Interrelationship Analysis of L-Band Backscattering Intensity and Soil Dielectric Constant for Soil Moisture Retrieval Using PALSAR Data

The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of the dielectric constant of arid soils in the 0.3 - 3 GHz frequency range, particularly focused in L-band was analyzed in varied soil moisture content and soil textures. The interrelationship between the relative dielectric constant with soil textures and backscattering of PALSAR data was also analyzed and statistical model was computed. In this study, after collecting the soil samples in the field from top soil (0 - 10 cm) in a homogeneous area then, the dielectric constant was measured using a dielectric probe tool kit. For investigated of the characteristics and behaviors of the dielectric constant and relationship with backscattering a variety of moisture content from 0% to 40% and soil fraction conditions was tested in laboratory condition. All data were analyzed by integrating it with other geophysical data in GIS, such as land cover and soil texture. Thus, the regression model computed between measured soil moisture and backscattering coefficient of PALSR data which were extracted as same point of each soil sample pixel. Finally, after completing the preprocessing, such as removing the speckle noise by averaging, the model was applied to the PALSAR data for retrieving the soil moisture map in arid region of Iran. The analysis of dielectric constant properties result has shown the soil texture after the moisture content has the largest effected on dielectric constant. In addition, the PALSAR data in dual polarization are also able to derive the soil moisture using statistical method. The dielectric constant and backscattering shown have the exponential relationship and the HV polarization mode is more sensitive than the HH mode to soil moisture and overestimated the soil moisture as well. The validation of result has shown the 4.2 Vol-% RMSE of soil moisture. It means that the backscattering analysis should consider about other factors such a surface roughness and mix pixel of vegetation effective.

Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM

A regional map of mangrove forests was produced for six islands located in the southern part of Japan by integrating the spectral analyses of Landsat Enhanced Thematic Mapper plus (ETM+) images with a digital elevation model (DEM). Several attempts were applied to propose a reliable method, which can be used to map the distribution of mangrove forests at a regional scale. The methodology used in this study comprised of obtaining the difference between Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), band ratio 5/4, and band 5, from Landsat ETM+, and integrating them with the topographic information. The integration of spectral analyses with topographic data has clearly separated the mangrove forests from other vegetation. An accuracy assessment was carried out in order to check the accuracy of the results. High overall accuracy ranging from 89.3% to 93.6% was achieved, which increased the opportunity to use this methodology in other countries rich in mangrove forests.

Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement

The purpose of this paper is to simulate the backscattered signal by experimental data and field working then, comparing with the backscattered signal from actual L-band SAR data over arid to semi-arid environments. The experimental data included the laboratory-measured dielectric constant of soil samples and the roughness parameter. A backscattering model used to simulate the backscattering coefficient in sparse vegetation land cover. The backscattering coefficient (σ0) simulated using the AIEM (advanced integral equation model) based on the experimental data. The roughness data were considered by the field observation, chain method measuring and photogrammetry simulation technique by stereo image of ground real photography. The simulated backscattering coefficients were compared with the real extracted backscattering coefficient (σ0) from the ALOS PALSAR single and dual polarization mode data. The most problem in backscattering simulation was the vegetation water content. Therefore, the water-cloud model using the water index result of optical data applied on the simulated backscatter model for enhancement the backscattering heterogeneity from vegetation water contents due to the mix pixel of vegetation in spars vegetation. At the results the AIEM model overestimated the backscattering simulation, it might be cause of high sensitivity of this model to roughness. The ALOS PALSAR HV polarization mode is more sensitive than the HH mode to vegetation water content. The water-cloud model could improve the result and the correlation function of the samples was increased but, the difficulties were the input the A and B parameters to model.

A method for canopy water content estimation for highly vegetated surfaces-shortwave infrared perpendicular water stress index

In this paper, a new method for canopy water content (FMC) estimation for highly vegetated surfaces- shortwave infrared perpendicular water stress index (SPSI) is developed using NIR, SWIR wavelengths of Enhanced Thematic Mapper Plus (ETM+) on the basis of spectral features and distribution of surface targets with different water conditions in NIR-SWIR spectral space. The developed method is further explored with radiative transfer simulations using PROSPECT, Lillesaeter, SailH and 6S. It is evident from the results of validation derived from satellite synchronous field measurements that SPSI is highly correlated with FMC, coefficient of determination (R squared) and root mean square error are 0.79 and 26.41%. The paper concludes that SPSI has a potential in vegetation water content estimation in terms of FMC.

