Fractional vegetation cover (FVC) is a critical component of ecosystems, global climate change and the carbon cycle. Several FVC products have been released, the most widely used of which are the GLASS FVC products (including the GLASS-MODIS and GLASS-AVHRR FVC products). Specifically, the GLASS-MODIS FVC product covers the period from 2000 to present with a 500 m spatial resolution, whereas the GLASS-AVHRR FVC product is available from 1982 to present with a coarser spatial resolution of 5 km. For local monitoring of patterns of change in vegetation, however, there is a great need for fine spatial resolution (e.g., 500 m in this paper) and long-term time-series FVC datasets. To this end, we proposed to reconstruct a 500 m, 8-day historical MODIS FVC dataset (1982–2000) by making full use of the advantages of the existing GLASS-MODIS FVC (fine spatial resolution of 500 m) and GLASS-AVHRR FVC (long-term coverage from 1982 to the present) products covering China in this paper. The known GLASS-AVHRR FVC product was first used to fit the relationship between the FVC data after 2000 and before 2000, based on a random forest (RF) model. The trained relationship was migrated to the GLASS-MODIS FVC product, that is, predicting the MODIS FVC before 2000 based on the input of MODIS FVC after 2000. The validation using 64 scenes of Landsat FVC reference data revealed that the predicted historical MODIS FVC dataset has a reliable accuracy with a correlation coefficient (CC) value of 0.84, root mean square error (RMSE) of 0.14, Bias of 0.04 and unbiased RMSE (ubRMSE) of 0.12. Moreover, an accuracy evaluation in seven different regions in 1999 suggested that the historical MODIS FVC is closer to the Landsat FVC than the GEOV2 FVC product. Overall, the 500 m, 8-day MODIS FVC dataset (1982–2000) in China can provide important historical data for long-term, local monitoring of vegetation, which has great potential in supporting studies in a range of applications areas including ecology, hydrology and climatology. This dataset is available at https://doi.org/10.6084/m9.figshare.24616446.v1.

Solar-induced chlorophyll fluorescence (SIF) is a crucial variable towards timely and effective monitoring of vegetation productivity, as well as physiological and biochemical parameters, across extensive areas. Among these advances, the TROPOspheric Monitoring Instrument (TROPOMI) SIF has significantly increased the spatiotemporal resolution and data coverage compared to previous sensors. However, TROPOMI SIF data suffer from nonuniform sampling, swath gaps and cloud contamination, resulting in numerous instances of missing data. In this paper, we proposed a physical and spatial information-aided gap filling (PSGF) method, which addresses effectively the missing data problem, generating a spatially seamless, 0.05°, daily SIF (S2-SIF) dataset globally at a spatial resolution of 0.05° from 2018 to 2021. Through missing data simulation experiments conducted in six regions worldwide, we demonstrated consistency between the reference SIF and the filled SIF, with a correlation coefficient (CC) of 0.659. Furthermore, validation usingin situdata from 35 SIF and gross primary productivity (GPP) ground sites yielded a CC of approximately 0.70 for the SIF sites and CC values above 0.60 between the ground GPP and filled SIF. Additionally, consistency was observed between the filled SIF datasets and two other SIF products across 11 vegetation types, confirming the reliability of the filled SIF data and the efficacy of the PSGF method. The produced filled SIF data are made publicly available and should increase greatly the applicability of the daily SIF data for a wide range of applications, including quantifying the photosynthesis of vegetation and accurately estimating GPP globally.

Spatio-temporal fusion is a general technique used to blend fine spatial resolution and fine temporal resolution remote sensing data from multiple sensors, to generate time-series data with both fine spatial and temporal resolutions. It has received increasing attention in recent years. Drastic changes in land surface, however, pose great challenges for spatio-temporal fusion. To address this issue, this paper proposed a spatio-temporal fusion method which integrates multi-modal data (M2-STF), specifically SAR data with optical data. Considering the scenario of flooding (which causes drastic land surface changes) as an example, this study focused on spatio-temporal fusion based on Sentinel-2 MSI and Sentinel-3 OLCI data, and developed the M2-STF method by integrating Sentinel-1 SAR data. For the changed area, M2-STF integrates the Sentinel-2 image at the known time and the Sentinel-1 SAR image at the prediction time to obtain a more accurate fine spatial resolution classification map at the prediction time. Based on this map, a spatial unmixing model and spatial interpolation model were developed taking into account both homogeneity and heterogeneity characteristics, which were then combined into a homogeneity index. For the unchanged area, a new similar pixel selection strategy was constructed to exclude the influence of similar pixels from the changed area. In the experiments, three regions were selected for validation, and M2-STF was compared with five typical spatio-temporal fusion methods. By integrating Sentinel-1 SAR data at the prediction time, the accuracy of spatio-temporal fusion was increased remarkably, especially when the land surface changes greatly from the known to the prediction times. Specifically, the M2-STF method outperforms all five benchmark methods, by reducing root mean square error (RMSE) by at least 16%.

