scholarly journals A Learning-Enhanced Two-Pair Spatiotemporal Reflectance Fusion Model for GF-2 and GF-1 WFV Satellite Data

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1789 ◽  
Author(s):  
Yanqin Ge ◽  
Yanrong Li ◽  
Jinyong Chen ◽  
Kang Sun ◽  
Dacheng Li ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Surface ◽  
Signal To Noise Ratio ◽  
Reconstruction Technique ◽  
Wide Field ◽  
Fusion Model ◽  
Absolute Deviation ◽  
Spectral Angle Mapper ◽  
Fusion Methods

Since requirements of related applications for time series remotely-sensed images with high spatial resolution have been hard to be satisfied under current observation conditions of satellite sensors, it is key to reconstruct high-resolution images at specified dates. As an effective data reconstruction technique, spatiotemporal fusion can be used to generate time series land surface parameters with a clear geophysical significance. In this study, an improved fusion model based on the Sparse Representation-Based Spatiotemporal Reflectance Fusion Model (SPSTFM) is developed and assessed with reflectance data from Gaofen-2 Multi-Spectral (GF-2 MS) and Gaofen-1 Wide-Field-View (GF-1 WFV). By introducing a spatially enhanced training method to dictionary training and sparse coding processes, the developed fusion framework is expected to promote the description of high-resolution and low-resolution overcomplete dictionaries. Assessment indices including Average Absolute Deviation (AAD), Root-Mean-Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), spectral angle mapper (SAM), structure similarity (SSIM) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS) are then used to test employed fusion methods for a parallel comparison. The experimental results show that more accurate prediction of GF-2 MS reflectance than that from the SPSTFM can be obtained and furthermore comparable with popular two-pair based reflectance fusion models like the Spatial and Temporal Adaptive Fusion Model (STARFM) and the Enhanced-STARFM (ESTARFM).

scholarly journals Time Series High-Resolution Land Surface Albedo Estimation Based on the Ensemble Kalman Filter Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 753 ◽  
Author(s):  
Guodong Zhang ◽  
Hongmin Zhou ◽  
Changjing Wang ◽  
Huazhu Xue ◽  
Jindi Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
High Resolution ◽  
Spatial Resolution ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Surface Albedo ◽  
Data Sets ◽  
Broadleaf Forest ◽  
Kalman Filter Algorithm

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo“ approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model . The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.

Semi-Automatic Extraction and Mapping of Farmlands based on High-Resolution Remote Sensing Images

2021 ◽  
Author(s):  
Dongsheng Liu ◽  
Ling Han
Keyword(s):  
High Resolution ◽  
Precision Agriculture ◽  
Sample Selection ◽  
Aerial Image ◽  
Automatic Extraction ◽  
Wide Field ◽  
Spectral Angle Mapper ◽  
Wide Field Of View ◽  
High Resolution Satellite Imagery ◽  
Region Growth

Extraction of agricultural parcels from high-resolution satellite imagery is an important task in precision agriculture. Here, we present a semi-automatic approach for agricultural parcel detection that achieves high accuracy and efficiency. Unlike the techniques presented in previous literatures, this method is pixel based, and it exploits the properties of a spectral angle mapper (SAM) to develop customized operators to accurately derive the parcels. The main steps of the method are sample selection, textural analysis, spectral homogenization, SAM, thresholding, and region growth. We have systematically evaluated the algorithm proposed on a variety of images from Gaofen-1 wide field of view (GF-1 WFV), Resource 1-02C (ZY1-02C), and Gaofen-2 (GF-2) to aerial image; the accuracies are 99.09% of GF-1 WFV, 84.42% of ZY1-02C, 96.51% and 92.18% of GF-2, and close to 100% of aerial image; these results demonstrated its accuracy and robustness.

A Spatiotemporal data Fusion method for generating a high-resolution, regular and cloud-free time series of satellite images

2020 ◽  
Author(s):  
Aojie Shen ◽  
Yanchen Bo ◽  
Duoduo Hu
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Data Fusion ◽  
Satellite Images ◽  
Free Time ◽  
Spatiotemporal Data ◽  
Kriging Interpolation ◽  
Fusion Method ◽  
Fusion Methods ◽  
Fine Resolution

