The synergistic use of Sentinel SAR and optical remote sensing for mapping high-resolution soil moisture

2020 ◽  
Author(s):  
Jianxiu Qiu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Large Scale ◽  
Vegetation Index ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Biological Variables ◽  
Research Activities

<p>The launch of series of Sentinel constellations has provided data continuity of ERS, Envisat, and SPOT-like observations, in order to meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities. The synergistic use of this publicly-accessible SAR images and temporally collocated optical remote sensing datasets has provided great potential for estimating high-resolution soil moisture information. In this study, advanced integral equation model (AIEM) which simulates the backscattering coefficient of bare soil and the Water-Cloud Model (WCM) accounting for the scattering effect from vegetation, are coupled to map high-resolution soil moisture. Validation conducted in large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research (HiWATER-MUSOEXE) in northwest of China showed RMSE of 0.04~0.071 m3m3. In addition, the accuracies in describing vegetation contribution from backscatter coefficient were intercompared between different models including WCM and ratio vegetation model. Sensitivity analysis of soil moisture estimation accuracy to vegetation index also extends to different optical remote sensing data sets including Sentinel-2, Landsat 8 and MODIS.</p>

Simulation of streamflow in two Mediterranean catchments using a process-based model and remote sensing products

2020 ◽  
Author(s):  
Tiago Ramos ◽  
Lucian Simionesei ◽  
Marta Basso ◽  
Vivien Stefan ◽  
Ana Oliveira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Catchment Area ◽  
Vegetation Index ◽  
Crop Growth ◽  
Assessment Tools ◽  
Landsat 8 ◽  
Network Computing ◽  
Area Index ◽  
Model Simulations

<p>Watershed modelling is one of the most important assessment tools in watershed planning and management. Nonetheless, the classic calibration of watershed models, in which a few discharge gauges near the outlet of a catchment are used to compare measured and simulated streamflow, is often criticized by not assuring that relevant processes such as evapotranspiration, soil moisture, crop growth, and groundwater recharge are well represented in the catchment area. This study aimed to simulate streamflow in two Mediterranean catchments, Orba (778km<sup>2</sup>) in Italy and Segre (1286km<sup>2</sup>) in Spain, using the physically-based, fully distributed MOHID-Land model. Model calibration/validation of streamflow was first performed following a classical approach. Different products derived from remote sensing platforms were then used to evaluate the adequacy of model simulations of crop growth and soil moisture in the catchment area.</p><p>The MOHID-Land model considers four compartments or mediums (atmosphere, porous media, soil surface and river network), computing water dynamics through the different mediums using mass and momentum conservation equations. The model was implemented in the two simulated catchments with a resolution of 1 km. Data inputs included the Digital Elevation Model over Europe (EU-DEM) with a resolution of 30 m; the soil hydraulic properties map from EU-SoilHydroGrids ver1.0 with a resolution of 250 m; the CORINE land cover map from 2012 with a resolution of 100 m; the hourly weather data (precipitation, wind velocity, relative air humidity, solar radiation and surface air temperature) from local weather stations; and the reservoir discharge data from governmental and/or regional agencies. Simulations were run from 2006-2014 for Orba and from 2008-2018 for Segre, and included a model warm-up, a calibration, and a validation period. Comparison between simulated and measured flows were performed in 2 and 10 hydrometric stations located in the Orba and Segre catchments, respectively. Four statistical parameters (R<sup>2</sup>, RMSE, PBIAS and NSE) were used to evaluate model performance, confirming the good fitting of model simulations to measured data.</p><p>Model simulations of leaf area index (LAI) were then compared with LAI maps at 30 m resolution derived from ATCOR and Landsat 8 imagery data using the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). Furthermore, model simulation of soil moisture were also compared at the surface depth (0-5 cm) with soil moisture maps at 1 km resolution created with the DISaggregation based on a Physical And Theoretical scale CHange (DISPATCH) algorithm for the downscaling of the 40 km SMOS (Soil Moisture and Ocean Salinity) soil moisture data using land surface temperature (LST) and NDVI data. Results showed the fundamental differences between the MOHID-Land and remote sensing outputs, with major differences being analyzed by soil units and land use classes.</p>

scholarly journals Pemetaan Wilayah Lahan Basah Berdasarkan Data Penginderaan Jauh Optik Dan Radar

