Introduction to Flexible Spatiotemporal

What is/are Flexible Spatiotemporal?

Flexible Spatiotemporal - The performances of VDSR are analyzed in comparison with those of two other classical methods, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method. ^[1] To address this problem, this study compared six typical spatiotemporal fusion methods, including the Unmixing-Based Data Fusion (UBDF), Linear Mixing Growth Model (LMGM), Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), One Pair Dictionary-Learning method (OPDL), and Flexible Spatiotemporal DAta Fusion (FSDAF), based on simulation experiments and theoretical analysis considering three influential factors between sensors: geometric misregistration, radiometric inconsistency, and spatial resolution ratio. ^[2] To overcome this limitation, we employed an improved flexible spatiotemporal data fusion (IFSDAF) model to conduct data fusion using MODIS and Landsat imagery and extract NDVI time series with both high spatial and temporal resolution. ^[3] Finally, the image with spatial information is introduced into the Flexible Spatiotemporal DAta Fusion (FSDAF) framework to improve the performance of spatiotemporal image-fusion. ^[4] They include the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), SaTellite dAta IntegRation (STAIR), and the CubeSat Enabled Spatio-Temporal Enhancement Method (CESTEM); the former three blended Landsat and MODIS data, whereas the latter combined CubeSats, Landsat, and MODIS observations. ^[5] In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. ^[6] , spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. ^[7] , enhanced deep convolutional spatiotemporal fusion network (EDCSTFN), super resolution convolutional neural network (SRCNN), spatial and temporal adaptive reflectance fusion model (STARFM), enhanced STARFM (ESTARFM), and flexible spatiotemporal data fusion (FSDAF)]. ^[8] In this study, we presented an improved Flexible Spatiotemporal Data Fusion (FSDAF) strategy with consideration of highly dynamic SPM variations in estuarine waters, and generated 30-m hourly SPM concentrations based on Landsat 8 OLI and GOCI datasets. ^[9] Comparison experiments were conducted by simulating ideal and challenging conditions of input time-series data in two characteristic areas, and the proposed methods were also compared with four typical methods: The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), and Improved FSDAF. ^[10] Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0. ^[11] Through the applied methods, we used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) to blend L8 and S2 data and obtain reliable normalized difference vegetation index (NDVI) datasets with fine spatial and temporal resolution. ^[12] Using spectral unmixing analysis and spatial interpolation, the flexible spatiotemporal data fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-cover changes. ^[13] Because of the complex interactions and the abundant dynamics among neural circuits, good decoding algorithms usually have the capability of capturing flexible spatiotemporal structures embedded in the input feature space. ^[14] The models included the spatial and temporal adaptive reflectance model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion model (FSDAF), and a spatiotemporal vegetation index image fusion model (STVIFM). ^[15] In this study, we modified the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. ^[16] The performances of SEVIS are analyzed in comparison with those of two other STF methods, the classical Spatial and Temporal Adaptive Fusion Model (STARFM) and the more recent Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm. ^[17] The Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method was developed in this study to fill this need. ^[18] The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. ^[19] The Flexible Spatiotemporal Data Fusion (FSDAF) model was then used to create two fused, high-resolution time-series products (fused MODIS and fused reconstructed MODIS) in order to enhance the spatial and temporal effectiveness of satellite images for field-scale applications. ^[20]

temporal adaptive reflectance

The performances of VDSR are analyzed in comparison with those of two other classical methods, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method. ^[1] To address this problem, this study compared six typical spatiotemporal fusion methods, including the Unmixing-Based Data Fusion (UBDF), Linear Mixing Growth Model (LMGM), Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), One Pair Dictionary-Learning method (OPDL), and Flexible Spatiotemporal DAta Fusion (FSDAF), based on simulation experiments and theoretical analysis considering three influential factors between sensors: geometric misregistration, radiometric inconsistency, and spatial resolution ratio. ^[2] They include the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), SaTellite dAta IntegRation (STAIR), and the CubeSat Enabled Spatio-Temporal Enhancement Method (CESTEM); the former three blended Landsat and MODIS data, whereas the latter combined CubeSats, Landsat, and MODIS observations. ^[3] , spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. ^[4] , enhanced deep convolutional spatiotemporal fusion network (EDCSTFN), super resolution convolutional neural network (SRCNN), spatial and temporal adaptive reflectance fusion model (STARFM), enhanced STARFM (ESTARFM), and flexible spatiotemporal data fusion (FSDAF)]. ^[5] Comparison experiments were conducted by simulating ideal and challenging conditions of input time-series data in two characteristic areas, and the proposed methods were also compared with four typical methods: The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), and Improved FSDAF. ^[6] Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0. ^[7] Through the applied methods, we used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) to blend L8 and S2 data and obtain reliable normalized difference vegetation index (NDVI) datasets with fine spatial and temporal resolution. ^[8] The models included the spatial and temporal adaptive reflectance model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion model (FSDAF), and a spatiotemporal vegetation index image fusion model (STVIFM). ^[9] In this study, we modified the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. ^[10] The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. ^[11]

