Spatial And Temporal Adaptive Reflectance Fusion Model Research Articles

Exploring the optimal sequences for fusing land surface temperature via spatiotemporal fusion: ‘retrieval-then-fusion’ or ‘fusion-then-retrieval’?

ABSTRACT Due to the limitations inherent in satellite sensor capabilities, it remains a challenge to balance the temporal and spatial resolutions of current remote sensing observations. Spatiotemporal fusion has emerged as a viable approach to address this issue. Many spatiotemporal fusion models have been proposed to fuse surface reflectance initially; however, their adaption for land surface temperature (LST), particularly with the ‘fusion-then-retrieval (FTR)’ or ‘retrieval-then-fusion (RTF)’ strategy, has not yet been comprehensively explored. This paper describes a comparative study of the two strategies within the context of LST fusion using three classic fusion methods including 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). The findings illuminate nuanced applicability across different models: (1) the FTR strategy is more suitable for STARFM; (2) the strategic applicability for ESTARFM and FSDAF is strongly related to the reference images; (3) the STARFM and FSDAF models are easily affected by the time interval between the predicted date and the reference date. This work is expected to provide practical guidance for synthesizing high spatiotemporal LST data with fusion models.

Mapping Crop Evapotranspiration by Combining the Unmixing and Weight Image Fusion Methods

The demand for freshwater is increasing with population growth and rapid socio-economic development. It is more and more important for refined irrigation water management to conduct research on crop evapotranspiration (ET) data with a high spatiotemporal resolution in agricultural regions. We propose the unmixing–weight ET image fusion model (UWET), which integrates the advantages of the unmixing method in spatial downscaling and the weight-based method in temporal prediction to produce daily ET maps with a high spatial resolution. The Landsat-ET and MODIS-ET datasets for the UWET fusion data are retrieved from Landsat and MODIS images based on the surface energy balance model. The UWET model considers the effects of crop phenology, precipitation, and land cover in the process of the ET image fusion. The precision evaluation is conducted on the UWET results, and the measured ET values are monitored by eddy covariance at the Luancheng station, with average MAE values of 0.57 mm/day. The image results of UWET show fine spatial details and capture the dynamic ET changes. The seasonal ET values of winter wheat from the ET map mainly range from 350 to 660 mm in 2019–2020 and from 300 to 620 mm in 2020–2021. The average seasonal ET in 2019–2020 is 499.89 mm, and in 2020–2021, it is 459.44 mm. The performance of UWET is compared with two other fusion models: the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Spatial and Temporal Reflectance Unmixing Model (STRUM). UWET performs better in the spatial details than the STARFM and is better in the temporal characteristics than the STRUM. The results indicate that UWET is suitable for generating ET products with a high spatial–temporal resolution in agricultural regions.

Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data

Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R2>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.

Effectiveness of Spatiotemporal Data Fusion in Fine-Scale Land Surface Phenology Monitoring: A Simulation Study

Spatiotemporal data fusion technologies have been widely used for land surface phenology (LSP) monitoring since it is a low-cost solution to obtain fine-resolution satellite time series. However, the reliability of fused images is largely affected by land surface heterogeneity and input data. It is unclear whether data fusion can really benefit LSP studies at fine scales. To explore this research question, this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms, namely, pair-based Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data in extracting pixel-wise spring phenology (i.e., the start of the growing season, SOS) and its spatial gradient and temporal variation. Our results reveal that: (a) STARFM can improve the accuracy of pixel-wise SOS by up to 74.47% and temporal variation by up to 59.13%, respectively, compared with only using Landsat images, but it can hardly improve the retrieval of spatial gradient. For SSFIT, the accuracy of pixel-wise SOS, spatial gradient, and temporal variation can be improved by up to 139.20%, 26.36%, and 162.30%, respectively; (b) the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year, and it has a large variation with the same number of available Landsat images, and (c) this large variation is highly related to the temporal distributions of available Landsat images, suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period. This study calls for caution with the use of data fusion in LSP studies at fine scales.

