Evaluation of satellite-based soil moisture retrieval over the korean peninsula : using AMSR2 LPRM algorithm and ground measurement data

2016 ◽  
Vol 49 (5) ◽  
pp. 423-429 ◽  
Author(s):  
Seongkyun Kim ◽  
Hyunglok Kim ◽  
Minha Choi
Keyword(s):  
Soil Moisture ◽  
Korean Peninsula ◽  
Measurement Data ◽  
Ground Measurement

scholarly journals Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1292
Author(s):  
Hongchun Zhu ◽  
Zhilin Zhang ◽  
Aifeng Lv
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Microwave Radiation ◽  
Satellite Data ◽  
Measurement Data ◽  
Ground Truth ◽  
Error Variance ◽  
Vegetation Coverage ◽  
Qinghai Province ◽  
Ground Measurement

Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often difficult to obtain. The triple collocation (TC) method can be used to evaluate the accuracy of a product without obtaining the ground measurement data. This study focused on the whole of Qinghai Province, China (31°–40° N, 89°–103° E), and used the TC method to obtain the error variance for satellite-based soil moisture data, the signal-to-noise ratio (SNR) of the same data, and the correlation between the same data and the ground-truth soil moisture, using passive satellite products: Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS), Fengyun-3B Microwave Radiation Imager (FY3B), Fengyun-3C Microwave Radiation Imager (FY3C), and Advanced Microwave Scanning Radiometer 2 (AMSR2); an active satellite product Advanced Scatterometer (ASCAT), and reanalysis data Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system. The TC results for the passive satellite data were then compared with the satellite-derived enhanced vegetation index (EVI) to explore the influence of vegetation coverage on the results. The following conclusions are drawn: (1) for the SMAP, SMOS, FY3B, FY3C, and AMSR2 satellite data, the spatial distributions of the TC-derived error variance, the SNR of the satellite-derived soil moisture, and the correlation coefficient between the satellite-derived and ground-truth soil moisture, were all relatively similar, which indirectly verified the reliability of the TC method; and (2) SMOS data have poor applicability for the estimation of soil moisture in Qinghai Province due to their insufficient detection capability in the Qaidam area, high error variance (median 0.0053), high SNR (median 0.43), and low correlation coefficient with ground-truth soil moisture (median 0.57).

scholarly journals Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Xiaodong Huang ◽  
Beth Ziniti ◽  
Michael H. Cosh ◽  
Michele Reba ◽  
Jinfei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Canopy Cover ◽  
The United States ◽  
Optical Data ◽  
Study Region ◽  
Model Based ◽  
Key Indicator ◽  
Ground Measurement ◽  
L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

scholarly journals Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites

2021 ◽  
Vol 13 (11) ◽  
pp. 2032
Author(s):  
Junchan Lee ◽  
Sunil Bisnath ◽  
Regina S.K. Lee ◽  
Narin Gavili Kilane
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Measurement Data ◽  
Satellite System ◽  
Computation Method ◽  
Navigation Satellite ◽  
Local Soil ◽  
Global Navigation ◽  
Global Navigation Satellite

This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.

The Methods of Mapping Lithology with AVIRIS Data

2014 ◽  
Vol 955-959 ◽  
pp. 3879-3882
Author(s):  
Yan Fang Ming ◽  
Li Yang
Keyword(s):  
Remote Sensing ◽  
Measurement Data ◽  
Absorption Index ◽  
Mining District ◽  
Spectral Absorption ◽  
Spectral Library ◽  
Hyper Spectral ◽  
Ground Measurement ◽  
Spectral Angle

A hyper-spectral remote sensing instrument AVIRIS was used to map lithology. Ground measurement data of lithology from ASTER Spectral Library were used to analyze the characters of the spectrum and form the model to estimate the type of lithologies. we process the spectrum with the methods of spectral angle mapping, and spectral absorption index etc. To enhance the significance of the spectrum character, we used the methods of spectral angle mapping, and spectral absorption index etc to process the spectrum. An AVIRIS data covers the Cuprite mining district in western Nevada, USA was used to do the experiment, result shows that the lithologies got from the AVIRIS have a high consistence with groud measurement.