Extensive cloud contamination severely hinders the interpretation of optical remote sensing images. Existing cloud removal methods focus primarily on the reconstruction of individual cloudy images, with few studies addressing the reconstruction of cloudy time-series images. Furthermore, current methods tend to prioritize using cloud-free auxiliary images while overlooking valuable information present in the cloudy auxiliary images that are temporally closer to the target cloudy image. In this paper, we proposed a deep network called Res-cLSTM to reconstruct cloudy time-series images. Res-cLSTM processes time-series images sequentially using convolutional LSTM, synthesizing long- and short-term memory streams to match the complex temporal relationships amongst them. Then, Res-cLSTM further decodes the feature maps using a refined residual module with skip connections, resulting in the final output. Simulated and real cloud removal experiments on Landsat 8 OLI time-series data across five different regions demonstrated that Res-cLSTM is an effective cloud removal method, which can produce more accurate predictions than three benchmark approaches. For example, for reconstruction of the cloudy time-series of three simulated cloudy areas, the average CC of the Res-cLSTM prediction is about 0.01, 0.04 and 0.04 larger than that of the second most accurate method (i.e., autoencoder (AE)). As a lightweight network, Res-cLSTM does not require global sampling of training data and can fully exploit the valuable information in the non-cloud regions of cloudy time-series images to facilitate cloud removal. Moreover, Res-cLSTM demonstrates robustness to thin cloud omission and exhibits a faster convergence rate, thus, holds great potential for practical applications requiring real-time processing.

Temporally neighboring homologous images are crucial to provide auxiliary information for thick cloud removal. Due to the inherent satellite revisit period and frequent cloud obscuration, there is often a significant time interval between the target cloudy images and neighboring cloud-free homologous images, leading to potential land surface condition changes. Moreover, multitemporal cloudy images that may contain valuable complementary information in the noncloudy regions, are often neglected in practice. This article focused on thick cloud removal from Landsat 8 OLI images. We proposed to fuse the temporally more frequent Sentinel-2 MSI images and also cloudy multitemporal images consisting of Sentinel-2 MSI and Landsat 8 OLI time-series. Acquired by a sensor different from Landsat 8 OLI, Sentinel-2 MSI images exhibit great similarities in data characteristics. To fully exploit the spatio-temporal-spectral information in multisource and multitemporal auxiliary images, we proposed a novel deep network called MST-Net. MST-Net was validated using 12 simulated and two real cloudy Landsat 8 OLI images. The results show that the MST-Net can produce more satisfactory predictions than the five benchmark methods. Both the images acquired by a different sensor and homogeneous multitemporal cloudy images are beneficial. Under different sizes of clouds, the MST-Net produces consistently the most accurate predictions. Furthermore, due to the fusion of all bands simultaneously in the temporally closest Sentinel-2 MSI images, the MST-Net is less affected by thin cloud occlusion errors. Overall, the MST-Net shows great potential for cloud removal from optical images produced by a wide range of sensors and, more generally, filling gaps in various global scale products.

Accurate estimation of canopy height is crucial for monitoring forest health, carbon cycling, biodiversity, and climate change. Existing canopy height mapping methods often integrate Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with optical or radar remote sensing images. However, these methods typically establish relationships between single GEDI relative height (RH) metrics (e.g., RH95 or RH98) and surface reflectance or backscatter signals, overlooking valuable information from multiple RH metrics. In this study, we developed a multiple relative height metrics-based canopy height mapping (MRH-CHM) model, which was implemented using deep learning based on the Google Earth Engine (GEE) cloud platform. The MRH-CHM model integrates features from multiple GEDI RH metrics (e.g., RH0 to RH100 with an interval of 10 units) and also Landsat-8 and Sentinel-1 images. To deal with the distinct features in the multimodal data, in the proposed MRH-CHM method, the convolutional neural network (CNN) and multi-layer perceptron (MLP) modules were designed separately before a feature fusion process. Using the MRH-CHM model, a canopy height map of China for the year 2020 was produced at 30 m spatial resolution. The MRH-CHM results present greater accuracy than Lang’s, Potapov’s, and Liu’s products, achieving the lowest Root Mean Square Error (RMSE) of 5.74 m and the largest correlation coefficient (r) of 0.78 when validated against 6,168,244 hold-out GEDI validation data points. The produced map provides valuable scientific data for policymakers, researchers, and forest stakeholders to monitor forest health and biodiversity and to guide efforts toward carbon neutrality. The produced canopy height map is publicly available at https://doi.org/10.5281/zenodo.11560195.

