Enhanced Spatial And Temporal Adaptive Reflectance Fusion Model Research Articles

Multi-factor weighted image fusion method for high spatiotemporal tracking of reservoir drawdown area and its vegetation dynamics

Reservoir drawdown areas (RDAs) with distinct dry-wet cycles and vegetation dynamics have emerged as significant hotspots for carbon-related activities. However, high-resolution spatiotemporal tracking of the variations and vegetation dynamics of RDAs remains challenging because they often change dramatically and are controlled by both human activities and natural factors. Herein, a modified image fusion method was proposed to capture rapid variations in RDAs by integrating impact factor information into the analysis. The capability of the proposed method was tested in the Danjiangkou (DJK) Reservoir as it is the largest artificial freshwater lake in Asia with a highly variable RDA, since it is surrounded by gently sloping plains or hills. The results showed that the modified workflow produced reliable predictions (r=0.83,RMSE=0.097) compared with the original Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) workflow (r=0.60,RMSE=0.195), demonstrating improved capability for mapping water surface changes and vegetation dynamics. Using the proposed method, the 15-d variations in the RDA were derived from 2013 to 2022 with a 30-m resolution. The interannual maximum RDA was estimated to be 278 km2 after the dam was elevated in 2013. The Normalized Difference Vegetation Index (NDVI) decreased as inundation frequency (IF) increased. Mean NDVI in the growing season (May–October) decreased by 0.109 (17.6 %) and 0.156 (26.0 %) under 30 %–40 % IF and 60 %–70 % IF, respectively, compared with the vegetation under 0 %–10 % IF, which was referred as “natural” vegetation considering its rare inundation. Moreover, the mean growing season length decreased to only 63 and 19 d for 30 %–40 % IF and 60 %–70 % IF, respectively. Furthermore, 77.3 % of the RDA exhibited a decrease in NDVI, whereas 22.7 % showed an unusual increase, possibly due to the selection of dominant species well-adapted to inundation during vegetation succession. Overall, this study not only proposed a new method for the high spatiotemporal monitoring of RDAs, but also highlighted the importance of variations and vegetation dynamics within RDAs for accurate estimation of their carbon budgets.

High Spatiotemporal Estimation of Reservoir Evaporation Water Loss by Integrating Remote-Sensing Data and the Generalized Complementary Relationship

Accurately estimating the reservoir evaporation loss is crucial for water resources management. The existing research on reservoir evaporation loss estimates primarily focuses on large spatiotemporal scales and neglects the rapid dynamic changes to reservoirs’ surface area. For reservoirs essential for frequent flood control and regular water supply, high spatiotemporal evaporation data are crucial. By integrating remote sensing and the evaporation model, this study proposes a new method for the high spatiotemporal estimation of the evaporation losses from reservoirs. The proposed method is applied to the largest artificial freshwater lake in Asia, i.e., Danjiangkou (DJK) Reservoir. The daily reservoir water surface area is extracted at a spatial resolution of 30 m during the period 2014–2018 based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). The daily evaporation rate is estimated at a spatial resolution of 100 m using the generalized complementary relationship (GCR). The results show that the water surface area of the DJK Reservoir exhibits rapid and frequent fluctuations from 2015 to 2018, with a multi-year average area of 731.9 km2 and a maximum and minimum difference of 304 km2. Significant seasonal variations are observed in both the evaporation rate and volume, with a multi-year average evaporation rate of 806 mm and evaporation volume of 595 million m3. The estimated results align well with three other independent estimates, indicating that the GCR is capable of water surface evaporation estimation. Further analysis suggests that the data resolution has a great influence on the evaporative water loss from the reservoir. The estimated mean annual evaporation volume based on the 1000 m resolution water surface area data is 14% lower than that estimated using the 30 m resolution water surface area data. This study not only provides a new method for the high spatiotemporal estimation of reservoir evaporation by integrating remote-sensing data and the GCR method but also highlights that reservoir evaporation water loss should be quantified using the volume rather than the rate and that the estimated loss is noticeably affected by the estimation spatial resolution.

Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data

The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin.