全覆盖植被冠层水分遥感监测的一种方法:短波红外垂直失水指数

利用叶片辐射传输模型PROSPECT、植被冠层辐射传输模型SailH和地气辐射传输模型6S,进一步探索近红外、短波红外反射光谱特征,从光谱特征空间的角度,分析地物在NIR-SWIR空间的分异规律,建立监测植被冠层水分含量的新方法-短波红外垂直失水指数(SPSI).通过实地观测数据和叶片、冠层辐射传输模型验证本文提出的新方法,结果表明SPSI和实地观测的植被冠层水分含量(FMC)具有较高的相关性,R2和RMSE分别为0.79,26.41%,证明了SPSI在FMC反演方面有一定的应用潜力.

Production of global land cover data-GLCNMO

Global land cover is one of the fundamental contents of Digital Earth.The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset
Global Land Cover by National Mapping Organizations.It has 20 land cover classes defined using the Land Cover Classification System.Of them,14 classes were derived using supervised classification.The remaining six were classified independently:urban,tree open,mangrove,wetland,snow/ice,andwater.Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003.Training data for supervised classification were collected using Landsat images,MODIS NDVI seasonal change patterns,Google Earth,Virtual Earth,existing regional maps,and expert’s comments.The overall accuracy is 76.5%and the overall accuracy with the weight of the mapped area coverage is 81.2%.The data are available from the Global Mapping project website(http://www.iscgm.org/).TheMODISdata used,land cover training data,and a list of existing regional maps are also available from the CEReS website.This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.

Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data

An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study.The Moderate Resolution Imaging Spectroradiometer(MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite(VIIRS)-based nighttime light(NTL)data for robust extraction and mapping of urban built-up areas.The MODIS-based newly proposed Urban Built-up Index(UBI)was combined with NTL data,and the resulting Enhanced UBI(EUBI)was used as a single master image for global extraction of urban built-up areas.Due to higher variation of the EUBI with respect to geographical regions,a region-specific threshold approach was used to extract urban built-up areas.This research provided 500-m-resolution global urban built-up map of year 2014.The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States.The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.

New urban map of Eurasia using MODIS and multi-source geospatial data

Urban areas are of paramount significance to both the individuals and communities at local and regional scales.However,the rapid growth of urban areas exerts effects on climate,biodiversity,hydrology,and natural ecosystems worldwide.Therefore,regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local,regional,and potentially global scales.This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data,including Moderate Resolution Imaging Spectroradiometer(MODIS)data of 2013,population density of 2012,the Defense Meteorological Satellite Program’s Operational Linescan System(DMSP-OLS)nighttime lights of 2012,and constructed Impervious Surface Area(ISA)data of 2010.The Eurasian urban map was created using the threshold method for these data,combined with references of fine resolution Landsat and Google Earth imagery.The resultant map was compared with nine global urban maps and was validated using random sampling method.Results of the accuracy assessment showed high overall accuracy of the new urban map of 94%.This urban map is one product of the 20 land cover classes of the next version of Global Land Cover by National Mapping Organizations.

Generating soil salinity, soil moisture, soil pH from satellite imagery and its analysis

In an agricultural field,the water content and salt content are defined as soil moisture and soil salinity and have to be estimated precisely.The changing of these two factors can be assessed using remote sensing technology.This study was conducted by analysing the Landsat 8 satellite images,soil data of field surveys,laboratory analyses and statistical computations.Soil properties such as soil moisture and soil salinity were estimated using soil moisture index(SMI)and soil salinity index(SSI),respectively.The research combined and integrated the soil data from survey and laboratory with Landsat 8 satellite images to build two multiple regression equations model named the soil pH Index(SpHI).They are based on bare soil and paddy leaf models as the explanatory factors of soil moisture and soil salinity changes.All the computation processes were replicated three times using three different dates of Landsat 8 satellite images to produce the multi-temporal analysis.Soil moisture increased after 30 days,while the salt content was only trace amounts.Both proposed models detected 4.49–7.59 of soil pH,4.66 in bare soil model and 6.62 in paddy leaf model.During the planting period,the soil pH in bare soil model decreased to 2.12–6.47 while the paddy leaf model increased to 4.49–7.59 with RMSE 1.40 and PRMSE 24%of accuracy.The spatial relationship between soil pH,soil salinity and soil moisture are linear but varied in correlation level from weak,moderate to strong.Based on the bare soil model,the relationship between soil pH and soil moisture shows a weak negative relationship with R28.37%and a strong positive relationship with R281.94%in paddy area and bare soil area respectively,as like as in paddy area based on the paddy leaf model with R2100%.The relationship between soil temperature and soil pH shows a weak negative relationship for all models and a moderate negative relationship of soil salinity and soil pH in bare soil area based on the bare soil model with R234.89%.