Improved Flexible Spatiotemporal

To overcome this limitation, we employed an improved flexible spatiotemporal data fusion (IFSDAF) model to conduct data fusion using MODIS and Landsat imagery and extract NDVI time series with both high spatial and temporal resolution. ^[1] In this study, we presented an improved Flexible Spatiotemporal Data Fusion (FSDAF) strategy with consideration of highly dynamic SPM variations in estuarine waters, and generated 30-m hourly SPM concentrations based on Landsat 8 OLI and GOCI datasets. ^[2] The Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method was developed in this study to fill this need. ^[3]

flexible spatiotemporal datum

The performances of VDSR are analyzed in comparison with those of two other classical methods, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method. ^[1] To address this problem, this study compared six typical spatiotemporal fusion methods, including the Unmixing-Based Data Fusion (UBDF), Linear Mixing Growth Model (LMGM), Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), One Pair Dictionary-Learning method (OPDL), and Flexible Spatiotemporal DAta Fusion (FSDAF), based on simulation experiments and theoretical analysis considering three influential factors between sensors: geometric misregistration, radiometric inconsistency, and spatial resolution ratio. ^[2] To overcome this limitation, we employed an improved flexible spatiotemporal data fusion (IFSDAF) model to conduct data fusion using MODIS and Landsat imagery and extract NDVI time series with both high spatial and temporal resolution. ^[3] Finally, the image with spatial information is introduced into the Flexible Spatiotemporal DAta Fusion (FSDAF) framework to improve the performance of spatiotemporal image-fusion. ^[4] They include the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), SaTellite dAta IntegRation (STAIR), and the CubeSat Enabled Spatio-Temporal Enhancement Method (CESTEM); the former three blended Landsat and MODIS data, whereas the latter combined CubeSats, Landsat, and MODIS observations. ^[5] In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. ^[6] , spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. ^[7] , enhanced deep convolutional spatiotemporal fusion network (EDCSTFN), super resolution convolutional neural network (SRCNN), spatial and temporal adaptive reflectance fusion model (STARFM), enhanced STARFM (ESTARFM), and flexible spatiotemporal data fusion (FSDAF)]. ^[8] In this study, we presented an improved Flexible Spatiotemporal Data Fusion (FSDAF) strategy with consideration of highly dynamic SPM variations in estuarine waters, and generated 30-m hourly SPM concentrations based on Landsat 8 OLI and GOCI datasets. ^[9] Comparison experiments were conducted by simulating ideal and challenging conditions of input time-series data in two characteristic areas, and the proposed methods were also compared with four typical methods: The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), and Improved FSDAF. ^[10] Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0. ^[11] Through the applied methods, we used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) to blend L8 and S2 data and obtain reliable normalized difference vegetation index (NDVI) datasets with fine spatial and temporal resolution. ^[12] Using spectral unmixing analysis and spatial interpolation, the flexible spatiotemporal data fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-cover changes. ^[13] The models included the spatial and temporal adaptive reflectance model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion model (FSDAF), and a spatiotemporal vegetation index image fusion model (STVIFM). ^[14] In this study, we modified the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. ^[15] The performances of SEVIS are analyzed in comparison with those of two other STF methods, the classical Spatial and Temporal Adaptive Fusion Model (STARFM) and the more recent Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm. ^[16] The Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method was developed in this study to fill this need. ^[17] The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. ^[18] The Flexible Spatiotemporal Data Fusion (FSDAF) model was then used to create two fused, high-resolution time-series products (fused MODIS and fused reconstructed MODIS) in order to enhance the spatial and temporal effectiveness of satellite images for field-scale applications. ^[19]