Backscattering Calculation of Inflatable Dielectric Lifting-turning Device for Ground Measurement of Radar Object Scattering Characteristics

2020 ◽  
Author(s):  
Oleg Sukharevsky ◽  
Gennady Zalevsky ◽  
Ivan Ryapolov ◽  
Vitaly Vasilets
Keyword(s):  
Ground Measurement ◽  
Object Scattering

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 Estimation of Incident Photosynthetically Active Radiation from GOES Visible Imagery

2008 ◽  
Vol 47 (3) ◽  
pp. 853-868 ◽  
Author(s):  
Tao Zheng ◽  
Shunlin Liang ◽  
Kaicun Wang
Keyword(s):  
Photosynthetically Active Radiation ◽  
Terrestrial Ecosystem ◽  
New Method ◽  
High Temporal Resolution ◽  
Atmospheric Conditions ◽  
Ecosystem Models ◽  
Surface Conditions ◽  
Active Radiation ◽  
Ground Measurement ◽  
Visible Imagery

Abstract Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.

Vegetation Canopy Coverage Estimation Using Physical Models

2013 ◽  
Vol 726-731 ◽  
pp. 4709-4713
Author(s):  
Lin Jing Zhang ◽  
Hong Zhang Ma ◽  
Zhu Bo Zhou ◽  
Zhong Liang Ren ◽  
Xiao Bo Zhu ◽  
...  
Keyword(s):  
Regression Model ◽  
Simulated Data ◽  
Hyperspectral Data ◽  
Physical Models ◽  
Vegetation Canopy ◽  
Canopy Coverage ◽  
Vegetation Indexes ◽  
Ground Measurement ◽  
Highly Correlated ◽  
Coverage Estimation

Based on the physical models of PROSPECT, SAIL and porosity model, hyperspectral data and canopy coverage data of different combined scenes were simulated. According to the simulated data, we chose four sensitive bands and four sensitive vegetation indexes highly correlated to vegetation canopy coverage, and analyzed the correlation between sensitive bands, sensitive vegetation indexes and canopy coverage. Then we built a regression model of canopy coverage with EVI highly correlated with canopy coverage. At last, we verified this model by experimental data from ground measurement experiment. It shows that there is a high correlation between EVI and canopy coverage and the regression model built by EVI can produce an effective result and the RMSE is less than 0.09.

Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales – Geostatistical interpolation framework

2021 ◽  
Author(s):  
Micha Eisele ◽  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Pearson Correlation ◽  
Urban Catchment ◽  
Space And Time ◽  
Geostatistical Interpolation ◽  
Primary Network ◽  
Ground Measurement ◽  
Catchment Areas ◽  
Important Input ◽  
Spatio Temporal

<p>Precipitation - highly variable in space and time - is the most important input for many hydrological models. As these models become more and more detailed in space and time, high-resolution input data are required. Especially for modeling and prediction in fast reacting catchments, such as urban catchment areas, a higher space-time resolution is needed than the current ground measurement networks operated by national weather services usually provide. With the increasing number and availability of opportunistic sensors such as commercial microwave links (CMLs) and personal weather stations (PWS) in recent years, new opportunities for measuring meteorological data are emerging.</p><p>We developed a geostatistical interpolation framework which allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g. point and line information. In this framework, a combined kriging approach is introduced, taking into account not only the point information of a reliable primary network, e.g., from national weather services, but also the higher uncertainty of the PWS- and CML-based precipitation. The path-averaged information of the CMLs is included through a block kriging-type approach.</p><p>The methodology was applied for two 7-months periods in Germany using an hourly temporal and a 1x1 km spatial resolution. By incorporating CMLs and PWS, the Pearson correlation could be increased from 0.56 to 0.73 compared to using only primary network for interpolation. The resulting precipitation maps also provided good agreement compared to gauge adjusted radar products.</p>

scholarly journals A Slight Temperature Warming Trend Occurred over Lake Ontario from 2001 to 2018

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1315
Author(s):  
Xiaoying Ouyang ◽  
Dongmei Chen ◽  
Shugui Zhou ◽  
Rui Zhang ◽  
Jinxin Yang ◽  
...  
Keyword(s):  
Land Surface ◽  
Lake Ontario ◽  
Kendall Test ◽  
Warming Trend ◽  
Linear Regression Method ◽  
Ground Measurement ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Mean Square Difference

Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months.