2020 ◽  
Vol 4 (2) ◽  
pp. 48-61
Author(s):  
Rian Nurtyawan ◽  
Ervan Muktamar Hendarna
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Satellite Image ◽  
Soil Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Radar Sensing

ABSTRAKPada umumnya lahan basah dikelola menjadi area pertanian ataupun perkebunan. Fungsi lahan basah memiliki fungsi ekologis seperti pengendali banjir, pencegah intrusi air laut, erosi, pencemaran, dan pengendali iklim global. Data pengindraan jauh yang digunakan pengelolaan lahan basah yaitu pengindraan jauh optik dan radar. Tujuan dari penelitian ini adalah mengeksplorasi korelasi potensial dari data optik dan radar untuk mengamati dinamika pada kawasan lahan basah tersebut dan melakukan pemetaan. Metode yang digunakan pada pengindraan jauh optik yaitu LST (Land Surface Temperature) berdasarkan Citra Satelit Landsat-8 dan metode yang digunakan pada pengindraan jauh radar yaitu estimasi kelembaban tanah berdasarkan Citra Satelit Sentinel-1A. Hasil pengamatan dinamika dan pemetaan pada wilayah Kabupaten Bandung Raya memiliki nilai kelembaban tanah tertinggi pada Bulan Mei dengan nilai kelembapan tanah tanah rata-rata sebesar 20,9 % pada polarisasi VH. Suhu permukaan tanah terendah terjadi pada bulan Mei dengan nilai suhu rata-rata sebesar 19.5 °C. Kolerasi antara nilai kelembapan tanah tanah dan suhu permukaan tanah pada wilayah Kabupaten Bandung Raya berdasarkan metode koefisien determinasi sebesar R2=0.705 didapatkan bahwa semakin tinggi nilai kelembapan tanah tanah maka nilai suhu permukaan tanah akan semakin rendah.Kata kunci: Kawasan lahan basah, Pengindraan Jauh Optik, Pengindraan Jauh Radar, Pengamatan Dinamika, Pemetaan. ABSTRACTIn general wetlands managed become an area of agriculture or plantations. The extent of wetland that has been used can be damaged if it is not managed properly and integrated.. The purpose of this research is to explore the potential correlations between several parameters of optical and radar data to observe the dynamics of wetlands area and mapping the wetlands area. The methodology that was used in optical remote sensing is LST (Land Surface Temperature) based on Landsat-8 Satellite Image and the method used in remote radar sensing is estimation of soil moisture based on Sentinel-1A Satellite Image. The result of the observation in the area and mapping the dynamics in Bandung Raya District had the highest soil moisture values in May with 27% of soil water level in VH polarization and 78.1% in VV polarization and the lowest value in each month is 11.8% and the highest soil surface temperature in August with a value 37.9 ° C and the minimum value 19 ° C..Keywords: Wetland Area, Optical Remote Sensing, Remote Radar Sensing, Dynamics Observation, Mapping.

scholarly journals Fusion of High- and Medium-Resolution Optical Remote Sensing Imagery and GlobeLand30 Products for the Automated Detection of Intra-Urban Surface Water

2020 ◽  
Vol 12 (24) ◽  
pp. 4037
Author(s):  
Zhi Li ◽  
Xiaomei Yang
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Automated Detection ◽  
Urban Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Model Based

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance.

scholarly journals APPLICATION OF MULTI-SOURCE REMOTE SENSING IMAGE IN YUNNAN PROVINCE GRASSLAND RESOURCES INVESTIGATION

2018 ◽  
Vol XLII-3 ◽  
pp. 837-841
Author(s):  
J. Li ◽  
G. Wen ◽  
D. Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Yunnan Province ◽  
Remote Sensing Image ◽  
Field Investigation ◽  
Background Information ◽  
Landsat 8 ◽  
Yield Model ◽  
Wide Range

