Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358940
Narayanarao Bhogapurapu, D. Mandal, Y. S. Rao, A. Bhattacharya
Soil moisture retrieval over the vegetated soil surfaces using Synthetic Aperture Radar (SAR) data is a challenging issue. Presence of vegetation over soil surface makes the interaction of the radar signal with the soil more complex. Several studies used the Water Cloud Model (WCM) to separate vegetation effect on the soil backscatter while estimating the soil moisture. The general form of WCM utilizes one or two vegetation descriptors (e.g., Vegetation Water Content (VWC) and Leaf Area Index (LAI)) in determining the vegetation contribution. Eventually, these descriptors replaced by vegetation metric derived from ancillary sources (e.g., Normalized Difference Vegetation Index-NDVI). This ancillary data may not be available close to the date of SAR data acquisition due to several reasons. To circumvent these challenges, we use SAR derived vegetation descriptors in estimating soil moisture over wheat fields. We studied the performance of four different descriptors (viz., VWC, NDVI, cross-pol ratio (CPR), Dual-pol Radar Vegetation Index (DpRVI)) for estimating soil moisture using WCM. SAR derived vegetation descriptors for dual-pol data provided a reliable accuracy with a r value of 0.86 and RMSE of 5.9% (DpRVI-VV) as compared to NDVI. HH polarisation outperformed VV polarisation agreeing with the fact that vertically oriented crops less affects horizontally polarized signal.
利用合成孔径雷达(SAR)数据反演植被土壤表面的土壤水分是一个具有挑战性的问题。土壤表面植被的存在使得雷达信号与土壤的相互作用更加复杂。已有研究在估算土壤湿度时，利用水云模型分离植被对土壤后向散射的影响。WCM的一般形式是利用一个或两个植被描述符(如植被含水量(VWC)和叶面积指数(LAI))来确定植被的贡献。最终，这些描述符被来自辅助来源的植被度量所取代(例如，归一化植被指数- ndvi)。由于几个原因，这些辅助数据可能无法在接近SAR数据采集日期时获得。为了规避这些挑战，我们使用SAR衍生的植被描述符来估计麦田上的土壤湿度。我们研究了四种不同描述符(即VWC、NDVI、cross-pol ratio (CPR)、Dual-pol Radar Vegetation Index (DpRVI))在利用WCM估算土壤湿度方面的性能。与NDVI相比，SAR衍生的双pol植被描述符的r值为0.86,RMSE为5.9% (DpRVI-VV)，具有可靠的精度。HH偏振优于VV偏振，这与垂直方向的作物对水平极化信号的影响较小这一事实一致。
{"title":"Soil Moisture Estimation for Wheat Crop Using Dual-Pol L-Band SAR Data","authors":"Narayanarao Bhogapurapu, D. Mandal, Y. S. Rao, A. Bhattacharya","doi":"10.1109/InGARSS48198.2020.9358940","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358940","url":null,"abstract":"Soil moisture retrieval over the vegetated soil surfaces using Synthetic Aperture Radar (SAR) data is a challenging issue. Presence of vegetation over soil surface makes the interaction of the radar signal with the soil more complex. Several studies used the Water Cloud Model (WCM) to separate vegetation effect on the soil backscatter while estimating the soil moisture. The general form of WCM utilizes one or two vegetation descriptors (e.g., Vegetation Water Content (VWC) and Leaf Area Index (LAI)) in determining the vegetation contribution. Eventually, these descriptors replaced by vegetation metric derived from ancillary sources (e.g., Normalized Difference Vegetation Index-NDVI). This ancillary data may not be available close to the date of SAR data acquisition due to several reasons. To circumvent these challenges, we use SAR derived vegetation descriptors in estimating soil moisture over wheat fields. We studied the performance of four different descriptors (viz., VWC, NDVI, cross-pol ratio (CPR), Dual-pol Radar Vegetation Index (DpRVI)) for estimating soil moisture using WCM. SAR derived vegetation descriptors for dual-pol data provided a reliable accuracy with a r value of 0.86 and RMSE of 5.9% (DpRVI-VV) as compared to NDVI. HH polarisation outperformed VV polarisation agreeing with the fact that vertically oriented crops less affects horizontally polarized signal.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"37 1","pages":"33-36"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85700241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358938
Satendra Singh, Jaya Sreevalsan-Nair
Managing and processing large-scale point clouds are much needed for the exploration and contextual understanding of the data. Hence, we explore the use of a widely used big data analytics framework, Apache Spark, in distributed systems for large-scale point cloud processing. To effectively use Spark, we propose to use its integration with Cassandra for persistent storage, and to appropriately partition the point cloud across the nodes in the distributed system. We use this integrated framework for multiscale feature extraction and semantic classification using random forest classifier. We have shown the efficacy of our proposed application through our results in the DALES aerial LiDAR point cloud.