Landsat surface temperature (LST) is an important physical quantity for global climate change monitoring. Over the past decades, several LST products have been produced by satellite thermal infrared (TIR) bands or land surface models (LSMs). Recent research has increased the spatio-temporal resolution of LST products to 2 km, hourly based on Geostationary Operational Environmental Satellites (GOES)-R Advanced Baseline Imager (ABI) LST data. The spatial resolution of 2 km, however, is insufficient for monitoring at the regional scale. This paper investigates the feasibility of applying spatio-temporal fusion to generate reliable 100 m, hourly LST data based on fusion of the newly released 2 km, hourly GOES-16 ABI LST and 100 m Landsat LST data. The most accurate fusion method was identified through a comparison between several popular methods. Furthermore, a comprehensive comparison was performed between fusion (with Landsat LST) involving satellite-derived LST (i.e., GOES) and model-derived LSMs (i.e., European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysisv.5 (ERA5)-Land). The spatial and temporal adaptive reflectance fusion model (STARFM) method was demonstrated to be an appropriate method to generate 100 m, hourly data, which produced an average root mean square error (RMSE) of 2.640 K, mean absolute error (MAE) of 2.159 K and average coefficient of determination (R2) of 0.982 referring to thein situtime-series. Furthermore, inheriting the advantages of direct observation, and the fusion of Landsat and GOES for the generation of 100 m, hourly LST produced greater accuracy compared to the fusion of Landsat and ERA5-Land LST in the experiments. The generated 100 m, hourly LST can provide important diurnal data with fine spatial resolution for various monitoring applications.

Soil moisture (SM) plays a significant role in many natural and anthropogenic systems. Thus, accurate assessment of changes in SM globally is of great value, including long-term historical assessment. The European Space Agency established the Climate Change Initiative (CCI) program to produce long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing the pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the SMAP dataset historically (named Hist_SMAP), using the corresponding CCI SM time-series. We assumed that the temporal changes in the SMAP SM dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the temporal (extracted from the CCI SM data), coupled with terrain and location characteristics, and migrated to predict the Hist_SMAP dataset. The availablein-situand the real SMAP data were used as references for validation. Compared with the CCI dataset, the predicted Hist_SMAP dataset is closer to thein-situSM data and the real SMAP data. Moreover, the historical Hist_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset. Thus, the Hist_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset. The new long time-series Hist_SMAP dataset is provided with free access and will be of great value for research and practical application in a range of fields.

Subpixel mapping (SPM) can address the mixed pixel problem by producing land cover maps at a finer spatial resolution than the input images. The Hopfield neural network (HNN) method has shown great advantages in SPM and various extended versions have been developed recently. However, a long-standing issue in the HNN, especially in the multiclass scenario, is its tendency to fall into local optima with vanished gradients, failing to push subpixels to the hard class label of 0 or 1. This can lead to great uncertainties in determining hard class labels and, moreover, the disappearance of many small-sized land cover features and spatial details. In this article, we proposed a hard-constrained HNN (H-HNN) model that introduces hard label-based constraints at both the subpixel and coarse pixel scales. These constraints aim to increase the accuracy of SPM by guiding the optimization process fully toward obtaining hard classification maps at the subpixel level. Experimental evaluations against benchmark methods demonstrated the effectiveness of the H-HNN. The findings reveal that the H-HNN method is a general and robust alternative to the HNN, which can increase the overall accuracy of the SPM results by about 1%. In addition, the H-HNN can effectively reduce the uncertainties by predicting more accurate hard class labels and coarse proportions (with root-mean-square error (RMSE) of the coarse proportions decreased by about 0.015).

Hyperspectral anomaly detection is one of the most important applications in the field of hyperspectral image (HSI) processing. However, hyperspectral anomaly detectors still face several challenges, including the limited use of spatial information and the unavoidable anomaly pollution problem. To cope with the above problems, we propose a hyperspectral anomaly detector, termed COPCRD, which enhances the prevailing collaborative-representation-based detector (CRD) using COPula-based Outlier Detection (COPOD) for background purification and guided filter for spatial difference enhancement. COPCRD mainly solves the anomaly pollution problem and further considers the spatial information of hyperspectral data to enhance discrimination of backgrounds and anomalies. Experimental results on four hyperspectral datasets reveal that the proposed method is more accurate than four state-of-the-art anomaly detectors.

Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing SU methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, the determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this article, a geographically weighted SU (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatiotemporal fusion. SU-GW is a general model suitable for any SU method. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for SU. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatiotemporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatiotemporal fusion, which can be used to update existing methods and future potential versions.

