Spatiotemporal Data Fusion Method Research Articles

High spatiotemporal-resolution mapping for a seasonal erosion flooding inundation using time-series Landsat and MODIS images

Seasonal erosion flooding events present a significant challenge for effective disaster monitoring and land degradation studies. This research addresses this challenge by harnessing the combined capabilities of time-series Landsat and MODIS images to achieve high spatiotemporal-resolution mapping of flooding during such events. The study underscores the critical importance of precise flood monitoring for disaster mitigation and informed land management. To overcome the limitations posed by the trade-off between spatial and temporal resolution in current satellite sensors, we emplyedand theflexible spatiotemporal data fusion (FSDAF) methods to produce synthetic flood images with enhanced spatiotemporal resolutions for mapping by using MODIS and Landsat data from August 29 to September 3, 2016. A comparison was made between flood maps from several post-disaster forecasts based on ground-obtained time-series images of the Tumen River flood in China. According to the FSDAF approach, the input Landsat image of March 25, 2016, and the fused results had a root mean square error (RMSE) of 0.0301, average difference of 0.001, r of 0.941, and structure similarity indexof 0.939, indicating that temporal variation data had been effectively incorporated into a forecast on August 16, 2016. Results also indicated that the FSDAF forecast values are lower than those from the actual Landsat image. The results of the study also showed that the generated images could be effectively used for flood mapping. By using our newly developed simulation model, we were able to produce a comprehensive map of the inundated areas during the event from August 29 to September 3, 2016. This shows that FSDAF holds great potential for flood prediction and study and has the potential to benefit further disaster-related land degradation by combining multi-source images to provide high temporal and spatial resolution remote sensing information.

Deep learning models for groundwater level prediction based on delay penalty

Abstract In irrigation agriculture, predicting groundwater level (GWL) using deep learning models can help decision-makers coordinate surface water and groundwater usage, thus aiding in the sustainable development and utilization of groundwater. However, when making a long sequence prediction, prediction sequences often have severe delays affecting the availability of prediction results. In this paper, a new loss function is proposed to minimize the lag and oversmoothing on the prediction of GWLs. GWL, meteorology, and pumping data are collected via an irrigation Internet of Things system in Hutubi County, Xinjiang. Through Pearson's correlation analysis, historical potential evapotranspiration (ET0), groundwater extraction, and GWL were chosen to predict GWLs. Datasets were constructed through the proposed spatiotemporal data fusion method; then, the best model from the six deep learning models was selected by comparing the prediction capability of the datasets. Finally, the mean-squared error (MSE) loss function is replaced by the proposed loss function. Compared to the mean absolute error, MSE, and predicted sequence graphs, the new loss function significantly depresses the time delay with similar prediction accuracy.

Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition

Early detection of defects, such as keyhole pores and cracks is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM) to prevent build failures. However, the complex melt pool behaviour cannot be adequately captured by conventional single-modal process monitoring approaches. This study introduces a multisensor fusion-based digital twin (MFDT) for localized quality prediction in the robotic L-DED process. The data used in multisensor fusion includes features extracted from a coaxial melt pool vision camera, a microphone, and an off-axis short wavelength infrared thermal camera. The key novelty of this work is a spatiotemporal data fusion method that synchronizes multisensor features with the real-time robot motion data to achieve localized quality prediction. Optical microscope (OM) images of the printed part are used to locate defect-free and defective regions (i.e., cracks and keyhole pores), which serve as ground truth labels for training supervised machine learning (ML) models for quality prediction. The trained ML model is then used to generate a virtual quality map that registers quality prediction outcomes within the 3D volume of the printed part, thus eliminating the need of physical inspections by destructive methods. Experiments show that the virtual quality map closely matches the actual quality observed by OM. Compared to traditional single-sensor-based quality prediction, the MFDT has achieved a significantly higher quality prediction accuracy (96%), a higher ROC-AUC score (99%), and a lower false alarm rate (4.4%). As a result, the MFDT is a more reliable method for defect prediction. The proposed MFDT also lays the groundwork for our future development of a self-adaptive hybrid processing strategy that combines machining with AM for defect removal and quality improvement.

Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario.

Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect.

A Big Data Grided Organization and Management Method for Cropland Quality Evaluation

A new gridded spatio-temporal big data fusion method is proposed for the organization and management of cropland big data, which could serve the analysis application of cropland quality evaluation and other analyses of geographic big data. Compared with traditional big data fusion methods, this method maps the spatio-temporal and attribute features of multi-source data to grid cells in order to achieve the structural unity and orderly organization of spatio-temporal big data with format differences, semantic ambiguities, and different coordinate projections. Firstly, this paper constructs a dissected cropland big data fusion model and completes the design of a conceptual model and logic model, constructs a cropland data organization model based on DGGS (discrete global grid system) and Hash coding, and realizes the unified management of vector data, raster data and text data by using multilevel grids. Secondly, this paper researches the evaluation methods of grid-scale adaptability, and generates distributed multilevel grid datasets to meet the needs of cropland area quality evaluation. Finally, typical data such as soil organic matter data, road network data, cropland area data, and statistic data in Da’an County, China, were selected to carry out the experiment. The experiment verifies that the method could not only realize the unified organization and efficient management of cultivated land big data with multimodal characteristics, but also support the evaluation of cropland quality.

Fine-scale mapping of Spartina alterniflora-invaded mangrove forests with multi-temporal WorldView-Sentinel-2 data fusion

Mangrove forests, a critical component of blue carbon ecosystems, make a significant contribution to mitigating climate change and maintaining biodiversity. But in China, they have been suffering from severe invasion by Spartina alterniflora (S. alterniflora). Accurate information on spatial distribution of mangroves and S. alterniflora is a prerequisite to protecting indigenous mangrove ecosystems and managing S. alterniflora invasion. However, the classification performance is often constrained by two major issues: (1) A single-date image, regardless of acquisition time, hardly provides optimal spectral separability among evergreen mangroves, deciduous S. alterniflora and mudflats; (2) Due to the gradual invasion process, the mixtures between land cover classes are prevalent in moderate-resolution images. The use of high-resolution (finer than 2 m) image time series could effectively solve these two issues. However, due to frequent cloud cover in coastal areas and long revisit time of high-resolution satellite sensors, the collection of high-resolution image time series is still a great challenge, let alone substantial expenses. In this study, we proposed a new multi-temporal classification approach to fine-scale mapping of exotic S. alterniflora and native mangroves using synthetic high-resolution image time series derived from spatiotemporal fusion of WorldView-2 and Sentinel-2 data. A total of six synthetic WorldView-2-like images in different phenological periods were produced by the Flexible Spatiotemporal DAta Fusion (FSDAF) method. With the combination of the original and synthetic images, both types of image-based multi-temporal features and pixel-based image compositing features were extracted and evaluated in the following Random Forest classifications. Monte Carlo cross-validation was adopted to provide robust accuracy estimates, and a spatially explicit confidence map was generated to show classification uncertainty and potential errors. In order to further demonstrate the necessity of generating synthetic WorldView-2 images, the entire classification process was repeated using Sentinel-2 images at 10-m spatial resolution and the comparison between fine- and moderate-resolution classification results was made in terms of classification accuracies, classification maps and fractional abundances. Our results show that the spectral profiles of S. alterniflora and mangroves generated from the synthetic WorldView-2 images were consistent with the ones from the paired Sentinel-2 images. Meanwhile, the spatial details of high landscape heterogeneity, unrecognizable in the Sentinel-2 images, were revealed by the synthetic WorldView-2 images. Compared to the single-date classification with the original WorldView-2 image, most of multi-temporal classifications using both the original and synthetic images provided more accurate results, with the overall accuracy raised by up to 3.9%. Among various temporal features adopted in this study, the use of the image-based, stacked spectral reflectance and vegetation index features of all original and synthetic images produced the best classification performance, increasing the producer's and user's accuracies of mangroves, S. alterniflora and mudflats by 2.5%–7.1%. Finally, the Sentinel-2 classification accuracies were generally lower than the WorldView-2 counterparts across all temporal features, with a 6% difference in the highest overall accuracy between the two. Prediction errors of the moderate-resolution classification were often found in heterogeneous landscapes, where S. alterniflora was mixed with mangroves or mudflats. Considering the availability of moderate-resolution data with high revisit frequencies, such as Sentinel-2A/B and Landsat 8/9, future fine-scale mapping exercises might benefit from the multi-temporal classification approach with spatiotemporal fusion of moderate- and high-resolution data, alleviating the problem of the scarcity of high-resolution data in cloudy areas.