A robust gap-filling method for predicting missing observations in daily Black Marble nighttime light data

ABSTRACT Nighttime light (NTL) remote sensing data plays a crucial role in comprehending changes in human activities. The availability of the daily lunar BRDF-corrected Black Marble NTL product (VNP46A2) enables the use of NTL data to detect and assess the impact of short-term emergencies. However, daily NTL data often experience missing values due to cloud cover and low-quality signals. To address this issue, many studies utilize monthly or annual time-composite NTL products, which restrict the timeliness and potential application scenarios of NTL data usage. Therefore, it is necessary to generate the gap-filled daily NTL product. This study presented a novel NTL gap-filling method comprising rough reconstruction based on spatiotemporal weighting and refined gap-filling using a Bidirectional Long Short-Term Memory (Bi-LSTM) model. We evaluate the accuracy of the proposed method using the “remove-reconstruct-compare” approach, which randomly removes some original data from the complete image, fills the gaps with the proposed gap-filling method, and compares the reconstructed NTL data with the original observations in Beijing, Shanghai, Xi’an and New York. The results reveal that when the rate of missing values in Beijing is 40% and 50%, the proposed gap-filling method achieves accuracy with mean coefficient of determination (R2) values of 0.834 and 0.841, accompanied by corresponding root mean square (RMSE) values of 7.793 and 7.171, respectively. Furthermore, the gap-filling accuracy was evaluated quantitatively, and our proposed gap-filling method performed better than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Our proposed gap-filling method had R2 values of 0.685, 0.781, 0.720 and 0.642, which were higher than those for STARFM (0.430, 0.662, 0.221 and 0.345). The RMSE values of our gap-filling method were 9.628, 12.083, 10.963 and 19.882 for the four sites, while those of STARFM were 12.953, 14.872, 18.280 and 26.990, respectively. The temporal and spatial analysis results demonstrate that this model is robust, capturing city boundaries and NTL high-brightness hotspots with high accuracy and stability. The gap-filling model proposed in this study provides a new technique for expanding the potential applications and reliability of NASA’s daily Black Marble product (VNP46A2) in remote sensing.

Improving the STARFM Fusion Method for Downscaling the SSEBOP Evapotranspiration Product from 1 km to 30 m in an Arid Area in China

Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r2) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm/10 d to 8.48 mm/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP.

A spatio-temporal fusion strategy for improving the estimation accuracy of the aboveground biomass in grassland based on GF-1 and MODIS

The accurate estimation of the aboveground biomass (AGB) is crucial for the sustainable utilization and management of grassland resources. Spatio-temporal inconsistencies between field samples and satellite images are major source of error in the estimation of grassland AGB. To solve this problem, this study selected the Three Rivers Headwater Region as the study area and proposed a selection strategy for base-period images of spatio-temporal fusion that was suitable for use at a large spatial scale in which cloud-free images are difficult to obtain. The spatial and temporal adaptive reflectance fusion model (STARFM) based on the selection strategy was used to generate a synthetic Normalized Difference Vegetation Index (NDVI) dataset with high spatial-temporal resolution by using the maximum value composite of GF-1 NDVI and MODIS NDVI to enhance the spatial-temporal quality of the images for field-scale application. Three estimation models for grassland AGB were then constructed by the random forest algorithm using synthetic NDVI, MODIS NDVI, GF-1 NDVI respectively, together with ancillary data. Following this, the estimation model with the highest accuracy was used to generate a 16-m eight-day time-series AGB in the growing season. The results showed: (1) The synthetic NDVI was correlated closely with the observed GF-1 NDVI, with an average R of 0.825 and a RMSE of 0.087. The temporal trend of the synthetic NDVI for each grassland type was highly consistent with that of the MODIS NDVI in the growing season with a correlation higher than 0.9. (2) The synthetic NDVI reduced the spatial difference between field samples and images to 16-fold, and the temporal difference was controlled to within four days under ideal conditions. (3) The synthetic NDVI improved the estimation accuracy of grassland AGB by about 15.9% and 19.7% (R2), and 13.7% and 17.5% (RMSE) relative to MODIS NDVI and GF-1 NDVI, respectively. (4) The time-series AGB revealed accurately the spatial distribution of and seasonal temporal variations in the grassland biomass. The results of this study may serve as scientific guidance for timely monitoring of grassland conditions and precise management of grassland resources in China.