Mapping and classification of Liao River Delta coastal wetland based on time series and multi-source GaoFen images using stacking ensemble model

The precise mapping of coastal wetlands holds great significance for monitoring carbon sequestration and storage within coastal ecosystems, particularly in light of climate change and human-induced activities. Time series and multi-source remote sensing data offer distinct advantages in spatial and temporal land use mapping, particularly in wetland systems encompassing various vegetation types. The primary aim of this study was to delineate the spatial distribution of land use within the Liao River Delta (LRD) wetland. This was achieved by employing a stacking ensemble model that integrates time-series GaoFen-1 (GF-1) optical imagery, GaoFen-3 (GF-3) synthetic aperture radar (SAR) imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. The first step involved the application of an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to fuse the GF-1 NDVI and MODIS NDVI datasets, producing time-series NDVI data. Subsequently, the parameters pertaining to vegetation phenology were obtained by employing the threshold method on the time-series NDVI data. We compiled feature datasets encompassing GF-1 spectral bands, indices, phenological parameters, and GF-3 SAR features. A Recursive Feature Elimination and Cross-Validation (RFECV) model was employed to identify and select significant features to mitigate data redundancy. Finally, a stacking ensemble model was constructed by combining five base models [K-Nearest Neighbors (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Machine (LightGBM)] to perform wetland classification. The findings were as follows: (1) ESTARFM was able to successfully fuse GF-1 NDVI and MODIS NDVI data, resulting in spatiotemporal fusion with a coefficient of determination (R2) of 0.85 and a root mean square error (RMSE) of 0.07. (2) the RFECV algorithm was employed to select relevant features in the wetland classification process. Specifically, 75 spectral band features, 89 spectral index features, 13 SAR features, and seven phenological parameters were identified as significant for this task. (3) A stacking ensemble model was constructed using the aforementioned multi-source features. This model exhibited a robust and consistent performance in wetland classification, achieving the highest overall accuracy of 94.33%. Notably, this accuracy improvement ranged from approximately 0.09% to 10.02% compared to the individual base models. Thus, the present study has the potential to be utilized for fine-scale wetland monitoring, thereby offering valuable assistance in the field of wetland environmental research.

Evaluation of spatial downscaling for satellite retrieval of evapotranspiration from the nonparametric approach in an arid area

Evapotranspiration (ET) data is critical for monitoring the limited hydrological resources in arid areas. At present, the spatial resolution of ET derived by the thermal infrared images from satellite data is coarse (always more than 500 m). The low-resolution data cannot meet the needs of the high-precision mapping of ET. Therefore, it is important to undertake a downscaling procedure to improve the spatial resolution of ET. This paper selects the oasis region and surrounding deserts of Zhangye City of China as study area and compares with two downscaling procedures that include input and output downscaling procedure. The ET is estimated with the Remote Sensing-Non Parametric (RS-NP) model. The two downscaling procedures use Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to improve the spatial resolution of ET from 1-km to 100 m. Compared with the result derived from the output downscaling procedure, the high-resolution ET derived from the input downscaling procedure has a slightly better performance with R2 of 0.92 and RMSE of 41.8 W/m2, respectively. Neglectingthe downscaling of land surface temperature and broadband reflectance (BBR) result in larger errors of ET estimation with bias values of 73.0 W/m2 and 23.7 W/m2, respectively. Those values account for almost 1/3 and 1/10 of ET values, respectively. In non-vegetated areas, those proportions increase further around 90.6 % and 21.3 %, respectively. Furthermore, the influence of the neglect of the downscaling of the near-infrared reflectance on the ET estimation dominates that of BBR with a proportion of 42 %. The input downscaling procedure is suitable for generating the high-precision map of ET, especially in vegetated areas and during northern hemisphere summer months. Our study is helpful to obtain ET maps in fine spatial resolutions and contribute to the comprehensive understanding of the importance of the downscaling of each kind of input for reliable ET estimations.

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.

Downscaling of Oceanic Chlorophyll-a with a Spatiotemporal Fusion Model: A Case Study on the North Coast of the Yellow Sea

Chlorophyll-a concentration (Chl-a) is an important indicator of coastal eutrophication. Remote sensing technology provides a global view of it. However, different types of sensors are subject to design constraints and cannot meet the requirements of high temporal and spatial resolution on nearshore engineering simultaneously. To obtain high-spatiotemporal-resolution images, this study examines the performance of the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) on GOCI and Landsat Chl-a data fusion. Considering the rapidly changing rate and consistency of oceanic Chl-a, the ESTARFM was modified via segmented fitting and numerical conversion. The results show that both fusion models can fuse multiple data advantages to obtain high-spatiotemporal-resolution Chl-a images. Compared with the ESTARFM, the modified solution has a better performance in terms of the root mean square error and correlation coefficient, and its results have better spatial consistency for coastal Chl-a. In addition, the new solution expands the data utilization range of data fusion by reducing the influence of the time interval of original data and realizes better monitoring of nearshore Chl-a changes.