MULTISENSOR FUSION-BASED DIGITAL TWIN IN ADDITIVE MANUFACTURING FOR IN-SITU QUALITY MONITORING AND DEFECT CORRECTION

AbstractEarly detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser-directed energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defect correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.

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.

STFRDN: a residual dense network for remote sensing image spatiotemporal fusion

ABSTRACT Convolutional Neural Networks (CNN) are useful models for spatiotemporal fusion, especially under strong temporal changes. Convolutional layers with receptive fields of various sizes produce hierarchical features that can improve prediction performance. In this study, we aimed to develop a CNN model for spatiotemporal fusion, which can extract hierarchical features and fully use them. The proposed network is composed of residual dense blocks with local dense connections that effectively utilize the hierarchical features. We conducted the experiments in the newly proposed Kansas dataset, which includes Sentinel-2 and Sentinel-3 image pairs with large resolution differences and strong temporal changes. Based on both quantitative and qualitative evaluation, experiments revealed that the STFRDN model outperformed the Flexible Spatiotemporal DAta Fusion (FSDAF) 2.0, Reliable and Adaptive Spatiotemporal Data Fusion (RASDF), Fit-FC, and DMNet methods in all of the test groups. The second major finding was that the STFRDN model was able to produce sufficiently successful predictions against cases with such large resolution differences. To get more accurate prediction results when there is a significant resolution difference, it is important to extract the right features that can model the discrepancy. Loss of spatial detail can be avoided by making full use of hierarchical features without any manipulation after extraction. The results of this study provide insights into the significance of extracting and fully utilizing the hierarchical features.

Sequential Spatiotemporal Bias Compensation and Data Fusion for Maneuvering Target Tracking

In multisensor tracking systems, bias compensation is essential for accurate data fusion. It is challenging to obtain correct fusion results in the presence of both spatial and temporal biases, as well as target maneuver. In this article, a sequential spatiotemporal bias compensation and data fusion method is proposed for maneuvering target tracking with asynchronous multisensor measurements. The spatiotemporal biases of the sensors are augmented into the state vector to be estimated. The state equations are presented to formulate the nearly coordinated turn motion and the nearly constant acceleration motion, respectively, without exactly known measurement interval. In each target motion, the measurements are formulated as functions of both spatiotemporal bias and target state based on the time difference between the measurements and the states to be estimated. Furthermore, the interacting multiple model estimator is incorporated with the unscented Kalman filter to achieve simultaneous sequential estimation of spatiotemporal biases and target states in the presence of target maneuvers. Finally, the posterior Cramer–Rao lower bound for spatiotemporal bias and state estimation is provided. Simulation experiments are performed to demonstrate the effectiveness of the proposed method.

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.

Monitoring the spatiotemporal change of Dongting Lake wetland by integrating Landsat and MODIS images, from 2001 to 2020