STEPSBI: Quick spatiotemporal fusion with coarse- and fine-resolution scale transformation errors and pixel-based synthesis base image pair

Spatiotemporal fusion (STF) is crucial for reconciling the conflict between temporal and spatial resolutions of remote sensing observations. However, fusing images in heterogeneous areas remains challenging under continuous missing values. Moreover, most current STF methods only consider temporal errors and disregard the spatial scale error in time variation. Therefore, we proposed a Quick Spatiotemporal Fusion with Coarse- and Fine-Resolution Scale Transformation Errors and Pixel-Based Synthesis Base Image Pair (STEPSBI). First, the optimal pixel-based image synthesis strategy was designed using all available fine- and coarse-resolution images. Then, the scale transformation error (STE) of coarse-resolution downscaling to fine-resolution in temporal variation was quantified. And a residual term was introduced to reduce the prediction error from the temporal variation. Finally, the spatial scale and temporal errors were corrected using the results of super-pixel segmentation as spatial weights. This model has two strengths: (1) Pixel-based image synthesis alleviates the absence of base images under continuous missing values; and (2) STE correction restores spatial details of heterogeneous areas in rapid land cover change. In scenarios of continuous missing images, abrupt land cover changes, and high-resolution heterogeneity, we evaluated STEPSBI, the Gap Filling and Savitzky–Golay filtering method (GF-SG), the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), the cross-attention-based adaptive weighting fusion network (CAFE), and the Multi-scene Spatiotemporal Fusion Network (MUSTFN). The results indicate that STEPSBI yields better overall performance than other models in cropland, woodland, grassland, and other land cover types. Furthermore, ablation experiments demonstrated that each component improved the model’s performance. In addition, STEPSBI had higher fusion efficiency because it was developed on the Google Earth Engine cloud computing platform. Therefore, STEPSBI was feasible for advancing fine monitoring of spatiotemporal image fusion under continuous missing values and heterogeneous surfaces. The program of STEPBSI is freely available at: https://code.earthengine.google.com/684844aa42f64fa6b4eebe3bc0dd6483.

Generating daily 100 m resolution land surface temperature estimates continentally using an unbiased spatiotemporal fusion approach

Fine spatial resolution (i.e., ≤ 100 m) land surface temperature (LST) data are crucial to study heterogeneous landscapes (e.g., agricultural and urban). Some well-known spatiotemporal fusion methods like the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), which were originally developed to fuse surface reflectance data, may not be suitable for direct application in LST studies due to the high sub-diurnal dynamics of LST. Furthermore, the effectiveness of spatiotemporal fusion methods for LST data has not been thoroughly evaluated in previous studies that only focused on relatively small spatiotemporal extents. To address these limitations, we proposed a variant of ESTARFM, referred to as the unbiased ESTARFM (ubESTARFM), specifically designed to accommodate the high temporal dynamics of LST to generate fine-resolution LST estimates. We evaluated ubESTARFM and ESTARFM against in-situ LST and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST across 12 regions throughout Australia, encompassing various land covers and environments. Independent validation showed that ubESTARFM had a bias of 2.55 K, unbiased root mean squared error (ubRMSE) of 2.57 K, and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11,290 observations at the 12 sites, all of which were considerably better than those calculated for ESTARFM, being a bias of 4.73 K, ubRMSE of 3.80 K and R of 0.92. When compared to ECOSTRESS data, ubESTARFM LST had a bias of −1.69 K, ubRMSE of 2.00 K, and R of 0.70 over 43 near clear-sky scenes, while ESTARFM LST had a bias of 1.79 K, ubRMSE of 2.68 K, and R of 0.59. Overall, our results demonstrated that ubESTARFM can avoid systematic bias accumulation, substantially reduce uncertainty deviation, and maintain a good level of correlation with validation datasets when compared to ESTARFM. A further assessment underscored the potential of ubESTARFM for application using LST data acquired from geostationary platforms (e.g., Himawari-8), with a mean ubRMSE (R) of 2.22 K (0.97) against in-situ LST over 1327 observations at 3 sites from southeast Australia at the overpass time of MODIS/Terra. This promising method leverages reliable numeric values from coarse-resolution LST while borrowing spatial heterogeneity from fine-resolution LST and has the potential to be coupled with energy balance and/or radiative transfer models thus enabling better farm and/or regional-scale water management strategies to be implemented. Furthermore, both the input and generated LST data, encompassing a comprehensive spatial extent over diverse land covers and climatic conditions, are publicly available for benchmarking future algorithmic refinements.