Trough mastering background information of Yunnan province grassland resources utilization and ecological conditions to improves grassland elaborating management capacity, it carried out grassland resource investigation work by Yunnan province agriculture department in 2017. The traditional grassland resource investigation method is ground based investigation, which is time-consuming and inefficient, especially not suitable for large scale and hard-to-reach areas. While remote sensing is low cost, wide range and efficient, which can reflect grassland resources present situation objectively. It has become indispensable grassland monitoring technology and data sources and it has got more and more recognition and application in grassland resources monitoring research. This paper researches application of multi-source remote sensing image in Yunnan province grassland resources investigation. First of all, it extracts grassland resources thematic information and conducts field investigation through BJ-2 high space resolution image segmentation. Secondly, it classifies grassland types and evaluates grassland degradation degree through high resolution characteristics of Landsat 8 image. Thirdly, it obtained grass yield model and quality classification through high resolution and wide scanning width characteristics of MODIS images and sample investigate data. Finally, it performs grassland field qualitative analysis through UAV remote sensing image. According to project area implementation, it proves that multi-source remote sensing data can be applied to the grassland resources investigation in Yunnan province and it is indispensable method.

LANDSAT TM-8 DATA FOR RETRIEVING SALINITY IN AL-HUWAIZAH MARSH, SOUTH OF IRAQ

2015 ◽  
Vol 75 (1) ◽  
Author(s):  
Hashim Ali Hasab ◽  
Anuar Ahmad ◽  
Maged Marghany ◽  
Abdul Razzak Ziboon
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Hydroelectric Power ◽  
South Of Iraq ◽  
Western Eurasia ◽  
Southern Iraq

Mesopotamia marshlands constitute the largest wetland ecosystem in the Middle East and western Eurasia. These marshlands are located at the confluence of Tigris and Euphrates rivers in southern Iraq. Al-Huwaizah marsh is the biggest marsh in southern Iraq covered by an area (2400 Km2-3000 Km2) and depth (1.5 m-5 m). The construction dams by Turkey and Syrian for water storage as well as hydroelectric power generation along Tigris and Euphrates rivers, led to reduce and deteriorate water quality in Iraq's marshes. Salinity has become one of the major problems affecting crop production and food security in central and southern Iraq. The objective of this study to develop a new algorithm to retrieve salinity and normalized difference vegetation index (NDVI) from optical remote sensing Landsat-8 (OLI/TIRS) data based on differential equations algorithms. The mathematical algorithms are linear, power and exponential algorithm. The integration between remote sensing techniques and geographic information system (GIS) to map hydrodynamic and the spatial variation of salinity distribution. There is a pressing need to quantify and map the spatial extent and distribution of the salinity in Al-Huwaizah marsh of southern Iraq during March-2013. The findings of this study proved that the integration between Landsat-8 data and GIS with salinity algorithms could provide a powerful tool for retrieving salinity in marshes zone.

scholarly journals A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture

2019 ◽  
Vol 2 ◽  
Author(s):  
Ebrahim Babaeian ◽  
Paheding Sidike ◽  
Maria S. Newcomb ◽  
Maitiniyazi Maimaitijiang ◽  
Scott A. White ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Optical Remote Sensing ◽  
Remote Sensing Technique ◽  
High Resolution Mapping ◽  
Resolution Mapping

scholarly journals Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

2021 ◽  
Vol 11 (3) ◽  
pp. 2101
Author(s):  
Noraisyah Tajudin ◽  
Norsuzila Ya'acob ◽  
Darmawaty Mohd Ali ◽  
Nor Aizam Adnan
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Northeast Monsoon ◽  
Contributing Factors ◽  
Moisture Index ◽  
Rainfall Distribution ◽  
Rainfall Analysis

Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events due to the increase in pore pressure which eventually reduces the soil strength. For landslide prediction and monitoring purposes, large-scale measurement involves estimating the soil moisture. However, estimation of soil moisture usually involves point-based measurements at a particular site and time, which is difficult to capture the spatial and temporal soil moisture dynamics. This paper presents the estimation of the SMI using Landsat 8 images for prediction and monitoring of landslide events in Ulu Kelang, Selangor. The selected SMI map for dry, moist, and wet seasons are obtained from climatology rainfall analysis over 20-year periods (1998-2017). SMI is assessed based on remote sensing data which are land surface temperature (LST) and normalized difference vegetation index (NDVI) using GIS software. Overall results indicated that rainfall distribution is high during inter-monsoon (IM), followed by northeast monsoon (NEM) and southwest monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. Results from simulation show that April 2017 is known to have the highest SMI estimation season and selected to be the best SMI mapping parameter to be applied for prediction and monitoring of landslide events.

Monitoring Hydroperiod and Hydropatterns of coastal wetland systems in Mexico using Landsat time series

2020 ◽  
Author(s):  
Bart (A.J.) Wickel ◽  
Rene Colditz ◽  
Rainer Ressl ◽  
John Kucharski ◽  
Sergio Salinas-Rodríguez
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Full Range ◽  
Water Bodies ◽  
Bird Conservation ◽  
Optical Remote Sensing ◽  
Data Set ◽  
The Pacific

<p>The main objective of this study was the evaluation of remote sensing methods that allow for extraction of metrics that link riparian flow regimes to hydro-periods (duration) and -patterns (extent) of wetland systems known to be of critical importance to migratory water fowl and shorebirds along the Pacific Flyway in Mexico. In this study we emphasized the use of freely available and easily accessible optical remote-sensing data and their processing using free and open-source tools. </p><p>Through application of a set of common and well documented water and vegetation indices on the full Landsat 5 and Landsat 7 record spanning two decades, we created a data set that captures episodic, intra-annual and inter-annual variability in inundation for two contrasting wetland systems. For this study we focussed on the Marismas Nacionales wetland system along the Pacific coast and the Alvarado Lagoon system on the Gulf coast. A comparison of indices designed to extract vegetation and water characteristics from Landsat data (NDVI, EVI, NDWI, Tasseled Cap and MNDWI) led us to conclude that the Modified Normalized Difference Water Index (MNDWI) was most effective for identifying inundated areas while the Normalized Difference Vegetation Index (NDVI) worked best for identifying differences in vegetated areas. Our study also established that the high sensitivity to thresholds requires site specific optimization.</p><p>For the study we developed metrics to represent the hydro-pattern and hydro-periodicity of waterbodies in the study areas. The first method provides a metric for the intra-annual and inter-annual <em>permanence</em> of water bodies, while the second method quantifies <em>recurrence</em> of seasonal inundation. The Marismas Nacionales revealed a surprisingly strong and direct relationship between inundated area and gauge meassured discharge of the Rio San Pedro Mezquital. Annual and multi annual hydropatterns in this system are very strong and predictable, and primarily driven by large scale inundation of the delta of this river as it enters Marismas Nacionales. The relationship between discharge and inundated area was so string that the inundated area (up to several hundreds of sqare kilometers during peaks) remained correlated throught the full range of the hydrograph. For this system recurrent inundation patterns and their timing metrics were linked to specific ecosystem types and used to inform a bird conservation planning effort.</p><p>At the Laguna de Alvarado a very different dynamic was observed, where large scale inundation was less frequent, permanent water bodies were much more persistent in space, and the correlation between inundated area and discharge was much weaker. In this region persistent cloud cover was an issue and SAR based approached may be the only way to monitor inundation dynamics more consistently. Earlier studies by WIckel et al for other systems using PALSAR data for wetland systems in Colombia revealed other technical shortcomings of these kinds of data. A study by Colditz et al for wetland systems in Mexico revealed a strong potential of MODIS derived MNDWI data as well. We propose that future efforts explore the possibilties of aplications of combined (optical and SAR) products.</p>

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