<p>Scientific research of land surface dynamics in heterogeneous landscapes often require remote sensing data with high resolutions in both space and time. However, single sensor could not provide such data at both high resolutions. In addition, because of cloud pollution, images are often incomplete. Spatiotemporal data fusion methods is a feasible solution for the aforementioned data problem. However, for existing data fusion methods, it is difficult to address the problem constructed regular and cloud-free dense time-series images with high spatial resolution. To address these limitations of current spatiotemporal data fusion methods, in this paper, we presented a novel data fusion method for fusing multi-source satellite data to generate s a high-resolution, regular and cloud-free time series of satellite images.</p><p>We incorporates geostatistical theory into the fusion method, and takes the pixel value as a random variable which is composed of trend and a zero-mean second-order stationary residual. To fuse satellite images, we use the coarse-resolution image with high frequency observation to capture the trend in time, and use Kriging interpolation to obtain the residual in fine-resolution scale to provide the informative spatial information. In this paper, in order to avoid the smoothing effect caused by spatial interpolation, Kriging interpolation is performed only in time dimension. For certain region, the temporal correlation between pixels is fixed after the data reach stationary. So for getting the weight in temporal Kriging interpolation, we can use the residuals obtained from coarse-resolution images to construct the temporal covariance model. The predicted fine-resolution image can be obtained by returning the trend value of pixel to their own residual until the each pixel value was obtained. The advantage of the algorithm is to accurately predict fine-resolution images in heterogeneous areas by integrating all available information in the time-series images with fine spatial resolution.  </p><p>We tested our method to fuse NDVI of MODIS and Landsat at Bahia State where has heterogeneous landscape, and generated 8-day time series of NDVI for the whole year of 2016 at 30m resolution. By cross-validation, the average R<sup>2 </sup>and RMSE between NDVI from fused images and from observed images can reach 95% and 0.0411, respectively. In addition, experiments demonstrated that our method also can capture correct texture patterns. These promising results demonstrated this novel method can provide effective means to construct regular and cloud-free time series with high spatiotemporal resolution. Theoretically, the method can predict the fine-resolution data required on any given day. Such a capability is helpful for monitoring near-real-time land surface and ecological dynamics at the high-resolution scales most relevant to human activities.</p><p> </p>

scholarly journals A Comprehensive and Automated Fusion Method: The Enhanced Flexible Spatiotemporal DAta Fusion Model for Monitoring Dynamic Changes of Land Surface

2019 ◽  
Vol 9 (18) ◽  
pp. 3693 ◽  
Author(s):  
Shi ◽  
Wang ◽  
Zhang ◽  
Liang ◽  
Niu ◽  
...  
Keyword(s):  
Data Fusion ◽  
Land Surface ◽  
Abrupt Change ◽  
Spatiotemporal Data ◽  
Gradual Change ◽  
Fusion Method ◽  
Fusion Model ◽  
Dynamic Changes ◽  
Change Events ◽  
Fusion Methods

Spatiotemporal fusion methods provide an effective way to generate both high temporal and high spatial resolution data for monitoring dynamic changes of land surface. But existing fusion methods face two main challenges of monitoring the abrupt change events and accurately preserving the spatial details of objects. The Flexible Spatiotemporal DAta Fusion method (FSDAF) was proposed, which can monitor the abrupt change events, but its predicted images lacked intra-class variability and spatial details. To overcome the above limitations, this study proposed a comprehensive and automated fusion method, the Enhanced FSDAF (EFSDAF) method and tested it for Landsat–MODIS image fusion. Compared with FSDAF, the EFSDAF has the following strengths: (1) it considers the mixed pixels phenomenon of a Landsat image, and the predicted images by EFSDAF have more intra-class variability and spatial details; (2) it adjusts the differences between Landsat images and MODIS images; and (3) it improves the fusion accuracy in the abrupt change area by introducing a new residual index (RI). Vegetation phenology and flood events were selected to evaluate the performance of EFSDAF. Its performance was compared with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Spatial and Temporal Reflectance Unmixing Model (STRUM), and FSDAF. Results show that EFSDAF can monitor the changes of vegetation (gradual change) and flood (abrupt change), and the fusion images by EFSDAF are the best from both visual and quantitative evaluations. More importantly, EFSDAF can accurately generate the spatial details of the object and has strong robustness. Due to the above advantages of EFSDAF, it has great potential to monitor long-term dynamic changes of land surface.

Spiral CT of the pancreas

1998 ◽  
Vol 39 (1) ◽  
pp. 60-63 ◽  
Author(s):  
T. Nishiharu ◽  
Y. Yamashita ◽  
I. Ogata ◽  
S. Sumi ◽  
K. Mitsuzaki ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Spiral Ct ◽  
Reconstruction Technique ◽  
Signal To Noise ◽  
Standard Size ◽  
Noise Ratio ◽  
Good Signal

Purpose: to compare the value of a retrospective targeted high-resolution spiral CT to the standard reconstruction technique in the assessment of pancreatic diseases Material and Methods: Spiral CT pancreatic images of a standard-size reconstruction protocol were compared prospectively with those of a retrospective targeted high-spatial-resolution reconstruction protocol in 30 patients. Prior to clinical evaluation, a phantom study was performed to evaluate the spatial resolution and signal-to-noise ratio of both protocols Results: the high-resolution protocol achieved a good signal-to-noise ratio with acceptable spatial resolution. Phantom studies revealed increased image noise (+17%) with an increase in spatial resolution (+100%). in patients studied with the high-resolution protocol, the increase in noise was not significant but there was a marked improvement in the definition of small details Conclusion: Images obtained with a targeted high-spatial-resolution reconstruction protocol showed superior lesion definition and vascular opacification compared with those obtained with a standard-size reconstruction protocol. This technique may have potential in the evaluation of small pancreatic abnormalities

scholarly journals An Effective High Spatiotemporal Resolution NDVI Fusion Model Based on Histogram Clustering