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores 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 (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR.

Tree Species Classification over Cloudy Mountainous Regions by Spatiotemporal Fusion and Ensemble Classifier

Accurate mapping of tree species is critical for the sustainable development of the forestry industry. However, the lack of cloud-free optical images makes it challenging to map tree species accurately in cloudy mountainous regions. In order to improve tree species identification in this context, a classification method using spatiotemporal fusion and ensemble classifier is proposed. The applicability of three spatiotemporal fusion methods, i.e., the spatial and temporal adaptive reflectance fusion model (STARFM), the flexible spatiotemporal data fusion (FSDAF), and the spatial and temporal nonlocal filter-based fusion model (STNLFFM), in fusing MODIS and Landsat 8 images was investigated. The fusion results in Helong City show that the STNLFFM algorithm generated the best fused images. The correlation coefficients between the fusion images and actual Landsat images on May 28 and October 19 were 0.9746 and 0.9226, respectively, with an average of 0.9486. Dense Landsat-like time series at 8-day time intervals were generated using this method. This time series imagery and topography-derived features were used as predictor variables. Four machine learning methods, i.e., K-nearest neighbors (KNN), random forest (RF), artificial neural networks (ANNs), and light gradient boosting machine (LightGBM), were selected for tree species classification in Helong City, Jilin Province. An ensemble classifier combining these classifiers was constructed to further improve the accuracy. The ensemble classifier consistently achieved the highest accuracy in almost all classification scenarios, with a maximum overall accuracy improvement of approximately 3.4% compared to the best base classifier. Compared to only using a single temporal image, utilizing dense time series and the ensemble classifier can improve the classification accuracy by about 20%, and the overall accuracy reaches 84.32%. In conclusion, using spatiotemporal fusion and the ensemble classifier can significantly enhance tree species identification in cloudy mountainous areas with poor data availability.

A Robust Framework for Resolution Enhancement of Land Surface Temperature by Combining Spatial Downscaling and Spatiotemporal Fusion Methods

Land surface temperature (LST) products with high spatial resolution and short revisiting cycles are crucial for environmental studies. However, due to the tradeoff between spatial and temporal resolutions of satellite observations, such data are not directly available. Spatial downscaling and spatiotemporal fusion methods are existing solutions for this problem, but their robustness is limited under different surface conditions. Here, we propose a Robust Framework for Combining Downscaling and spatiotemporal Fusion methods (RFCDF) to generate the synthesized daily high-resolution LST with high accuracy in different landscapes. RFCDF introduces a novel weighting strategy that determines pixel-level weights using an empirical function under the constraint of the image-level weights of two predictions. We implement the framework using the thermal sharpening algorithm (TsHARP) and Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing and Baotou, as well as Landsat 8 and simulated coarse resolution imagery in nine sub-regions with different surface landscapes in Beijing. Our results demonstrate that RFCDF can generate more accurate estimations and preserve more spatial details than either individual or combination methods, improving accuracy by 0.1–0.6 K and 0.4–1.3 K in the two study areas, respectively. Moreover, the proposed framework is robust, reducing the root mean square error of estimations by 8-24% under different surface conditions. RFCDF can also generate dense high-resolution LST time series, which is crucial for studying the surface thermal environment at a finer scale.