Mixed pixels are a ubiquitous problem in remote sensing images. Spectral unmixing has been used widely for mixed pixel analysis. However, up to now, most spectral unmixing methods require endmembers and cannot consider fully intraclass spectral variation. The recently proposed spatiotemporal spectral unmixing (STSU) method copes with the aforementioned problems through exploitation of the available temporal information. However, this method requires coarse-to-fine spatial image pairs both before and after the prediction time and is, thus, not suitable for important real-time applications (i.e., where the fine spatial resolution data after the prediction time are unknown). In this article, we proposed a real-time STSU (RSTSU) method for real-time monitoring. RSTSU requires only a single coarse-to-fine spatial resolution image pair before, and temporally closest to, the prediction time, coupled with the coarse image at the prediction time, to extract samples automatically to train a learning model. By fully incorporating the multiscale spatiotemporal information, the RSTSU method inherits the key advantages of STSU; it does not need endmembers and can account for intraclass spectral variation. More importantly, RSTSU is suitable for real-time analysis and, thus, facilitates the timely monitoring of land cover changes. The effectiveness of the method was validated by experiments on four Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. RSTSU utilizes and enriches the theory underpinning the advanced STSU method and enhances greatly the applicability of spectral unmixing for time-series data.

Hyperspectral images (HSIs) always contain abundant spectral and spatial information. Most of the existing deep learning-based hyperspectral anomaly detection methods consider spectral differences between the background and anomalies, and the local spatial information is usually ignored. To make complete use of the spatial-spectral information, this paper proposed a Local Spatial-Spectral information-integrated Semi-supervised Two-stream Network (LS3T-Net) for hyperspectral anomaly detection. The two-stream network comprises an adaptive convolution and fully connected network and a variational autoencoder (VAE). The adaptive convolution and fully connected network is used to extract the local spatial features of patches, while the VAE is trained to learn spectral information close to the background pixels. Furthermore, the detection maps from the two-stream network are incorporated through a process combining the benefits of spatial learning and spectral learning. This enhances the ability to separate the background and anomalies and suppress the false alarm. The experimental results for six real HSI datasets reveal that LS3T-Net can produce more accurate detection results than seven popular benchmark methods.

Deep learning methods, especially convolutional neural network (CNN)-based methods, have shown promising performance for hyperspectral image (HSI) change detection (CD). It is acknowledged widely that different spectral channels and spatial locations in input image patches may contribute differently to CD. However, they are treated equally in existing CNN-based approaches. To increase the accuracy of HSI CD, we propose an end-to-end Siamese CNN (SiamNet) with a spectral–spatial-wise attention (SSA-SiamNet) mechanism. The proposed SSA-SiamNet method can emphasize informative channels and locations and suppress less informative ones to refine the spectral–spatial features adaptively. Moreover, in the network training phase, the weighted contrastive loss function is used for more reliable separation of changed and unchanged pixels and to accelerate the convergence of the network. SSA-SiamNet was validated using four groups of bitemporal HSIs. The accuracy of CD using the SSA-SiamNet was found to be consistently greater than for ten benchmark methods.

Deep learning-based methods (e.g., convolutional neural network (CNN)-based methods), have shown increasing potential in hyperspectral image (HSI) change detection (CD). However, the recent advances in CNN-based methods in HSI CD tasks are mostly devoted to designing more complex architectures or adding additional hand-designed blocks. This increases the number of parameters making model training difficult. In this paper, we propose an end-to-end residual self-calibrated network (RSCNet) to increase the accuracy of HSI CD. To fully exploit the spatial information, the proposed RSCNet method adaptively builds inter-spatial and inter-spectral dependencies around each spatial location with fewer extra parameters and reduced complexity. Moreover, the introduced self-calibrated convolution (SCConv) helps to generate more discriminative representations by heterogeneously exploiting convolutional filters nested in the convolutional layer. The designed RSC module can explicitly incorporate richer information by introducing response calibration operation. The experiments on four bi-temporal HSI datasets demonstrated that the proposed RSCNet method is more accurate than ten widely used benchmark methods.

Due to the limitations of hardware technology and budget constraints, there always exists a tradeoff between spatial and spectral resolutions in a hyperspectral image (HSI). Because of the limited spatial resolution, mixed pixels are a common issue in HSIs, and consequently, some targets appear as subpixels. The effectiveness of hyperspectral target detection is affected greatly by the subpixel targets, especially when the size of the targets is small. In this article, we proposed a double dictionary-based nonlinear representation model for hyperspectral subpixel target detection (DDNRTD). DDNRTD represents HSIs with a nonlinear model based on background and target dictionaries, which fully considers the spatial property of background and targets and can separate background and targets reliably, especially for small-sized subpixel targets. In addition, we designed an over-completed background dictionary construction strategy to represent the background part more effectively, which integrates spectral angle distance (SAD) with sparse representation. Experiments on two simulated and five real HSI datasets showed that the proposed DDNRTD method produced more accurate detection results than six state-of-the-art methods.