2020 ◽  
Vol 12 (22) ◽  
pp. 3774
Author(s):  
Xuegang Xing ◽  
Changzhen Yan ◽  
Yanyan Jia ◽  
Haowei Jia ◽  
Junfeng Lu ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Time Series Data ◽  
Series Data ◽  
Fusion Model ◽  
Spatiotemporal Resolution ◽  
Model Based ◽  
High Spatiotemporal Resolution ◽  
Ndvi Time Series ◽  
Shape Changes

The normalized difference vegetation index (NDVI) is a powerful tool for understanding past vegetation, monitoring the current state, and predicting its future. Due to technological and budget limitations, the existing global NDVI time-series data cannot simultaneously meet the needs of high spatial and temporal resolution. This study proposes a high spatiotemporal resolution NDVI fusion model based on histogram clustering (NDVI_FMHC), which uses a new spatiotemporal fusion framework to predict phenological and shape changes. Meanwhile, this model also uses four strategies to reduce error, including the construction of an overdetermined linear mixed model, multiscale prediction, residual distribution, and Gaussian filtering. Five groups of real MODIS_NDVI and Landsat_NDVI datasets were used to verify the predictive performance of the NDVI_FMHC. The results indicate that NDVI_FMHC has higher accuracy and robustness in forest areas (r = 0.9488 and ADD = 0.0229) and cultivated land areas (r = 0.9493 and ADD = 0.0605), while the prediction effect is relatively weak in areas subject to shape changes, such as flooded areas (r = 0.8450 and ADD = 0.0968), urban areas (r = 0.8855 and ADD = 0.0756), and fire areas (r = 0.8417 and ADD = 0.0749). Compared with ESTARFM, NDVI_LMGM, and FSDAF, NDVI_FMHC has the highest prediction accuracy, the best spatial detail retention, and the strongest ability to capture shape changes. Therefore, the NDVI_FMHC can obtain NDVI time-series data with high spatiotemporal resolution, which can be used to realize long-term land surface dynamic process research in a complex environment.

scholarly journals Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 3738
Author(s):  
Adrià Descals ◽  
Aleixandre Verger ◽  
Gaofei Yin ◽  
Josep Peñuelas
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Google Earth ◽  
The Arctic ◽  
Threshold Method ◽  
Land Surface Phenology ◽  
Sentinel 2

The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.

scholarly journals Can We Use the QA4ECV Black-sky Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using AVHRR Surface Reflectance to Assess Terrestrial Global Change?

2019 ◽  
Vol 11 (24) ◽  
pp. 3055 ◽  
Author(s):  
Gobron ◽  
Marioni ◽  
Robustelli ◽  
Vermote
Keyword(s):  
Time Series ◽  
Photosynthetically Active Radiation ◽  
Land Surface ◽  
Third Party ◽  
Research Centre ◽  
Retrieval Algorithm ◽  
Wide Field ◽  
Surface Reflectance ◽  
Active Radiation ◽  
Absorbed Photosynthetically Active Radiation

NOAA platforms provide the longest period of terrestrial observation since the 1980s. The progress in calibration, atmospheric corrections and physically based land retrieval offers the opportunity to reprocess these data for extending terrestrial product time series. Within the Quality Assurance for Essential Climate Variables (QA4ECV) project, the black-sky Joint Research Centre (JRC)-fraction of absorbed photosynthetically active radiation (FAPAR) algorithm was developed for the AVHRR sensors on-board NOAA-07 to -16 using the Land Surface Reflectance Climate Data Record. The retrieval algorithm was based on the radiative transfer theory, and uncertainties were included in the products. We proposed a time and spatial composite for providing both 10-day and monthly products at 0.05º × 0.05º. Quality control and validation were achieved through benchmarking against third-party products, including Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) datasets produced with the same retrieval algorithm. Past ground-based measurements, providing a proxy of FAPAR, showed good agreement of seasonality values over short homogeneous canopies and mixed vegetation. The average difference between SeaWiFS and QA4ECV monthly products over 2002–2005 is about 0.075 with a standard deviation of 0.091. We proposed a monthly linear bias correction that reduced these statistics to 0.02 and 0.001. The complete harmonized long-term time series was then used to address its fitness for the purpose of analysis of global terrestrial change.

scholarly journals A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

2019 ◽  
Vol 11 (16) ◽  
pp. 1931 ◽  
Author(s):  
Hua Zhang ◽  
Paul V. Zimba ◽  
Emmanuel U. Nzewi
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Near Infrared ◽  
Aerial Imagery ◽  
Surface Reflectance ◽  
Radiometric Correction ◽  
Surface Patterns ◽  
Relative Radiometric Correction

The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.

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