Dongting Lake wetland provides critical ecological functions in the Yangtze River Basin, however its landscape pattern has changed during the past 20 years due to both climate change and the human activities such as the operation of the Three Gorges dam (TGD). Numerous studies have used remote sensing technology to monitor such changes. However, most of studies were conducted at low spatial resolutions (250–1000 m) or low temporal resolutions (few images per year), which can introduce a degree of unreliability as regards the associated conclusions. To thoroughly analyze the spatiotemporal characteristics of Dongting Lake wetland, high spatiotemporal resolution images from 2001 to 2020 have been produced by a modified version of the flexible spatiotemporal data fusion method (cuFSDAF 2.0) developed in this paper. This method is able to retrieve both phenology and land-cover changes both quickly and accurately. In addition, a tailored category by category extraction mapping method (CCEMM) is proposed to reliably classify wetland cover types. The mapping results show that the water area expanded by 66.43 Km2 in the wet season, and shrank by 132.86 Km2 in the dry season, between and including the years, 2001 to 2020. It appeared that the water area in the dry season declined obviously from 2003 to 2010 due to the operation of the TGD and is gradually stabilized after 2010. The reed area increased by 279.59 Km2 and the grass area decreased by 220.46 Km2 in the dry season, from 2001 to 2020. The shrinkage of the water area in the dry season may be one of the main factors driving reeds to occupy the previous grass growth area.

SCRNet: an efficient spatial channel attention residual network for spatiotemporal fusion

Spatiotemporal fusion is a simple, feasible, and economical method for balancing the temporal and spatial resolutions of satellite images. However, the current spatiotemporal fusion methods have some disadvantages, such as their insufficient feature extraction abilities and unsatisfactory fusion image effects. To obtain higher-quality spatiotemporal fusion images, a spatiotemporal data fusion method based on deep learning is proposed. This method combines an attention mechanism and a residual learning strategy to design an asymmetric spatial channel attention residual network (SCRNet). For different input images, the SCRNet extracts rich feature information to fuse high-quality images in a more emphatic and scientific manner than other approaches. Specifically, we design an efficient and flexible spatial channel attention mechanism that not only focuses on spatial information but also takes channel information into account. The optimized residual network can further enhance the ability of the network to extract feature information. In addition, we design a compound loss function and propose an edge loss to further enrich the extracted feature information and improve the resulting image quality. Experimental results on two datasets show that the proposed method outperforms existing fusion methods in subjective and objective evaluations.

A Sensor Bias Correction Method for Reducing the Uncertainty in the Spatiotemporal Fusion of Remote Sensing Images

With the development of multisource satellite platforms and the deepening of remote sensing applications, the growing demand for high-spatial resolution and high-temporal resolution remote sensing images has aroused extensive interest in spatiotemporal fusion research. However, reducing the uncertainty of fusion results caused by sensor inconsistencies and input data preprocessing is one of the challenges in spatiotemporal fusion algorithms. Here, we propose a novel sensor bias correction method to correct the input data of the spatiotemporal fusion model through a machine learning technique learning the bias between different sensors. Taking the normalized difference vegetation index (NDVI) images with low-spatial resolution (MODIS) and high-spatial resolution (Landsat) as the basic data, we generated the neighborhood gray matrices from the MODIS image and established the image bias pairs of MODIS and Landsat. The light gradient boosting machine (LGBM) regression model was used for the nonlinear fitting of the bias pairs to correct MODIS NDVI images. For three different landscape areas with various spatial heterogeneities, the fusion of the bias-corrected MODIS NDVI and Landsat NDVI was conducted by using the spatiotemporal adaptive reflection fusion model (STARFM) and the flexible spatiotemporal data fusion method (FSDAF), respectively. The results show that the sensor bias correction method can enhance the spatially detailed information in the input data, significantly improve the accuracy and robustness of the spatiotemporal fusion technology, and extend the applicability of the spatiotemporal fusion models.