{"title":"A Distributed System for Multiscale Feature Extraction and Semantic Classification of Large-Scale Lidar Point Clouds","authors":"Satendra Singh, Jaya Sreevalsan-Nair","doi":"10.1109/InGARSS48198.2020.9358938","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358938","url":null,"abstract":"Managing and processing large-scale point clouds are much needed for the exploration and contextual understanding of the data. Hence, we explore the use of a widely used big data analytics framework, Apache Spark, in distributed systems for large-scale point cloud processing. To effectively use Spark, we propose to use its integration with Cassandra for persistent storage, and to appropriately partition the point cloud across the nodes in the distributed system. We use this integrated framework for multiscale feature extraction and semantic classification using random forest classifier. We have shown the efficacy of our proposed application through our results in the DALES aerial LiDAR point cloud.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"296 1","pages":"74-77"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84672747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358972
Amit Kumar, H. Maurya, Arundhati Ray Misra, Rajib Kumar Panigrahi
Scattering mechanism ambiguity has been a significant challenge in the field of model-based decomposition of polarimetric SAR data. Even after continuous reported advancements, still, it is not being concluded that problem have successfully been suppressed. To address this issue, the proposed method focuses on the analysis of specific complex urban and sloped mountainous bare land profiles that can rotate the polarization basis. The approach optimizes the coherency matrix by subtracting helix component prior to decomposition followed by the incorporation of unitary matrix rotations to decouple the energy between the orthogonal states of polarization by neutralizing T23 and T13, separately. Furthermore, instead of conventional branching condition, mean alpha angle had been utilized to discriminate between dominant surface and dihedral scattering area. Validation has been done using two different polarimetric datasets. Quantitative analysis shows the improved decomposition results through empowering the co-polarized powers in their respective underlying dominant scattering areas.
{"title":"An Improved Four-Component Model-Based Decomposition Scheme with Emphasis on Unitary Matrix Rotations","authors":"Amit Kumar, H. Maurya, Arundhati Ray Misra, Rajib Kumar Panigrahi","doi":"10.1109/InGARSS48198.2020.9358972","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358972","url":null,"abstract":"Scattering mechanism ambiguity has been a significant challenge in the field of model-based decomposition of polarimetric SAR data. Even after continuous reported advancements, still, it is not being concluded that problem have successfully been suppressed. To address this issue, the proposed method focuses on the analysis of specific complex urban and sloped mountainous bare land profiles that can rotate the polarization basis. The approach optimizes the coherency matrix by subtracting helix component prior to decomposition followed by the incorporation of unitary matrix rotations to decouple the energy between the orthogonal states of polarization by neutralizing T23 and T13, separately. Furthermore, instead of conventional branching condition, mean alpha angle had been utilized to discriminate between dominant surface and dihedral scattering area. Validation has been done using two different polarimetric datasets. Quantitative analysis shows the improved decomposition results through empowering the co-polarized powers in their respective underlying dominant scattering areas.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"1977 1","pages":"70-73"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90252379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358944
Ashish B. Itolikar, A. Joshi, S. Deshpande, M. Kurtadikar
Present paper consists of laboratory measurements of complex dielectric properties of bare/uncovered soil and soil covered with vegetation (dry and green grass) at C-band microwave frequency at 30° C. The soil sample was collected from Gwalior, Madhya Pradesh, India. The Von Hippel method is used to measure complex dielectric properties using an automated C-band microwave bench set-up. The least square fitting technique is used to calculate dielectric constant ε΄, dielectric loss ε΄΄ and errors in their measurements. From measured dielectric properties, emissivity and brightness temperature are estimated at different angles of incidence using Fresnel equations. The comparative study of complex dielectric properties of bare/uncovered soil and soil covered with vegetation (dry and green grass) is a unique effort. This study provides useful information for interpretation of microwave remote sensing data of soil moisture under vegetation cover (grass).