Analysis of spatial and temporal variation of vegetation NPP in Daning River Basin and its driving forces

ABSTRACT The Daning River Basin is a typical representative of the ‘mountain forest’ in the Three Gorges Reservoir (TGR) area of the Yangtze River. In recent years, with the completion of the Three Gorges Project, the local vegetation has degraded, soil erosion has become severe, and there is an urgent need to assess the environmental quality. Data were fused using the MODIS Normalized Difference Vegetation Index (NDVI) (250 m) and Landsat NDVI (30 m) through an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to obtain ESTARFM NDVI (30 m). This data was then entered into the Carnegie Ames Stanford Approach (CASA) model, along with meteorological and land use data, to calculate vegetation net primary productivity (NPP) and trends in the Daning River Basin. The detection of drivers with a high impact on vegetation NPP was done using a Geodetector. The results show that: (1) In terms of spatial and temporal changes, the annual average NPP of the watershed during the 13 years from 2008 to 2020 generally showed an upward trend, with the average yearly vegetation NPP being 512.33gC·m−2·a−1, exhibiting a low southwest to surrounding increasing trend along the river. (2) The spatial and temporal variation of vegetation NPP is influenced by several factors synergistically, with elevation, temperature, and distance from settlements being the dominant factors. The interaction of these two factors can enhance the explanatory power of vegetation NPP. Through the estimation of vegetation NPP and the analysis of influencing factors in the Daning River Basin, this study can provide a reference for the ecological restoration and management of vegetation NPP in small watersheds under similar environments.

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.

Detecting the long-term spatiotemporal crop phenology changes in a highly fragmented agricultural landscape

Trends in crop phenometrics reveal the influence of climate variability and change on crop growth and development. However, the trends are less clear in fragmented tropical smallholder landscapes, because there is high spatial and temporal variability in crop phenology. Frequent historical and high spatial resolution (≤30 m) Earth observations are needed to track changes in crop phenology in fragmented landscapes but are often unavailable. The spatial–temporal gap can be closed by integrating infrequent high spatial resolution Earth observations with low spatial/high temporal resolution observations through data fusion. We fused 30 m resolution Landsat and 250 m resolution MODIS imagery to investigate trends in crop phenology from 2000 to 2020 in a fragmented agricultural landscape of Ethiopia. We used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) that had recently been modified for application in fragmented agricultural landscapes. We used the non-parametric Mann–Kendall test for crop phenology trend analysis. Crop phenology based on Landsat–MODIS fusion was compared to MODIS-based crop phenology without fusion. We found data fusion yielded a smaller magnitude of changes in the start of season (SOS: -0.2-day-y−1) and end of season (EOS: -0.50-day-y−1) compared to MODIS SOS (-0.5-day-y−1) and EOS (1.38-day-y−1) due to MODIS-related mixing with the surrounding natural vegetation in the fragmented agricultural landscape. EOS showed a faster rate of change compared to SOS over the 21-years. The Landsat and MODIS fusion captured spatial variation in the timing and magnitude of change specific to crops and their growing environment, which has implications for adaptation strategies. Our results highlight the importance of long-term data fusion to improve the spatial dimension of crop phenology time series analysis. Integrating time series land cover maps into the data fusion processing chain could further improve long-term data fusion for crop phenology trend analysis.

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.

Mapping fine-spatial-resolution vegetation spring phenology from individual Landsat images using a convolutional neural network