In recent years, the hyperspectral target detection technique has received widespread attention, and various methods have been proposed. However, most of these methods suffer mainly from two problems. First, the methods perform detection based on the original image, and thus, the targets are easily contaminated by the complex background. Second, a large amount of prior information is usually required, but difficult to obtain. To solve these problems, we proposed a Sparse Tensor model-based Spectral Angle (STSA) detector for hyperspectral target detection. First, with a tensor decomposition model, we obtained a sparse tensor component separated from the original image. Based on the sparse tensor, the detection is less contaminated by the background, and the sparse tensor retains the spatial structure using the developed 3D tensor-based decomposition model. Second, to further enhance the performance, the projected spectral angle method was developed, which only requires a single target spectrum as prior information. Finally, to increase the separation ability between the target and background, we applied a statistical strategy to highlight the target and suppress the background. The experiments were carried out based on four public hyperspectral datasets. The results showed that the proposed STSA method is more accurate than 11 benchmark methods.

Subpixel mapping (SPM) is a technique to tackle the mixed-pixel problem and produces land cover and land use (LCLU) maps at a finer spatial resolution than the original coarse data. However, uncertainty exists unavoidably in SPM, which is an ill-posed downscaling problem. Spatio-temporal SPM methods have been proposed to deal with this uncertainty, but current methods fail to explore fully the information in the time-series images, especially more rapid changes over a short-time interval. In this article, a fast and slow changes constrained spatio-temporal subpixel mapping (FSSTSPM) method is proposed to account for fast LCLU changes over a short time interval and slow changes over a long time interval. Both fast and slow changes-based temporal constraints are proposed and incorporated simultaneously into the FSSTSPM to increase the accuracy of SPM. The proposed FSSTSPM method was validated using two synthetic datasets with various proportion errors. It was also applied to oil-spill mapping using a real PlanetScope-Sentinel-2 dataset and Amazon deforestation mapping using a real Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. The results demonstrate the superiority of FSSTSPM. Moreover, the advantage of FSSTSPM is more obvious with an increase in proportion errors. The concepts of the fast and slow changes, together with the derived temporal constraints, provide a new insight to enhance SPM by taking fuller advantage of the temporal information in the available time-series images.

The scan-line corrector (SLC) of the Landsat 7 ETM+ failed permanently in 2003, resulting in about 22% unscanned gap pixels in the SLC-off images, affecting greatly the utility of the ETM+ data. To address this issue, we propose a spatial-spectral radial basis function (SSRBF)-based interpolation method to fill gaps in SLC-off images. Different from the conventional spatial-only radial basis function (RBF) that has been widely used in other domains, SSRBF also integrates a spectral RBF to increase the accuracy of gap filling. Concurrently, global linear histogram matching is applied to alleviate the impact of potentially large differences between the known and SLC-off images in feature space, which is demonstrated mathematically in this article. SSRBF fully exploits information in the data themselves and is user-friendly. The experimental results on five groups of data sets covering different heterogeneous regions show that the proposed SSRBF method is an effective solution to gap filling, and it can produce more accurate results than six popular benchmark methods.

Optical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with a fine spatial resolution (FR) panchromatic (PAN) image. Pansharpening is a technique applied to such satellite sensor images to generate an FR MS image by injecting spatial detail taken from the FR PAN image while simultaneously preserving the spectral information of MS image. Pansharpening methods are mostly applied on a per-pixel basis and use the PAN image to extract spatial detail. However, many land cover objects in FR satellite sensor images are not illustrated as independent pixels, but as many spatially aggregated pixels that contain important semantic information. In this article, an object-based pansharpening approach, termed object-based area-to-point regression kriging (OATPRK), is proposed. OATPRK aims to fuse the MS and PAN images at the object-based scale and, thus, takes advantage of both the unified spectral information within the CR MS images and the spatial detail of the FR PAN image. OATPRK is composed of three stages: image segmentation, object-based regression, and residual downscaling. Three data sets acquired from IKONOS and Worldview-2 and 11 benchmark pansharpening algorithms were used to provide a comprehensive assessment of the proposed OATPRK approach. In both the synthetic and real experiments, OATPRK produced the most superior pan-sharpened results in terms of visual and quantitative assessment. OATPRK is a new conceptual method that advances the pixel-level geostatistical pansharpening approach to the object level and provides more accurate pan-sharpened MS images.

Spatial downscaling is an ill-posed, inverse problem, and information loss (IL) inevitably exists in the predictions produced by any downscaling technique. The recently popularized area-to-point kriging (ATPK)-based downscaling approach can account for the size of support and the point spread function (PSF) of the sensor, and moreover, it has the appealing advantage of the perfect coherence property. In this article, based on the advantages of ATPK and the conceptualization of IL, an IL-guided image fusion (ILGIF) approach is proposed. ILGIF uses the fine spatial resolution images acquired in other wavelengths to predict the IL in ATPK predictions based on the geographically weighted regression (GWR) model, which accounts for the spatial variation in land cover. ILGIF inherits all the advantages of ATPK, and its prediction has perfect coherence with the original coarse spatial resolution data which can be demonstrated mathematically. ILGIF was validated using two data sets and was shown in each case to predict downscaled images more accurately than the compared benchmark methods.