{"title":"Dielectric Response Due to Combine Effect of Soil and Vegetation Layer (Grass) at C-Band Microwave Frequency","authors":"Ashish B. Itolikar, A. Joshi, S. Deshpande, M. Kurtadikar","doi":"10.1109/InGARSS48198.2020.9358944","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358944","url":null,"abstract":"Present paper consists of laboratory measurements of complex dielectric properties of bare/uncovered soil and soil covered with vegetation (dry and green grass) at C-band microwave frequency at 30° C. The soil sample was collected from Gwalior, Madhya Pradesh, India. The Von Hippel method is used to measure complex dielectric properties using an automated C-band microwave bench set-up. The least square fitting technique is used to calculate dielectric constant ε΄, dielectric loss ε΄΄ and errors in their measurements. From measured dielectric properties, emissivity and brightness temperature are estimated at different angles of incidence using Fresnel equations. The comparative study of complex dielectric properties of bare/uncovered soil and soil covered with vegetation (dry and green grass) is a unique effort. This study provides useful information for interpretation of microwave remote sensing data of soil moisture under vegetation cover (grass).","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"79 4","pages":"9-12"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91478937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358935
A. V, E. Rajasekaran, G. Boulet
Evapotranspiration (ET) links the energy, water and carbon cycles from local to global scales. Several remote sensing (RS) based models, with varying complexity and underlying physical concepts have been developed. Some of these models estimate total ET and some can partition ET into its constituent components. This study aims to compare three multi-source ET models, Priestley-Taylor-Jet Propulsion Lab (PT-JPL), Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) and Surface Temperature Initiated Closure (STIC) for their performance in simulating total ET and its components over four sites. The PT-JPL, SPARSE (layer), SPARSE (patch) and STIC models exhibited RMSE of 89.68, 57.3, 61.67 and 96.43 W m–2 respectively for the four sites taken together at half hourly time scales. In addition to differences in total ET simulated by the models, there was a remarkable difference between them in simulating the E and T components too. This clearly suggests that care must be taken when using these models to simulate ET and its components.
蒸散作用(ET)将从地方到全球的能源、水和碳循环联系在一起。已经开发了几种基于遥感(RS)的模型，它们具有不同的复杂性和潜在的物理概念。其中一些模型估计总蒸散发，一些模型可以将蒸散发划分为其组成部分。本研究旨在比较Priestley-Taylor-Jet Propulsion Lab (PT-JPL)、Soil - Plant - Atmosphere and Remote Sensing Evapotranspiration (SPARSE)和Surface Temperature Initiated Closure (STIC)三种多源ET模型在模拟4个站点总ET及其组分方面的性能。在半小时时间尺度上，PT-JPL、SPARSE (layer)、SPARSE (patch)和STIC模型的RMSE分别为89.68、57.3、61.67和96.43 W m-2。除了模型模拟的总蒸散发存在差异外，模型模拟的E和T分量也存在显著差异。这清楚地表明，在使用这些模式模拟ET及其组成部分时必须小心。
{"title":"Comparison of Three Remote Sensing Based Multi-Source Evapotranspiration Models","authors":"A. V, E. Rajasekaran, G. Boulet","doi":"10.1109/InGARSS48198.2020.9358935","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358935","url":null,"abstract":"Evapotranspiration (ET) links the energy, water and carbon cycles from local to global scales. Several remote sensing (RS) based models, with varying complexity and underlying physical concepts have been developed. Some of these models estimate total ET and some can partition ET into its constituent components. This study aims to compare three multi-source ET models, Priestley-Taylor-Jet Propulsion Lab (PT-JPL), Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) and Surface Temperature Initiated Closure (STIC) for their performance in simulating total ET and its components over four sites. The PT-JPL, SPARSE (layer), SPARSE (patch) and STIC models exhibited RMSE of 89.68, 57.3, 61.67 and 96.43 W m–2 respectively for the four sites taken together at half hourly time scales. In addition to differences in total ET simulated by the models, there was a remarkable difference between them in simulating the E and T components too. This clearly suggests that care must be taken when using these models to simulate ET and its components.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"22 1","pages":"50-53"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75710823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358977