ABSTRACT Monitoring and mapping vegetation dynamics using remote sensing data are essential for our understanding of land surface processes. Most current satellite-based methods process vegetation index time-series data from a series of images to retrieve key points that correspond to vegetation phenophases. As deep learning approaches have been found to be powerful in processing individual images, we tested the applicability of convolutional neural network (CNN) in mapping vegetation growth days (VGD) and the start of growing season (SOS) from each Landsat image at fine-spatial-resolution. To provide references for both model training and testing, we applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to fuse image pairs of Landsat 8 Operational Land Imager (OLI) and Moderate-resolution Imaging Spectroradiometer (MODIS) for four Landsat tiles in China. We then applied a first derivative method to retrieve VGD for the fused satellite data at fine-spatial-resolution. The CNN model was trained using each fused individual image as the model inputs and derived VGD as the targets. The trained model was further used to map VGD from individual Landsat image. The result could match the reference map well as indicated by the evaluation metrics. In terms of VGD, the method achieved a coefficient of determination of 0.85 and a root mean squared error of 8.17 days. In terms of SOS, the method achieved a coefficient of determination of 0.75 and a root mean squared error of 4.09 days. Compared with existing methods that require time series of satellite data spanning the entire growth cycles to retrieve phenological metrics, this study provides an alternative method to map VGD as well as SOS using individual Landsat image. Our study highlights the power of deep learning models in extracting phenological features from individual remote sensing images. Researchers can use our methods to predict near real-time VGD and SOS in the future.

Evapotranspiration of Winter Wheat in the Semi-Arid Southeastern Loess Plateau Based on Multi-Source Satellite Data

Continuous monitoring of evapotranspiration (ET) at high spatio-temporal resolutions is vital for managing agricultural water resources in arid and semi-arid regions. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to calculate the ET of winter wheat between the green-up and milk stages in Linfen Basin, a typical, semi-arid area of the Loess Plateau, at temporal and spatial resolutions of 30 m and 8 d, respectively. We then analyzed the impact of meteorological factors on ET and its variation during the main growth period of winter wheat. The fused ET data displayed the spatial details of the OLI ET data better and could accurately reflect ET variation and local sudden variations during the main growth period of winter wheat. Moreover, winter wheat ET in rain-fed areas is more heavily influenced by meteorological factors, and the effect is more direct. Affected by the synergistic effect of wind velocity, precipitation, and temperature, the ET of winter wheat in rain-fed area was lower in the green-up stage. Then, ET gradually increased, reaching its maximum in the heading–grain filling stage. At the jointing stage, temperature had a significant effect on ET. A combination of precipitation and temperature had the greatest impact on the ET of winter wheat in the heading–filling stage. In the milk stage, meteorological factors had a minor impact on ET. This study serves as a reference for ET in winter wheat in semi-arid areas and its influencing meteorological factors, which can assist in drought mitigation and regional food security strategies.

Fusion of Landsat 8 Operational Land Imager and Geostationary Ocean Color Imager for hourly monitoring surface morphology of lake ice with high resolution in Chagan Lake of Northeast China

Abstract. The surface morphology of lake ice remarkably changes under the combined influence of thermal and mechanical forces. However, research on the surface morphology of lake ice and its interaction with climate is scarce. A large-scale linear structure has repeatedly appeared on satellite images of Chagan Lake in recent years. The Geostationary Ocean Color Imager (GOCI), with a 1 h revisit, and Landsat 8 Operational Land Imager (OLI), with a spatial resolution of 30 m, provide the possibility for the study of hourly changes in the large-scale linear structure. We merged the Landsat and GOCI images, using an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and extracted the lengths and angles of the linear structure. We monitored the hourly changes in the surface morphology during the cold season from 2018 to 2019. The average length of the linear structure in the completely frozen period was 21 141.57 ± 68.36 m. The average azimuth angle was 335.48 ± 0.23∘, nearly perpendicular to the domain wind in winter. Through two field investigations during the two recent cold seasons, we verified the linear structure as being ice fractures and ridges. The evolution of surface morphology is closely associated with air temperature, wind, and shoreline geometry.

An Advanced Spatiotemporal Fusion Model for Suspended Particulate Matter Monitoring in an Intermontane Lake

Ebinur Lake is the largest brackish-water lake in Xinjiang, China. Strong winds constantly have an impact on this shallow water body, causing high variability in turbidity of water. Therefore, it is crucial to continuously monitor suspended particulate matter (SPM) for water quality management. This research aims to develop an advanced spatiotemporal fusion model based on the inversion technique that enables time-continuous and detailed monitoring of SPM over an intermontane lake. The findings shows that: (1) the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) fusion in blue, green, red, and near infrared (NIR) bands was better than the flexible spatiotemporal data fusion (FSDAF) model in extracting SPM information; (2) the inversion model constructed by random forest (RF) outperformed the support vector machine (SVM) and partial least squares (PLS) algorithms; and (3) the SPM concentrations acquired from the fused images of Landsat 8 OLI and ESTARFM matched with the actual data of Ebinur Lake based on the visual perspective and accuracy assessment.