Many remote sensing soil moisture (SM) products have been developed with global coverage. However, most of them are derived from passive microwave observations with very coarse resolution, greatly constraining the applications at regional scales. To increase the spatial resolution, a downscaling method is developed to downscale the 36-km Soil Moisture Active Passive L3 SM (SMAP SM) product to 1 km using the Moderate Resolution Imaging Spectroradiometer (MODIS) products (8-d land surface temperature, LST, and 16-d normalized difference vegetation index, NDVI). In this method, a linking model is first established between SM and LST and NDVI, and a self-adaptive window method is applied with the use of the geographically weighted regression (GWR) method to obtain an optimal local regression. Then, the uncertainty of the linking model, expressed as the regression residual, is redistributed to fine-resolution pixels to analyze the consistency before and after downscaling. The method was applied to the Iberian Peninsula to produce the 8-d downscaled SM product in 2016. The downscaled SM was validated with the in-situ SM network (REMEDHUS). A good agreement was found between the two data sets, with a correlation coefficient (R) of 0.87 and an unbiased root-mean-squared error (ubRMSE) of 0.043 m3/m3at a network level. At station level, the R is larger than 0.6 for all the REMEDHUS stations, with an ubRMSE smaller than 0.06 m3/m3. The evaluation indicates the good potential of the proposed method in the SM downscaling, which achieves a robust consistency and provides rich spatial information while maintaining good accuracy.

Sentinel-2 is a wide-swath and fine spatial resolution satellite imaging mission designed for data continuity and enhancement of the Landsat and other missions. The Sentinel-2 data are freely available at the global scale, and have similar wavelengths and the same geographic coordinate system as the Landsat data, which provides an excellent opportunity to fuse these two types of satellite sensor data together. In this paper, a new approach is presented for the fusion of Landsat 8 Operational Land Imager and Sentinel-2 Multispectral Imager data to coordinate their spatial resolutions for continuous global monitoring. The 30 m spatial resolution Landsat 8 bands are downscaled to 10 m using available 10 m Sentinel-2 bands. To account for the land-cover/land-use (LCLU) changes that may have occurred between the Landsat 8 and Sentinel-2 images, the Landsat 8 panchromatic (PAN) band was also incorporated in the fusion process. The experimental results showed that the proposed approach is effective for fusing Landsat 8 with Sentinel-2 data, and the use of the PAN band can decrease the errors introduced by LCLU changes. By fusion of Landsat 8 and Sentinel-2 data, more frequent observations can be produced for continuous monitoring (this is particularly valuable for areas that can be covered easily by clouds, thereby, contaminating some Landsat or Sentinel-2 observations), and the observations are at a consistent fine spatial resolution of 10 m. The products have great potential for timely monitoring of rapid changes.

Forest mapping from satellite sensor imagery provides important information for the timely monitoring of forest growth and deforestation, bioenergy potential assessment, and modeling of carbon flux, among others. Due to the daily global revisit rate and wide swath width, MODerate-resolution Imaging Spectroradiometer (MODIS) images are used commonly for satellite-derived forest mapping at both regional and global scales. However, the spatial resolution of MODIS images is too coarse to observe fine spatial variation in forest cover. The last few decades have seen the production of several fine-spatial-resolution satellite-derived global forest cover maps, such as Hansen’s global tree canopy cover map of 2000, which includes abundant spectral, temporal, and spatial prior information about forest cover at a fine spatial resolution. In this paper, a novel learning-based spatial–temporal superresolution mapping approach is proposed to integrate both current MODIS images and prior maps of Hansen’s tree canopy cover, to map present forest cover with a fine spatial resolution. The novel approach is composed of three main stages: 1) automatic generation of 240-m forest proportion images from both 240- and 480-m MODIS images using a nonlinear learning-based spectral unmixing method; 2) downscaling the 240-m forest proportion images to 30 m to predict the class possibilities at the subpixel scale using a temporal-example learning-based downscaling method; and 3) final production of the fine-spatial-resolution forest map by solving a regularization-based optimization problem. The novel approach produced more accurate fine-spatial-resolution forest cover maps in terms of both visual and quantitative evaluation than traditional pixel-based classification and the latest subpixel based superresolution mapping methods. The results show the great efficiency and potential of the novel approach for producing fine-spatial-resolution forest maps from MODIS images.