A Spatio-Temporal Fusion Framework of UAV and Satellite Imagery for Winter Wheat Growth Monitoring

Accurate and continuous monitoring of crop growth is vital for the development of precision agriculture. Unmanned aerial vehicle (UAV) and satellite platforms have considerable complementarity in high spatial resolution (centimeter-scale) and fixed revisit cycle. It is meaningful to optimize the cross-platform synergy for agricultural applications. Considering the characteristics of UAV and satellite platforms, a spatio-temporal fusion (STF) framework of UAV and satellite imagery is developed. It includes registration, radiometric normalization, preliminary fusion, and reflectance reconstruction. The proposed STF framework significantly improves the fusion accuracy with both better quantitative metrics and visualized results compared with four existing STF methods with different fusion strategies. Especially for the prediction of object boundary and spatial texture, the absolute values of Robert’s edge (EDGE) and local binary pattern (LBP) decreased by a maximum of more than 0.25 and 0.10, respectively, compared with the spatial and temporal adaptive reflectance fusion model (STARFM). Moreover, the STF framework enhances the temporal resolution to daily, although the satellite imagery is discontinuous. Further, its application potential for winter wheat growth monitoring is explored. The daily synthetic imagery with UAV spatial resolution describes the seasonal dynamics of winter wheat well. The synthetic Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2) are consistent with the observations. However, the error in NDVI and EVI2 at boundary changes is relatively large, which needs further exploration. This research provides an STF framework to generate very dense and high-spatial-resolution remote sensing data at a low cost. It not only contributes to precision agriculture applications, but also is valuable for land-surface dynamic monitoring.

Applicability Assessment of a Spatiotemporal Geostatistical Fusion Model for Disaster Monitoring: Two Cases of Flood and Wildfire

A spatial time series geostatistical deconvolution/fusion model (STGDFM), as one of spatiotemporal data fusion model, combines Dense time series data with a Coarse-scale (i.e., DC data) and Sparse time series data with a Fine-scale (i.e., SF data) to generate Synthetic Dense time series data with a Fine-scale (i.e., SDF data). Specifically, STGDFM uses a geostatistics-based spatial time series modeling to capture the temporal trends included in time series DC data. This study evaluated the prediction performance of STGDFM for abrupt changes in reflectance due to disasters in spatiotemporal data fusion, and a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM) were selected as comparative models. For the applicability assessment, flood and wildfire were selected as case studies. In the case of flood, MODIS-like data (240 m) with spatial resolution converted from Landsat data and Landsat data (30 m) were used as DC and SF data, respectively. In the case of wildfire, MODIS and Landsat data were used as DC and SF data, respectively. The case study results showed that among the three spatiotemporal fusion models, STGDFM presented the best prediction performance with 0.894 to 0.979 at the structure similarity and 0.760 to 0.872 at the R-squared values in the flood- and wildfire-affected areas. Unlike STARFM and ESTARFM that adopt the assumptions for reflectance changes, STGDFM combines the temporal trends using time series DC data. Therefore, STGDFM could capture the abrupt changes in reflectance due to the flood and wildfire. These results indicate that STGDFM can be used for cases where satellite images of appropriate temporal and spatial resolution are difficult to acquire for disaster monitoring.

VSDF: A variation-based spatiotemporal data fusion method

The fusion of spatiotemporal data provides the possibility to improve both the spatial and temporal resolution of remote sensing data. Nevertheless, the performance of current spatiotemporal data fusion methods is affected by several aspects, e.g., (1) retrieval of abrupt land cover changes, (2) recovery of detailed spatial information, and (3) the need to reduce side effects related to the performance differences between sensors. Concerning the above aspects, this study proposes the use of a Variation-based Spatiotemporal Data Fusion (VSDF) method. In VSDF, an abundant variation classification (AVC) is used to identify explicitly the land cover changes for spectral unmixing. In addition, feature-level fusion is introduced to strengthen the spatial structures, i.e., the edges and texture. Furthermore, a relative reliability index (RRI) is proposed to guide the prediction process to lower the uncertainty of the input datasets. With reference to the all-round performance assessment (APA) metrics, the performance of VSDF was compared with five popular methods, i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), FSDAF 2.0, Reliable and Adaptive Spatiotemporal Data Fusion (RASDF), and Fit-FC (regression model Fitting, spatial Filtering and residual Compensation). The experimental results demonstrated that VSDF can realize a more accurate prediction of temporal land cover changes with better spatial detail than the benchmark methods. Consequently, VSDF has the potential to generate accurate high-spatiotemporal-resolution simulations for global remote sensing studies.

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands.