High-Spatial-Resolution NDVI Reconstruction with GA-ANN.

The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm-artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space-time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction's NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution.

Classification of Conservation Tillage Using Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model

In the remote sensing monitoring of conservation tillage, the acquisition of remote sensing data with high spatial and temporal resolution is critical. The current optical remote sensing images cannot realize both temporal and spatial resolution, especially under cloud and rain interference. Thus, this study employs the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to obtain the normalized difference tillage index (NDTI) with both temporal and spatial resolution estimated by Sentinel−2 and MODIS using the Index−then−Blend (IB) and Blend−then−Index (BI) fusion schemes. After comparison, the IB scheme was better than the BI scheme in predicting results and prediction efficiency. The NDTI predicted by ESTARFM and Sentinel−2 on June 12, 2020 was compared. A coefficient of determination R2 of 0.73 and RMSE of 0.000117 was obtained, indicating a high prediction accuracy, which meets the prediction requirements. Based on the predicted ESTARFM NDTI of the study area on May 17, 2021, the maize residue cover (MRC) of the study area was estimated using the previously constructed MRC unary linear regression model. The MRC of the sampling points of the remote sensing images was estimated by verifying the predicted ESTARFM NDTI with the MRC of the sampling points taken in the field extracted by the maximum likelihood classifier, which has a coefficient of determination R2 of 0.78 and RMSE of 0.00676, signifying better prediction results. The proposed method provides considerable data sources for the remote sensing monitoring studies of conservation tillage.

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.

MUSTFN: A spatiotemporal fusion method for multi-scale and multi-sensor remote sensing images based on a convolutional neural network

• A deep learning spatiotemporal fusion method for large area remote sensing images. • Multi-constraint loss function to improve model accuracy. • Multi-scale features to avoid the influence of different sensor registration errors on fusion results. • Model is suitable for drastically changing remote sensing image fusion scenarios. • Model has strong transferability for remote sensing images at different times and locations. Spatiotemporal data fusion is a commonly-used and well-proven technique to enhance the application potential of multi-source remote sensing images. However, most existing methods have trouble in generating quality fusion results when areas covered by the images undergoes rapid land cover changes or images have substantial registration errors. While deep learning algorithms have demonstrated their capabilities for imagery fusion, it is challenging to apply deep-learning-based fusion methods in regions that experiences persistent cloud covers and have limited cloud-free imagery observations. To address these challenges, we developed a Multi-scene Spatiotemporal Fusion Network (MUSTFN) algorithm based on a Convolutional Neural Network (CNN). Our approach uses multi-level features to fuse images at different resolutions acquired by multiple sensors. Furthermore, MUSTFN uses the multi-scale features to overcome the effects of geometric registration errors between different images. Additionally, a multi-constrained loss function is proposed to improve the accuracy of imagery fusion over large areas and solve fusion and gap-filling problems simultaneously by utilizing cloud-contaminated images with the fine-tuning method. Compared with several commonly-used methods, our proposed MUSTFN performs better in fusing the 30-m Landsat-7 images and 500-m MODIS images over a small area that has undergone large changes (the average relative Mean Absolute Errors (rMAE) of the first four bands are 6.8% by MUSTFN as compared to 14.1% by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), 12.8% by the Flexible Spatiotemporal Data Fusion (FSDAF), 8.4% by the Extended Super-Resolution Convolutional Neural Network (ESRCNN), 8.1% by the Spatiotemporal Fusion Using a Generative Adversarial Network (STFGAN)). In particularly for images at different resolutions with different registration accuracies (e.g., 16-m Chinese GaoFen-1 and 500-m MODIS), MUSTFN achieved fusion results of good quality with an average rMAE of 9.3% in spectral reflectance at the first four bands. Finally, we demonstrated the applicability of MUSTFN (average rMAE of 9.18%) when fusing long-term Landsat-8 composite images and MODIS images over a large region (830 km × 600 km). Overall, our results suggest the effectiveness of MUSTFN to address the challenges in imagery fusion, including rapid land cover changes between image acquisition dates, geometric misregistration between images and limited availabilities of cloud-free images. The program of MUSTFN is freely available at: https://github.com/qpyeah/MUSTFN .

High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data.