This paper presents a novel adaptive fuzzy local information c-means (ADFLICM) clustering approach for remotely sensed imagery classification by incorporating the local spatial and gray level information constraints. The ADFLICM approach can enhance the conventional fuzzy c-means algorithm by producing homogeneous segmentation and reducing the edge blurring artifact simultaneously. The major contribution of ADFLICM is use of the new fuzzy local similarity measure based on pixel spatial attraction model, which adaptively determines the weighting factors for neighboring pixel effects without any experimentally set parameters. The weighting factor for each neighborhood is fully adaptive to the image content, and the balance between insensitiveness to noise and reduction of edge blurring artifact to preserve image details is automatically achieved by using the new fuzzy local similarity measure. Four different types of images were used in the experiments to examine the performance of ADFLICM. The experimental results indicate that ADFLICM produces greater accuracy than the other four methods and hence provides an effective clustering algorithm for classification of remotely sensed imagery.

Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.

The availability of the panchromatic (PAN) band in remote sensing images gives birth to so-called image fusion techniques for increasing the spatial resolution of images to that of the PAN band. The spatial resolution of such spatially sharpened images, such as for the MODIS and Landsat sensors, however, may not be sufficient to provide the required detailed land-cover/land-use information. This paper proposes an area-to-point regression kriging (ATPRK)-based geostatistical solution to increase the spatial resolution of remote sensing images beyond that of any input images, including the PAN band. The new approach is a two-stage approach, including covariate downscaling and ATPRK-based image fusion. The new approach treats the PAN band as the covariate and takes advantages of its textural information. It explicitly accounts for the size of support, spatial correlation, and the point spread function of the sensor and has the characteristic of perfect coherence with the original coarse data. Moreover, the new downscaling approach can be extended readily by incorporating other ancillary information. The proposed approach was examined using both Landsat and MODIS images. The results show that it can produce more accurate sharpened images than four benchmark approaches.

The kernel function plays an important role in machine learning methods such as the support vector machine. In this letter, a new kernel framework is developed for hyperspectral image classification. In contrast to existing composite kernels constructed via a linearly weighted combination, the multiple-mapping kernel proposed in this letter is obtained through repeated nonlinear mappings. Experiments indicate that the proposed multiple-mapping kernel framework (MMKF) is effective for hyperspectral image classification. Compared to the single kernel methods, the MMKF tends to be more advantageous in terms of classification accuracy, particularly for the situation with a small-size training set.

Object extraction from remote sensing images has long been an intensive research topic in the field of surveying and mapping. Most past methods are devoted to handling just one type of object, and little attention has been paid to improving the computational efficiency. In recent years, level set evolution (LSE) has been shown to be very promising for object extraction in the field of image processing because it can handle topological changes automatically while achieving high accuracy. However, the application of state-of-the-art LSEs is compromised by laborious parameter tuning and expensive computation. In this paper, we proposed two fast LSEs for man-made object extraction from high spatial resolution remote sensing images. We replaced the traditional mean curvature-based regularization term by a Gaussian kernel, and it is mathematically sound to do that. Thus, we can use a larger time step in the numerical scheme to expedite the proposed LSEs. Compared with existing methods, the proposed LSEs are significantly faster. Most importantly, they involve much fewer parameters while achieving better performance. Their advantages over other state-of-the-art approaches have been verified by a range of experiments.

Presents correctons made to the paper, "Extracting man-made objects from high spatial resolution remote sensing images via fast level set evolutions" (Li, Z., et al., IEEE Trans. Geosci. Remote Sens., vol. 53, no. 2, pp. 883–899, Feb. 2015).

Indicator cokriging (ICK) has been shown to be an effective subpixel mapping (SPM) algorithm. It is noniterative and involves few parameters. The original ICK-based SPM method, however, requires the semivariogram of land cover classes from prior information, usually in the form of fine spatial resolution training images. In reality, training images are not always available, or laborious work is needed to acquire them. This paper aims to seek spatial structure information for ICK when such prior land cover information is not obtainable. Specifically, the fine spatial resolution semivariogram of each class is estimated by the deconvolution process, taking the coarse spatial resolution semivariogram extracted from the class proportion image as input. The obtained fine spatial resolution semivariogram is then used to estimate class occurrence probability at each subpixel with the ICK method. Experiments demonstrated the feasibility of the proposed ICK with the deconvolution approach. It obtains comparable SPM accuracy to ICK that requires semivariogram estimated from fine spatial resolution training images. The proposed method extends ICK to cases where the prior spatial structure information is unavailable.