First look at the urban carbon flux inversion system for Megacity CO2-Seoul

2021 ◽  
Author(s):  
Eunsil Oh ◽  
Sujong Jeong ◽  
Yeonsu Kim ◽  
Hoonyoung Park ◽  
Charin Park ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Carbon Flux ◽  
Inverse Model ◽  
Machine Learning Techniques ◽  
Measurement Instruments ◽  
Model Framework ◽  
Learning Techniques ◽  
Ground Measurement ◽  
National University ◽  
Seoul National University

<p>To verify the urban fossil fuel carbon dioxide (FFCO<sub>2</sub>) flux over the Seoul Capital Area (SCA), we initiated the “Megacity CO<sub>2</sub>-Seoul” project in the year 2018. For the project, our research group established CO<sub>2</sub> and XCO<sub>2</sub> ground measurement stations deploying Seoul National University CO<sub>2</sub> Measurement instruments (SNUCO<sub>2</sub>M) and EM27/SUN. We also produced 1x1km urban biospheric flux with the CArbon Simulator from Space (CASS) and 1x1km FFCO<sub>2</sub> carbon emission inventory by employing machine learning techniques. The project comprises inverse modeling system using WRF-STILT. Under the Bayesian inverse model framework, we assess FFCO<sub>2</sub> inventory of Seoul, which are generated by the bottom-up approach, by paring the ground CO<sub>2</sub> measurement constraints. This is the first look at the verification of self-developed FFCO<sub>2</sub> inventory of Seoul. We are currently working on the improvement of the WRF-STILT inverse modeling system. In this presentation, we report verification of FFCO<sub>2</sub> emissions in SCA on February 2018. Our estimate reflects that our prior FFCO<sub>2</sub> inventory was overestimated in the comparison with results of the inverse model. Detailed results will be presented at the webinar. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government (MSIT) (No. NRF-2019R1A2C3002868).</p>

scholarly journals Development and Testing of a New Ground Measurement Tool to Assist in Forest GIS Surveys

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 643 ◽  
Author(s):  
Guangpeng Fan ◽  
Feixiang Chen ◽  
Yan Li ◽  
Binbin Liu ◽  
Xu Fan
Keyword(s):  
Root Mean Square Error ◽  
Mobile Phone ◽  
Mean Square Error ◽  
Tree Species ◽  
Tree Height ◽  
Measurement Tool ◽  
Mean Square ◽  
Height Measurement ◽  
Test Dataset ◽  
Ground Measurement

In present forest surveys, some problems occur because of the cost and time required when using external tools to acquire tree measurement. Therefore, it is of great importance to develop a new cost-saving and time-saving ground measurement method implemented in a forest geographic information system (GIS) survey. To obtain a better solution, this paper presents the design and implementation of a new ground measurement tool in which mobile devices play a very important role. Based on terrestrial photogrammetry, location-based services (LBS), and computer vision, the tool assists forest GIS surveys in obtaining important forest structure factors such as tree position, diameter at breast height (DBH), tree height, and tree species. This paper selected two plots to verify the accuracy of the ground measurement tool. Experiments show that the root mean square error (RMSE) of the position coordinates of the trees was 0.222 m and 0.229 m, respectively, and the relative root mean square error (rRMSE) was close to 0. The rRMSE of the DBH measurement was 10.17% and 13.38%, and the relative Bias (rBias) of the DBH measurement was −0.88% and −2.41%. The rRMSE of tree height measurement was 6.74% and 6.69%, and the rBias of tree height measurement was −1.69% and −1.27%, which conforms to the forest investigation requirements. In addition, workers usually make visual observations of trees and then combine their personal knowledge or experience to identify tree species, which may lead to the situations when they cannot distinguish tree species due to insufficient knowledge or experience. Based on MobileNets, a lightweight convolutional neural network designed for mobile phone, a model was trained to assist workers in identifying tree species. The dataset was collected from some forest parks in Beijing. The accuracy of the tree species recognition model was 94.02% on a test dataset and 93.21% on a test dataset in the mobile phone. This provides an effective reference for workers to identify tree species and can assist in artificial identification of tree species. Experiments show that this solution using the ground measurement tool saves time and cost for forest resources GIS surveys.

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