scholarly journals ESTIMATION OF PHYSICAL PARAMETERS OF A MULTILAYERED MULTI-SCALE VEGETATED SURFACE

2016 ◽  
Vol XLI-B1 ◽  
pp. 453-460
Author(s):  
I. Hosni ◽  
L. Bennaceur Farah ◽  
M. S. Naceur ◽  
I. R. Farah
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Cloud Model ◽  
Optical Data ◽  
Vegetation Coverage ◽  
Physical Parameters ◽  
Atmospheric Effects ◽  
Optical Remote Sensing ◽  
Statistical Parameters ◽  
The Impact

Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. <br><br> Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. <br><br> Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. <br><br> This paper presents a synergistic methodology of SAR and optical remote sensing data, and it’s for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. <br><br> Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.

scholarly journals ESTIMATION OF PHYSICAL PARAMETERS OF A MULTILAYERED MULTI-SCALE VEGETATED SURFACE

2016 ◽  
Vol XLI-B1 ◽  
pp. 453-460
Author(s):  
I. Hosni ◽  
L. Bennaceur Farah ◽  
M. S. Naceur ◽  
I. R. Farah
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Cloud Model ◽  
Optical Data ◽  
Vegetation Coverage ◽  
Physical Parameters ◽  
Atmospheric Effects ◽  
Optical Remote Sensing ◽  
Statistical Parameters ◽  
The Impact

Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. <br><br> Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. <br><br> Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. <br><br> This paper presents a synergistic methodology of SAR and optical remote sensing data, and it’s for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. <br><br> Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.

Band Selection for Forest Stock Volume Estimation in Southern China

2012 ◽  
Vol 500 ◽  
pp. 616-622
Author(s):  
Chen Xi Song ◽  
Zhong Sheng Xia ◽  
Yun Shao ◽  
Feng Li Zhang ◽  
Kun Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Southern China ◽  
Remote Sensing Data ◽  
Volume Estimation ◽  
Guizhou Province ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Optimal Ratio ◽  
Stock Volume ◽  
The Impact

When forest stock volume is quantitative estimated using SPOT-5, QuickBird and ALOS optical data with linear regression model, the optimal ratio of remote sensing band is chosen from the above three types of optical remote sensing data respectively, which is a significant part. In this study, the experiments are taken in Zhazuo Forest of Xiuwen County of Guizhou Province. Comprehensive utilization of the three optical data, the selected ratio of band is confirmed according with characteristics of the forest region. Optimization of the ratio of band remote sensing method used is the criteria of mean residual sum of square called RMSq. In this paper the multicollinearity which commonly exsits between ratio of the original band is analyzed and studied to get rid of its unfavorable influence in this paper. By means of the criteria of mean residual sum of square, the ratio of remote sensing band which determines the impact of forest stock volume estimation is confirmed finally. Conclusions are as follows: Compared with the selected band, multiple-correlation has been greatly reduced. The optimal ratio of remote sensing band such as SP4, SP2-3/2+3, SP 1-4/1+4, SP1*3/2 has an important role on the interpretation of forest stock volume estimation.

Development of a Spatially Upscaled Soil Spectral Library (SUSSL) of cropland signatures for EO sensors data simulation and calibration/validation of topsoil organic carbon prediction models

2021 ◽  
Author(s):  
Sabine Chabrillat ◽  
Robert Milewski ◽  
Theres Kuester ◽  
Klara Dvorakova ◽  
Bas van Wesemael
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Prediction Models ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Residual Soil ◽  
Spectral Library ◽  
The Impact ◽  
Soil Reflectance

