Level 2 ◽

The U.S

Satellite sensor systems for soil moisture measurements have been continuously evolving. The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard. Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region of North America has originated from evaluation studies using in situ data from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture in this region, which is characterized by a relatively large soil moisture variability and is one of the least studied regions. In this work, SMAP Level 2‒4 soil moisture products: SMAP/Sentinel-1 L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU) are evaluated over the southern portion of the Great Lakes region using in situ measurements from Michigan State University’s Enviro-weather Automated Weather Station Network. The unbiased root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture estimates are below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of 0.045 m3 m−3 (0.037 m3 m−3) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of ~0.06 m3 m−3 against the sparse network. The average ubRMSE values are ~0.05‒0.06 m3 m−3 for high-resolution SPL2SMAP_S soil moisture retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture retrievals exceeding that of the optional algorithm-based counterparts. Clearly, the skill of SPL4SMAU surface soil moisture exceeds that of the SPL3SMP_E and SPL2SMAP_S soil moisture retrievals.

Download Full-text

Pola Distribusi Konsentrasi Klorofil-a di Laut Maluku berdasarkan Pengamatan In Situ INDESO Joint Expedition Program 2016 dan Data Penginderaan Jauh

Jurnal Segara ◽

10.15578/segara.v13i3.6494 ◽

2017 ◽

Vol 13 (2) ◽

Author(s):

Indra Hermawan ◽

Agus Setiawan ◽

Nikita Pusparini

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mixed Layer ◽

Mean Square

Tujuan dari penelitian ini adalah untuk mengetahui pola distribusi konsentrasi klorofil-a di perairan Laut Maluku yang termasuk dalam Wilayah Pengelolaan Perikanan RI 715 berdasarkan data pengamatan in situ dan penginderaan jauh. Penelitian ini dilakukan pada bulan September 2016 dan merupakan bagian dari Pelayaran Oseanografi INDESO Joint Expedition Program (IJEP) 2016 yang dilaksanakan oleh Badan Penelitian dan Pengembangan Kelautan dan Perikanan (Balitbang KP) menggunakan Kapal Riset Baruna Jaya VIII. Berdasarkan hasil pengamatan in situ pada 8 titik pengamatan di sepanjang Laut Maluku dari selatan ke utara, didapatkan bahwa konsentrasi klorofil-a di permukaan berkisar antara 0,1 hingga 0,6 mg/m3. Hasil perbandingan antara konsentrasi klorofil-a hasil pengamatan in-situ dengan model biogeokimia INDESO dan citra satelit SeaWiFS masing-masing memberikan root mean square error sebesar 0,1507 dan 0,1364 mg/m3. Sementara itu, secara vertikal konsentrasi klorofil-a maksimum (antara 0,4 hingga 1 mg/m3) ditemukan pada kedalaman antara 17 hingga 61 meter, yaitu pada lapisan mixed layer.

Download Full-text

On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

Hydrology and Earth System Sciences ◽

10.5194/hess-12-1339-2008 ◽

2008 ◽

Vol 12 (6) ◽

pp. 1339-1351 ◽

Cited By ~ 13

Author(s):

G. Laguardia ◽

S. Niemeyer

Keyword(s):

Soil Moisture ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Land Surface ◽

Soil Depth ◽

Meteorological Data ◽

Data Retrieval ◽

Mean Square ◽

Mountainous Regions

Abstract. In order to evaluate the reliability of the soil moisture product obtained by means of the LISFLOOD hydrological model (De Roo et al., 2000), we compare it to soil moisture estimates derived from ERS scatterometer data (Wagner et al., 1999b). Once evaluated the effect of scale mismatch, we calculate the root mean square error and the correlation between the two soil moisture time series on a pixel basis and we assess the fraction of variance that can be explained by a set of input parameter fields that vary from elevation and soil depth to rainfall statistics and missing or snow covered ERS images. The two datasets show good agreement over large regions, with 90% of the area having a positive correlation coefficient and 66% having a root mean square error minor than 0.5 pF units. Major inconsistencies are located in mountainous regions such as the Alps or Scandinavia where both the methodologies suffer from insufficiently resolved land surface processes at the given spatial resolution, as well as from limited availability of satellite data on the one hand and the uncertainties in meteorological data retrieval on the other hand.

Download Full-text

Numerical Simulation of Unsaturated Soil Water Flow from a Trickle Point System, Considering Evaporation and Root Water Uptake

Al-Khwarizmi Engineering Journal ◽

10.22153/kej.2018.05.005 ◽

2018 ◽

Vol 14 (4) ◽

pp. 103-114

Author(s):

Lina Ali Khalil ◽

Maysoon Basheer Abid

Keyword(s):

Soil Moisture ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Water Flow ◽

Single Point ◽

Maximum Error ◽

Soil Types ◽

Point System ◽

Mean Square

This research was carried out to study the effect of plants on the wetted area for two soil types in Iraq and predict an equation to determine the wetted radius and depth for two different soil types cultivated with different types of plants, the wetting patterns for the soils were predicted at every thirty minute for a total irrigation time equal to 3 hr. Five defferent discharges of emitter and five initial volumetric soil moisture contents were used ranged between field capacity and wilting point were utilized to simulate the wetting patterns. The simulation of the water flow from a single point emitter was completed by utilized HYDRUS-2D/3D software, version 2.05. Two methods were used in developing equations to predict the domains of the wetting pattern. The principal strategy manages each soil independently and includes plotting, fitting, and communicating relevant connections for wetted zone and profundity, maximum error did not exceed 31.2%, modeling efficiency did not less 0.95, and root mean square error did not surpass 1.43 cm. The second strategy additionally treated each soil independently yet used electronic programming that uses different relapse methods for wetted territory and profundity, the maximum error did not exceed 15.64 %, modeling efficiency did not less 0.98, and root mean square error did not surpass 1.18 cm. a field test was directed to quantify the wetted radius to check the outcome acquired by the software HYDRUS-2D, contrast the estimation and the reproduced by the software. The after effects of the conditions to express the wetted radius and depth regarding the time of water system, producer release, and initial soil moisture content were general and can be utilized with great precision.