scholarly journals Studi Perbandingan Hasil Pengukuran Alat Teodolit Digital dan Manual: Studi Kasus Pemetaan Situasi Kampus Kijang

2011 ◽  
Vol 2 (2) ◽  
pp. 1013
Author(s):  
Andryan Suhendra
Keyword(s):  
Civil Engineer ◽  
Data Processing ◽  
Coordinate System ◽  
Measurement Data ◽  
Abscissa Axis ◽  
Point Measurement ◽  
Ordinate Axis ◽  
Center Point ◽  
Ground Measurement ◽  
Land Leveling

Ground measurement helps illustrate a situation of land map to ease a civil engineer determining the center point of a building. From the situation map can determined the further works such as determining the point of building, land leveling, determining the point of the foundation and the volume of work for hoarding the land. A tool used for measurement activities is theodolite. Theodolite serves as a tool to determine the angle formed between the two points at the time of measurement. Drawing a situation map requires the results of the point measurement data. Theodolite is divided into two types, digital and manual. This study compares measurements results using both digital and manual theodolite performed at Kijang Campus, Binus University. From the comparison of data processing generated large differences in the coordinate system on the situation map with ranging from 1.31% to 322.67% on the abscissa axis and 0.39% to 41.83% on the ordinate axis. 

scholarly journals The IoT-BASED TEMPERATURE AND HUMIDITY MONITORING SYSTEM IN THE PROCESS OF MAKING ORGANIC FERTILIZER

SAINTEKBU ◽  
2021 ◽  
Vol 13 (02) ◽  
pp. 1-12
Author(s):  
Muhammad Saiful Amin ◽  
Ambar Susanti ◽  
Primaadi Airlangga
Keyword(s):  
Soil Moisture ◽  
Internet Of Things ◽  
Temperature Measurement ◽  
Organic Fertilizer ◽  
Measurement Data ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Temperature And Humidity ◽  
Humidity Monitoring ◽  
Monitoring Temperature

The Process Of Making Organic Fertilizer requires a certain temperature and humidity to keep the microorganisms used to be able to stay alive and can decompose perfectly, for that it is necessary to monitor temperature and humidity. Along with the development of this technology, it can be done by remote automation using a microcontroller on the ESP8266 nodemcu based on IoT (Internet Of Things), which is combined with the DS18B20 sensor for temperature measurement and soil moisture sensor, as a humidity meter with a temperature unit of °C. The humidity meter is an analog signal, the larger the value, the more humid of organic fertilizer. Acquired measurement data is sent to the Smartphone using an internet-connected ESP8266 monitoring temperature and humidity, which can be viewed via the Blynk App.

scholarly journals Climatology of Planetary Boundary Layer Height-Controlling Meteorological Parameters Over the Korean Peninsula

2020 ◽  
Vol 12 (16) ◽  
pp. 2571
Author(s):  
Shaik Allabakash ◽  
Sanghun Lim
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Planetary Boundary Layer ◽  
Korean Peninsula ◽  
Meteorological Parameters ◽  
Weather Forecasting ◽  
High Elevation ◽  
Transport Processes ◽  
Surface Fluxes ◽  
Pollution Transport

Planetary boundary layer (PBL) height plays a significant role in climate modeling, weather forecasting, air quality prediction, and pollution transport processes. This study examined the climatology of PBL-associated meteorological parameters over the Korean peninsula and surrounding sea using data from the ERA5 dataset produced by the European Centre for Medium-range Weather Forecasts (ECMWF). The data covered the period from 2008 to 2017. The bulk Richardson number methodology was used to determine the PBL height (PBLH). The PBLH obtained from the ERA5 data agreed well with that derived from sounding and Global Positioning System Radio Occultation datasets. Significant diurnal and seasonal variability in PBLH was observed. The PBLH increases from morning to late afternoon, decreases in the evening, and is lowest at night. It is high in the summer, lower in spring and autumn, and lowest in winter. The variability of the PBLH with respect to temperature, relative humidity, surface pressure, wind speed, lower tropospheric stability, soil moisture, and surface fluxes was also examined. The growth of the PBLH was high in the spring and in southern regions due to the low soil moisture content of the surface. A high PBLH pattern is evident in high-elevation regions. Increasing trends of the surface temperature and accordingly PBLH were observed from 2008 to 2017.

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