Morgan Simpson, A. Marino, G. Nagendra Prabhu, Deepayan Bhowmik, Srikanth Rupavatharam, A. Datta, A. Kleczkowski, J. A. R. Sujeetha, S. Maharaj
Water Hyacinth is an aquatic macrophyte and highly invasive species, indigenous to Amazonia, Brazil and tropical South America. It was first introduced to India in 1896 and has now become and environmental and social nuisance throughout the country in community ponds, freshwater lakes, irrigation channels, rivers and most other surface waterbodies. Considering the adverse impact the infesting weed has, a constant monitoring is needed to aid policy makers involved in remedial measures. Due to the synoptic coverage provided by satellite imaging and other remote sensing practices, it is convenient to find a solution using this type of data. This paper looks at the use of Synthetic Aperture Radar (SAR) Sentinel-1 to detect water hyacinth at an early stage of its life-cycle. While SAR has been used prominently to monitor wetlands, the technique is yet to be fully exploited for monitoring water hyacinth and we seek to fill this knowledge gap. We compare different change detection methodologies based on dual polarimetric data. We also demonstrate how Sentinel-1 can be used to monitor this type of aquatic weeds in our study areas, which is Vembanad Lake in Kuttanad, Kerala.
{"title":"Monitoring Water Hyacinth in Kuttanad, India Using Sentinel-1 Sar Data","authors":"Morgan Simpson, A. Marino, G. Nagendra Prabhu, Deepayan Bhowmik, Srikanth Rupavatharam, A. Datta, A. Kleczkowski, J. A. R. Sujeetha, S. Maharaj","doi":"10.1109/InGARSS48198.2020.9358977","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358977","url":null,"abstract":"Water Hyacinth is an aquatic macrophyte and highly invasive species, indigenous to Amazonia, Brazil and tropical South America. It was first introduced to India in 1896 and has now become and environmental and social nuisance throughout the country in community ponds, freshwater lakes, irrigation channels, rivers and most other surface waterbodies. Considering the adverse impact the infesting weed has, a constant monitoring is needed to aid policy makers involved in remedial measures. Due to the synoptic coverage provided by satellite imaging and other remote sensing practices, it is convenient to find a solution using this type of data. This paper looks at the use of Synthetic Aperture Radar (SAR) Sentinel-1 to detect water hyacinth at an early stage of its life-cycle. While SAR has been used prominently to monitor wetlands, the technique is yet to be fully exploited for monitoring water hyacinth and we seek to fill this knowledge gap. We compare different change detection methodologies based on dual polarimetric data. We also demonstrate how Sentinel-1 can be used to monitor this type of aquatic weeds in our study areas, which is Vembanad Lake in Kuttanad, Kerala.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"22 1","pages":"13-16"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74448149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358926
S. Chaudhri, N. S. Rajput, K. Singh
Remote sensing is the analytics of sensor data modalities to capture the earth's surface characteristics. The hyperspectral data widely used for surface material identification by using pixel-wise unique signature patterns. The true-color-composite (RGB) or/and a variety of false-color-composites (FCCs) used to classify various objects and features. In this paper, three novel FCCs have been proposed and compared with already existing popular FCCs. These FCCs have been analyzed using three different approaches viz., (i) k-means (ii) patch-based deep network and (iii) sample level mirror mosaicking (SLMM)-based deep network; for the classification of various objects or features viz., Vegetation, Soil, and Road. The open-source dataset provided by the National Ecological Observatory Network (NEON) has been used to show the efficacy of proposed FCCs and SLMM-based deep-network. Our proposed FCCs and SLMM-based deep networks outperform over all other considered FCCs and classification methods.