Download Full-text

UJI AKURASI PRODUK ESTIMASI CURAH HUJAN SATELIT GPM IMERG DI SURABAYA, INDONESIA

Jurnal Sains & Teknologi Modifikasi Cuaca ◽

10.29122/jstmc.v19i2.3153 ◽

2018 ◽

Vol 19 (2) ◽

pp. 83

Author(s):

Mukhamad Adib Azka ◽

Prabu Aditya Sugianto ◽

Andreas Kurniawan Silitonga ◽

Imma Redha Nugraheni

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Absolute Error ◽

Absolute Error ◽

Mean Square

Curah hujan merupakan parameter meteorologi yang sangat berpengaruh dalam kehidupan. Saat ini, pengamatan secara in situ sangat kurang representatif untuk digunakan sebagai analisis karena jangkauannya yang sangat sempit sehingga memerlukan instrumen pendukung seperti satelit agar dapat memberikan gambaran yang lebih baik terkait distribusi hujan. Namun, data satelit juga belum tentu sepenuhnya benar karena resolusi dan kondisi dari setiap wilayah berbeda. Penelitian ini bertujuan untuk mendapatkan nilai akurasi, bias, korelasi, root mean square error (RMSE), dan mean absolute error (MAE) data estimasi curah hujan GPM IMERG dengan data curah hujan pengamatan langsung. Penelitian ini dilakukkan di Surabaya dengan menggunakan data estimasi curah hujan GPM IMERG dan data curah hujan pengamatan langsung dari Stasiun Meteorologi Kelas I Juanda Surabaya selama tahun 2017 mewakili musim hujan, musim kemarau, dan periode transisi. Hasil penelitian menunjukkan bahwa data curah hujan produk GPM IMERG memiliki korelasi yang sangat baik untuk memperkirakan akumulasi curah hujan bulanan. Sedangkan, untuk akumulasi harian, memiliki korelasi yang sangat rendah. Sementara itu untuk akumulasi sepuluh harian, data curah hujan produk satelit GPM IMERG memiliki korelasi yang baik terutama di periode musim hujan dan musim kemarau, akan tetapi memiliki korelasi yang rendah selama periode transisi dari musim hujan ke musim kemarau atau sebaliknya. Pada umumnya, produk ini sangat bagus dalam menentukan ada atau tidaknya hujan, tetapi performanya sangat rendah dalam menentukan besarnya intensitas curah hujan.

Download Full-text

Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4403-2017 ◽

2017 ◽

Vol 21 (9) ◽

pp. 4403-4417 ◽

Cited By ~ 11

Author(s):

Kenneth J. Tobin ◽

Roberto Torres ◽

Wade T. Crow ◽

Marvin E. Bennett

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Root Zone ◽

European Space Agency ◽

Department Of Energy ◽

Reference Network ◽

Optimization Approach ◽

Moisture Variation ◽

The Us

Abstract. This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA's long-lasting AMSR-E mission. Additionally, three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25° and taken daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T). We examined five different eras (1997–2002; 2002–2005; 2005–2008; 2008–2011; 2011–2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root-zone soil moisture had a reasonably high lagged r value (r > 0. 5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI-based estimates. The best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash–Sutcliffe coefficients (NSs) for ARM ranged from −0. 1 to 0.3 and were similar to the results obtained from the USCRN network (0.2–0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1–0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02–0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05–0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

Download Full-text

On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-5-1227-2008 ◽

2008 ◽

Vol 5 (3) ◽

pp. 1227-1265 ◽

Cited By ~ 2

Author(s):

G. Laguardia ◽

S. Niemeyer

Keyword(s):

Soil Moisture ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Land Surface ◽

Soil Depth ◽

Meteorological Data ◽

Data Retrieval ◽

Mean Square ◽

Mountainous Regions

Abstract. In order to evaluate the reliability of the soil moisture product obtained by means of the LISFLOOD hydrological model (De Roo et al., 2000), we compare it to soil moisture estimates derived from ERS scatterometer data (Wagner et al., 1999). Once calculated the root mean square error and the correlation between the two soil moisture time series on a pixel basis, we assess the fraction of variance that can be explained by a set of input parameter fields that vary from elevation and soil depth to rainfall statistics and missing or snow covered ERS images. The two datasets show good agreement over large regions, with 90% of the area having a positive correlation coefficient and 66% having a root mean square error minor than 0.5. Major inconsistencies are located in mountainous regions such as the Alps or Scandinavia where both the methodologies suffer from insufficiently resolved land surface processes at the given spatial resolution, as well as from limited availability of satellite data on the one hand and the uncertainties in meteorological data retrieval on the other hand.

Download Full-text