&lt;p&gt;Optical remote sensing and in particular hyperspectral or imaging spectroscopy remote sensing has been long proved to be an adequate method to predict topsoil organic carbon (Corg) content with good accuracy when the soils are well exposed and undisturbed. Several recent studies demonstrated further in science cases the potential of multispectral Copernicus Sentinel-2 data for bare soils Corg prediction, although challenges were reported related to the impact of disturbing factors. Disturbing factors that can affect the prediction and performances of soil surface properties from optical remote sensing are several and can be e.g. due to mixing in the field-of-view with partial vegetation cover depending on the landscape fragmentation. Most pixels at the remote sensing level are composites and in croplands, mixtures of soils with trees or green plants, or mixture with crop residues after harvest are likely. Another factor might be the presence of residual soil moisture or standing water after rain events. Soil reflectance decreases with increasing soil moisture and increasing soil roughness. Soil Surface roughness changes are observed due to variations in soil texture and to variable microtopography. Possible angular and solar illumination changes may affect the soil reflectance as well.&lt;/p&gt;&lt;p&gt;In the frame of the ESA WORLDSOILS Project (https://www.world-soils.com) aiming at developing a pre-operational Soil Monitoring System to provide yearly estimations of soil organic carbon at global scale based on space-based EO data, we are working on the development of a spatially upscaled soil spectral library (SUSSL). The SUSSL is based on a sub-selection of the European LUCAS soil database, and includes simulation of realistic scenarios of &amp;#8216;landscape-like&amp;#8217; cropland reflectance data with effect of mixture with green and dry vegetation, effect of varying soil moisture content, and effect of variable soil roughness. This database is further convoluted to the different spectral response functions of several EO sensors to simulate EO view of surface reflectances in croplands. In a next step, the SUSSL shall be used for the test and validation of different correction, disaggregation and unmixing techniques to assess the capabilities of the retrieval of undisturbed surface reflectance, to which soil prediction models can be applied with increased accuracy. In this talk, we will present the database developed, including methodological choices and parameter selections for the simulation of the different disturbing effects. Further, preliminary assessments will be shown on the uncertainties of the undisturbed vs. disturbed signal and impact on soil properties prediction.&lt;/p&gt;

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula

2020 ◽  
Vol 12 (3) ◽  
pp. 570 ◽  
Author(s):  
Gerard Portal ◽  
Thomas Jagdhuber ◽  
Mercè Vall-llossera ◽  
Adriano Camps ◽  
Miriam Pablos ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Iberian Peninsula ◽  
Spatial Scale ◽  
Temporal Dynamics ◽  
Microwave Radiometer ◽  
Added Value ◽  
Optical Data ◽  
Enhanced Resolution ◽  
The Impact

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

scholarly journals Rapid Flood Progress Monitoring in Cropland with NASA SMAP

2019 ◽  
Vol 11 (2) ◽  
pp. 191 ◽  
Author(s):  
Md. Rahman ◽  
Liping Di ◽  
Eugene Yu ◽  
Li Lin ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Progress Monitoring ◽  
Soil Porosity ◽  
Optical Remote Sensing ◽  
Model Driven ◽  
Coarse Spatial Resolution ◽  
Direct Indication ◽  
Spatial Hazard ◽  
Flood Maps

Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal resolution SAR data hinder the application of flood mapping in many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture data derived from SMAP observations and are available at 3-h intervals, can offer an intermediate but effective solution. This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information. Soil moisture above the effective soil porosity is a direct indication of soil saturation. Soil moisture also increases considerably during a flood event. Therefore, this approach took into account three conditions to map the flooded pixels: a minimum of 0.05 m3m−3 increment in soil moisture from pre-flood to post-flood condition, soil moisture above the effective soil porosity, and the holding of saturation condition for the 72 consecutive hours. Results indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation (due to the coarse spatial resolution of SMAP). Finally, the inundated croplands are extracted from saturated areas by Spatial Hazard Zone areas (SHFA) of Federal Emergency Management Agency (FEMA) and cropland data layer (CDL). The flood maps extracted from SMAP data are validated with FEMA-declared affected counties as well as with flood maps from other sources.

Far-ultraviolet Ionospheric Photometer

2020 ◽  
Author(s):  
Ruyi Peng ◽  
Liping Fu
Keyword(s):  
High Sensitivity ◽  
Detection Sensitivity ◽  
Space Environment ◽  
Physical Parameters ◽  
Optical Remote Sensing ◽  
Electronic Density ◽  
Radio Communications ◽  
Important Means ◽  
Far Ultraviolet ◽  
The Impact