{"title":"The Novel Camouflaged False Color Composites for the Vegetation Verified by Novel Sample Level Mirror Mosaicking Based Convolutional Neural Network","authors":"S. Chaudhri, N. S. Rajput, K. Singh","doi":"10.1109/InGARSS48198.2020.9358926","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358926","url":null,"abstract":"Remote sensing is the analytics of sensor data modalities to capture the earth's surface characteristics. The hyperspectral data widely used for surface material identification by using pixel-wise unique signature patterns. The true-color-composite (RGB) or/and a variety of false-color-composites (FCCs) used to classify various objects and features. In this paper, three novel FCCs have been proposed and compared with already existing popular FCCs. These FCCs have been analyzed using three different approaches viz., (i) k-means (ii) patch-based deep network and (iii) sample level mirror mosaicking (SLMM)-based deep network; for the classification of various objects or features viz., Vegetation, Soil, and Road. The open-source dataset provided by the National Ecological Observatory Network (NEON) has been used to show the efficacy of proposed FCCs and SLMM-based deep-network. Our proposed FCCs and SLMM-based deep networks outperform over all other considered FCCs and classification methods.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"7 1","pages":"237-240"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90148345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/ingarss48198.2020.9358968
{"title":"InGARSS 2020 Title Page","authors":"","doi":"10.1109/ingarss48198.2020.9358968","DOIUrl":"https://doi.org/10.1109/ingarss48198.2020.9358968","url":null,"abstract":"","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"235 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87535987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358936
Kukku Sara, E. Rajasekaran
Land Surface Temperature (LST) and its diurnal variation are important parameters for several applications. Thermal sensors in polar orbiting and geostationary orbiting satellites can provide LST data at high spatial and temporal resolutions respectively. This study aims to generate high spatiotemporal LST by combining the coarse resolution geostationary satellite data (INSAT 3D) with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product using spatial disaggregation (DisTrad model) and spatiotemporal fusion (STITFM model) techniques. In addition, the ability of these two methods to properly represent the diurnal temperature cycle (DTC) is also examined. It was found that the spatial disaggregation method provided relatively better results than spatiotemporal fusion technique in improving the spatiotemporal resolution of LST.
{"title":"Improving the Spatiotemporal Resolution of Land Surface Temperature Data Using Disaggregation and Fusion Techniques: A Comparison","authors":"Kukku Sara, E. Rajasekaran","doi":"10.1109/InGARSS48198.2020.9358936","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358936","url":null,"abstract":"Land Surface Temperature (LST) and its diurnal variation are important parameters for several applications. Thermal sensors in polar orbiting and geostationary orbiting satellites can provide LST data at high spatial and temporal resolutions respectively. This study aims to generate high spatiotemporal LST by combining the coarse resolution geostationary satellite data (INSAT 3D) with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product using spatial disaggregation (DisTrad model) and spatiotemporal fusion (STITFM model) techniques. In addition, the ability of these two methods to properly represent the diurnal temperature cycle (DTC) is also examined. It was found that the spatial disaggregation method provided relatively better results than spatiotemporal fusion technique in improving the spatiotemporal resolution of LST.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"25 1","pages":"46-49"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81538147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/ingarss48198.2020.9358923
{"title":"InGARSS 2020 Table of Contents","authors":"","doi":"10.1109/ingarss48198.2020.9358923","DOIUrl":"https://doi.org/10.1109/ingarss48198.2020.9358923","url":null,"abstract":"","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87109964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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