A high spatiotemporal resolution land surface temperature research over Qinghai-Tibet Plateau for 2000–2020

As ‘the third pole of the world’, the land surface temperature (LST) of the Qinghai-Tibet Plateau (QTP) has a profound impact on the climate of central Asia and even the whole earth. Studying the impact of the LST over QTP depends on long time and high spatiotemporal resolution LST dataset. However, the unavailability of such dataset has hindered LST-related researches: one of the most important reasons is that traditional spatiotemporal fusion methods such as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) have heavy computation demands to process big data. To fill this gap, this paper outlines one new cloud spatiotemporal fusion method by combining Google Earth Engine and STARFM to develop for the first time a 3-hourly 30 m LST dataset over the QTP from 2000 to 2020 through fusing Landsat and GLDAS-2.1 derived LST data. The outlined method first fuses the LSTs obtained from Landsat and GLDAS-2.1 data within one year to synthesize the LST of the entire QTP on one base time, and then the base time LST is spatiotemporally fused with GLDAS-2.1 LSTs on the base and prediction times to derive the 3-hourly 30 m QTP's LSTs on prediction times. The outlined method provides a promising technical scheme for batch processing big data in combining traditional spatiotemporal fusion methods with cloud computing platforms. Derived LST dataset is validated by station observations at multiple time and spatial scales to have high accuracy, which provides a guarantee for analyzing water and heat exchange and climate change over the QTP.

The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation

Over the past decade, spatiotemporal fusion has become an indispensable tool for monitoring land surface dynamics due to its promising ability to produce surface reflectance products with both high spatial and temporal resolutions. However, existing fusion methods usually generate multispectral band products by predicting each spectral band separately, so the useful information of spectral autocorrelation within the spectrum has been ignored and waits to be exploited. To address this issue, we propose a novel spatiotemporal fusion method, the spatiotemporal Fusion Incorrporting Spectral autocorrelaTion (FIRST) model, to fully utilize the multiple spectral bands of surface reflectance products. Compared with other fusion methods, the model has three distinct advantages: (1) it utilizes spectral autocorrelation in a many-to-many regression framework that simultaneously inputs and predicts multispectral bands without the collinearity effect; (2) it maintains high fusion accuracy when the spatiotemporal variation is large with acceptable computational efficiency; and (3) it can produce robust results even with input images contaminated by haze and thin clouds. We tested the FIRST model at several experimental sites and compared it with four typical methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF) model, the regression model Fitting, spatial Filtering and residual Compensation (Fit-FC) model and the enhanced STARFM (ESTARFM). The results demonstrate that FIRST yields better overall performance for its simple and effective technical principles. FIRST is thus expected to provide high-quality remotely sensed data with high spatial resolution and frequent observations for various applications.

Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE¯ = 11.1d) is better than daily fusion datasets (RMSE¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation.

Land Surface Phenology Retrieval through Spectral and Angular Harmonization of Landsat-8, Sentinel-2 and Gaofen-1 Data

Land Surface Phenology is an important characteristic of vegetation, which can be informative of its response to climate change. However, satellite-based identification of vegetation transition dates is hindered by inconsistencies in different observation platforms, including band settings, viewing angles, and scale effects. Therefore, time-series data with high consistency are necessary for monitoring vegetation phenology. This study proposes a data harmonization approach that involves band conversion and bidirectional reflectance distribution function (BRDF) correction to create normalized reflectance from Landsat-8, Sentinel-2A, and Gaofen-1 (GF-1) satellite data, characterized by the same spectral and illumination-viewing angles as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Nadir BRDF Adjusted Reflectance (NBAR). The harmonized data are then subjected to the spatial and temporal adaptive reflectance fusion model (STARFM) to produce time-series data with high spatio–temporal resolution. Finally, the transition date of typical vegetation was estimated using regular 30 m spatial resolution data. The results show that the data harmonization method proposed in this study assists in improving the consistency of different observations under different viewing angles. The fusion result of STARFM was improved after eliminating differences in the input data, and the accuracy of the remote-sensing-based vegetation transition date was improved by the fused time-series curve with the input of harmonized data. The root mean square error (RMSE) estimation of the vegetation transition date decreased by 9.58 days. We concluded that data harmonization eliminates the viewing-angle effect and is essential for time-series vegetation monitoring through improved data fusion.