Remote-Sensing Monitoring of Postfire Vegetation Dynamics in the Greater Hinggan Mountain Range Based on Long Time-Series Data: Analysis of the Effects of Six Topographic and Climatic Factors

The frequency of forest fires is increasing under global climate change, and forest fires can cause devastating disturbances to forest systems and varying degrees of recovery of forest ecosystems after a disaster. Due to the different intensity of forest fires and forest systems, and in particular the fact that forest ecological recovery is influenced by many topographical and climatic factors, the process of postfire vegetation recovery is unclear and must be studied in depth. In this study, the Greater Hinggan Mountain Range was taken as the study area. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat time-series images acquired from 2000 to 2018, this study used the spatiotemporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after forest fires. The vegetation restoration was characterized by disturbance index (DI) values, which eliminated phenological influence. Six types of topography and climatic factors (elevation, aspect, slope; temperature, precipitation, and wind speed) were coupled with DI. Through single-factor analysis of variance and multiple comparison statistical methods, it was found that there was a significant relationship between the six factors and DI, which indicated those factors had a significant impact on the restoration of forest vegetation in burned areas. The results will be useful as a reference for future monitoring and management of forest resources.

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.

A classification-based spatiotemporal adaptive fusion model for the evaluation of remotely sensed evapotranspiration in heterogeneous irrigated agricultural area

Remotely sensed evapotranspiration (ET) with high spatial and temporal resolution is frequently required to understand the regional hydrological processes, particularly in agricultural areas with complex planting structures. Most of the existing spatio-temporal fusion models lacked the fusion of ET because they ignored the physiological characteristics of the vegetation moisture condition. Therefore, we propose a classification-based spatiotemporal adaptive fusion model (CSAFM) for the evaluation of remotely sensed ET in an irrigated agricultural area with a complex planting structure. This model combines the unmixing-based and weight-based fusion approaches to produce ET maps with high spatiotemporal resolution. It uses the mainstream weight-based fusion algorism—the spatial and temporal adaptive reflectance fusion model (STARFM)—in the fusion step. However, in contrast to the existing reflectance-based fusion algorithms, the CSAFM considers the effects of soil moisture and crop category on evapotranspiration rates. It replaces the unmixing window with an irregular hydrological response unit (HRU) containing homogeneous meteorological and irrigation conditions, and then unmixing the mixed pixels using a planting structure map. Moreover, an ET correction method was proposed in CSAFM to restore the spatial heterogeneity. The performance of CSAFM was compared to that of two mainstream fusion models using Landsat-ET and MODIS-ET based on the surface energy balance algorithm for land (SEBAL): the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion method (FSDAF). The models were validated with ground-based ET monitored by eddy covariance observed ET and Landsat inverted ET. It was found that the CSAFM model (mean MAE: 0.40 mm/day) beat the ESTARFM model (mean MAE: 0.49 mm/day) and the FSDAF (mean MAE: 0.53 mm/day) in accurately fusion ET and reproduce the details of complex surface landscapes. Additionally, CSAFM (RMSE: 0.66–1.10 mm/day) is less sensitive to the update frequency of input data in the crop growing season than ESTARFM (RMSE: 0.79–1.36 mm/day) and FSDAF (RMSE: 0.79–1.17 mm/day), indicating its suitability in areas with limited input dataset. Overall, the proposed CSAFM model can greatly improve the ET fusion accuracy in irrigated agricultural areas.

A Two-Source Normalized Soil Thermal Inertia Model for Estimating Field-Scale Soil Moisture from MODIS and ASTER Data