&lt;p&gt;As a space-based optical remote sensing method, Far-ultraviolet Ionospheric Photometer with small size, low power consumption, high sensitivity is an important means to detect physical parameters of the ionosphere. Using the Far-ultraviolet Ionospheric Photometer to detect the intensity of ionospheric 135.6nm night airglow can obtain the ionospheric TEC, F2 layer peak electronic density(NmF2), which can be used to study the information on changes in ionospheric space environment,and the impact of the ionosphere on the radio communications, etc.; The ionospheric 135.6nm day airglow and the LBH radiation radiance can be used to obtain the ionospheric O / N2 ratio information, which can be used to study the space weather events and monitor the electromagnetic environment changes in the Earth's space. The FY3-D Ionospheric Photometer(IPM), launched on November 15, 2017, has a detection sensitivity which is greater than 150 counts / s / Rayleigh and a spatial field of view of 1.6 &amp;#215; 3.5 &amp;#176; with high horizontal spatial resolution that will help to achieve the fine detection of the ionosphere. This report will analyze the FY3-D IPM detection results.At the same time,the report will introduce our research team&amp;#8217;s work on the development and application of other payloads in the far ultraviolet band&lt;/p&gt;

Mapping surface soil moisture over wheat crops in southern Mediterranean regions using the backscattering coefficient and the interferometric coherence derived from Sentinel-1

2020 ◽  
Author(s):  
Nadia Ouaadi ◽  
Lionel Jarlan ◽  
Jamal Ezzahar ◽  
Saïd Khabba ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Surface Soil ◽  
Cloud Model ◽  
Microwave Remote Sensing ◽  
Radar Data ◽  
Optical Data ◽  
Surface Soil Moisture ◽  
Backscattering Coefficient ◽  
New Approach

&lt;p&gt;High spatial and temporal resolution products of Sentinel-1 are used for surface soil moisture (SSM) mapping over wheat fields in semi-arid areas. Within these regions, monitoring the water-use is a critical aspect for optimizing the management of the limited water resources via irrigation monitoring. SSM is one of the principal quantities affecting microwave remote sensing. This sensitivity has been exploited to estimate SSM from radar data, which has the advantages of providing data independent of illumination and weather conditions. In addition, with the use of Sentinel-1 products, the spatial and temporal resolution is greatly improved. Within this context, the main objective of this work is estimate SSM over wheat fields using an approach based on the use of C-band Sentinel-1 radar data only. Over the study site, field measurement are collected during 2016-2017 and 2017-2018 growing seasons over two fields of winter wheat with drip irrigation located in the Haouz plain in the center of Morocco. Data of other sites in Morocco and Tunisia are taken for validation purposes. The validation database contains a total number of 20 plots divided between irrigated and rainfed wheat plots. Two different information extracted from Sentinel-1 products are used: the backscattering coefficient and the interferometric coherence. A total number of 408 GRD and 419 SLC images were processed for computing the backscattering coefficient and the interferometric coherence, respectively. The analysis of Sentinel-1 time series over the study site show that coherence is sensitive to the development of wheat, while the backscatter coefficient is widely linked to changes in surface soil moisture. Later on, the Water Cloud Model coupled with the Oh et al, 1992 model were used for better understand the backscattering mechanism of wheat canopies. The coupled model is calibrated and validated over the study site and it proved to goodly enough reproduce the Sentinel-1 backscatter with RMSE ranging from 1.5 to 2.52 dB for VV and VH using biomass as a descriptor of wheat. On the other side, the analysis show that coherence is well correlated to biomass. Thus, the calibrated model is used in an inversion algorithm to retrieve SSM using the Sentinel-1 backscatter and coherence as inputs. The results of inversion show that the proposed new approach is able to retrieve the surface soil moisture at 35.2&amp;#176; for VV, with R=0.82, RMSE=0.05m&lt;sup&gt;3/&lt;/sup&gt;m&lt;sup&gt;3 &lt;/sup&gt;and no bias. Using the validation database of Morocco and Tunisia, R is always greater than 0.7 and RMSE and bias are less than 0.008 m&lt;sup&gt;3/&lt;/sup&gt;m&lt;sup&gt;3&lt;/sup&gt; and 0.03 m&lt;sup&gt;3/&lt;/sup&gt;m&lt;sup&gt;3&lt;/sup&gt;, respectively even that the incidence angle is higher (40&amp;#176;). In order to assess its quality, the approach is compared to four SSM retrieval methods that use radar and optical data in empirical and semi-empirical approaches. Results indicate that the proposed approach shows an improvement of SSM retrieval between 17% and 42% compared to other methods. Finally, the validated new approach is used for SSM mapping, with a spatial resolution of 10*10 m, over irrigated perimeters of wheat in Morocco.&lt;/p&gt;

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