Soil moisture (SM) is a crucial component for understanding, modeling, and forecasting terrestrial water cycles and energy budgets. However, estimating field-scale SM based on thermal infrared remote-sensing data is still a challenging task. In this study, an improved Flexible Spatiotemporal DAta Fusion (FSDAF) method based on land-surface Diurnal Temperature Cycle (DTC) model (DFSDAF) was proposed to fuse Moderate Resolution Imaging Spectroradiometer (MODIS) and Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) land-surface temperature (LST) data to generate ASTER-like LST during the night. The reconstructed diurnal LST data at a high spatial resolution (90 m) was then utilized to drive a two-source normalized soil thermal inertia model (TNSTI) for the vegetated surfaces to estimate field-scale SM. The results of the proposed methods were validated at different observation depths (2, 4, 10, 20, 40, 60, and 100 cm) over the Zhangye oasis in the middle region of the Heihe River basin in the northwest of China and were compared with the SM estimates from the TNSTI model and other SM products, including AMSR2/AMSR-E, GLDAS-Noah, and ERA5-land. The results showed the following: (1) The DFSDAF method increased the accuracy of LST prediction, with the determination coefficient (R2) increasing from 0.71 to 0.77, and root mean square error (RMSE) decreasing from 2.17 to 1.89 K. (2) the estimated SMs had the best correlation with the observations at the 10 cm depth (with R2 of 0.657; RMSE of 0.069 m3/m3), but the worst correlation with observations at the 40 cm depth (with R2 of 0.262; RMSE of 0.092 m3/m3); meanwhile, the modeled SMs were significantly underestimated above 40 cm (2, 4, 10, and 20 cm) and slightly overestimated below 40 cm (60 and 100 cm); in addition, the field-scale SM series at high spatial resolution (90 m) showed significant spatiotemporal variation. (3) The SM estimates based on the TNSTI for the vegetated surfaces are more capable of characterizing the SM status in the root zone (~80 cm) or even deeper, while the SMs from AMSR2/AMSR-E, GLDAS-Noah, or ERA5-land products are closer to the SM in the surface layer (the depth is less than 5 cm). The TNSTI provided favorable data supports for hydrological model simulations and showed potential advantages for agricultural refinement managements and smart agriculture.

A Comprehensive Flexible Spatiotemporal DAta Fusion Method (CFSDAF) for Generating High Spatiotemporal Resolution Land Surface Temperature in Urban Area

Spatiotemporal fusion of land surface temperature (LST) has a vital significance in studying the temporal and spatial variation of urban heat islands. But most existing LST fusion methods don't consider the high heterogenous urban surface and complexity of spatial layout. In this study, a Comprehensive Flexible Spatiotemporal DAta Fusion (CFSDAF) method was proposed to generate high spatiotemporal resolution urban LST image which was improvements of the Flexible Spatiotemporal DAta Fusion (FSDAF). The CFSDAF first adjusted the differences between coarse resolution LST and fine resolution LST. Then, the visible and near-infrared image of fine resolution was introduced to execute spectral unmixing and to conduct soft classification, which considered the mixed pixel of fine resolution LST. The inverse distance weighting (IDW) interpolation was used in improving the computational efficiency, and the constrained least squares (CLS) was selected to better distribute the residual. The performance of CFSDAF was compared with the temporal adaptive reflectance fusion model (STARFM) and FSDAF. The results indicate that the predicted images by CFSDAF are better than STARFM and FSDAF from both visual comparison and quantitative assessment in two experiments, and CFSDAF can reserve more spatial details and accurately restore the spatial continuity of urban LST than others. Moreover, CFSDAF has high computational efficiency, and can monitor land cover changes same as FSDAF. Due to the above advantages, the CFSDAF has great potential for studying spatiotemporal changes of LST and UHI in urban area.

A reliable and adaptive spatiotemporal data fusion method for blending multi-spatiotemporal-resolution satellite images

Spatiotemporal image fusion is a potential way to resolve the constraint between the spatial and temporal resolutions of satellite images and has been developed rapidly in recent years. However, two key challenges related to fusion accuracy remain: a) reducing the uncertainty of image fusion caused by sensor differences and b) addressing strong temporal changes. To solve the above two issues, this paper presents the newly proposed Reliable and Adaptive Spatiotemporal Data Fusion (RASDF) method. In RASDF, the effects of four kinds of sensor differences on fusion are analyzed systematically. A reliability index is therefore proposed to describe the spatial distribution of the reliability in input data for image fusion. An optimization strategy based on the spatial distribution of the reliability quantified by the index is developed to improve the robustness of the fusion. In addition, an adaptive global unmixing model and an adaptive local unmixing model are constructed and utilized collaboratively to enhance the ability to retrieve strong temporal changes. The performance and robustness of RASDF were compared with six representative fusion methods for both real and simulated datasets covering both homogeneous and heterogeneous sites. Experimental results indicated that RASDF achieves a better performance and provides a more reliable image fusion solution in terms of reducing the impact of sensor differences on image fusion and retrieving strong temporal changes.