Due to rapid changes on the Earth's surface, it is important to perform land cover change detection (CD) at a fine spatial and fine temporal resolution. However, remote sensing images with both fine spatial and temporal resolutions are commonly not available or, where available, may be expensive to obtain. This paper attempts to achieve fine spatial and temporal resolution land cover CD with a new computer technology based on subpixel mapping (SPM): The fine spatial resolution land cover maps (FRMs) are first predicted through SPM of the coarse spatial but fine temporal resolution images, and then, subpixel resolution CD is performed by comparison of class labels in the SPM results. For the first time, five fast SPM algorithms, including bilinear interpolation, bicubic interpolation, subpixel/pixel spatial attraction model, Kriging, and radial basis function interpolation methods, are proposed for subpixel resolution CD. The auxiliary information from the known FRM on one date is incorporated in SPM of coarse images on other dates to increase the CD accuracy. Based on the five fast SPM algorithms and the availability of the FRM, subpixels for each class are predicted by comparison of the estimated soft class values at the target fine spatial resolution and borrowing information from the FRM. Experiments demonstrate the feasibility of the five SPM algorithms using FRM in subpixel resolution CD. They are fast methods to achieve subpixel resolution CD.

This letter presents a two-step method for urban main road extraction from high-resolution remotely sensed imagery by integrating spectral-spatial classification and shape features. In the first step, spectral-spatial classification segments the imagery into two classes, i.e., the road class and the nonroad class, using path openings and closings. The local homogeneity of the gray values obtained by local Geary's C is then fused with the road class. In the second step, the road class is refined by using shape features. The experimental results indicated that the proposed method was able to achieve a comparatively good performance in urban main road extraction.

In this letter, multiple subpixel shifted images (MSIs) were utilized to increase the accuracy of subpixel mapping (SPM), based on the fast bilinear and bicubic interpolation. First, each coarse spatial resolution image of MSI is soft classified to obtain class fraction images. Using bilinear or bicubic interpolation, all fraction images of MSI are upsampled to the desired fine spatial resolution. The multiple fine spatial resolution images for each class are then integrated. Finally, the integrated fine spatial resolution images are used to allocate hard class labels to subpixels. Experiments on two remote sensing images showed that, with MSI, both bilinear and bicubic interpolation-based SPMs are more accurate. The new methods are fast and do not need any prior spatial structure information.

The soft-then-hard subpixel mapping (STHSPM) algorithm is a type of subpixel mapping (SPM) algorithm consisting of soft class value (between 0 and 1) estimation and hard class allocation for each subpixel. This letter presents a new class allocation method for STHSPM algorithm. As an extension of our previous work in which subpixels for classes are decided in units of classes (UOC), the new approach, named adaptive UOC (AUOC), improves UOC with adaptive visiting order of classes. In AUOC, the visiting order of classes within each coarse pixel is determined based on the local structure rather than the global structure in UOC. Experiments on three remote sensing images show that AUOC is able to improve UOC in terms of SPM accuracy, particularly for SPM with small zoom factors.

There is a type of algorithm for subpixel mapping (SPM), namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at the subpixel scale level and then allocates classes (i.e., hard attribute values) for subpixels according to the soft attribute values. This paper presents a novel class allocation approach for STHSPM algorithms, which allocates classes in units of class (UOC). First, a visiting order for all classes is predetermined, and the number of subpixels belonging to each class is calculated using coarse fraction data. Then, according to the visiting order, the subpixels belonging to the being visited class are determined by comparing the soft attribute values of this class, and the remaining subpixels are used for the allocation of the next class. The process is terminated when each subpixel is allocated to a class. UOC was tested on three remote sensing images with five STHSPM algorithms: back-propagation neural network, Hopfield neural network, subpixel/pixel spatial attraction model, kriging, and indicator cokriging. UOC was also compared with three existing allocation methods, i.e., linear optimization technique (LOT), sequential assignment in units of subpixel (UOS), and a method that assigns subpixels with highest soft attribute values first (HAVF). Results show that for all STHSPM algorithms, UOC is able to produce higher SPM accuracy than UOS and HAVF; compared with LOT, UOC is able to achieve at least comparable accuracy but needs much less computing time. Hence, UOC provides an effective and real-time class allocation method for STHSPM algorithms.

Iterative Error Analysis (IEA) widely known as a good endmember extraction (EE) algorithm. It is robust, automatic and free of data transformation. However, IEA is faced with risks in some cases due to the sole use of unmxing distance, and its speed is lowed down by the iteration-based linear spectral mixture analysis (LSMA). To make IEA algorithm faster and more robust, its modified version is proposed based on two substitutions. One is substituting integrated distance for unmxing distance, which makes the algorithm more robust. The other is substituting SVM-based multiple endmember spectral mixture analysis (MESMA) for iteration-based LSMA, which speeds up the algorithm greatly. Experiments show that the modified IEA algorithm outperforms original one in terms of both robustness and running speed.

Spectral unmixing has been an important technique for hyperspectral imagery processing. In traditional spectral unmixing methods that are based on the linear spectral mixture model (LSMM), unmixing accuracy is limited by the inherent deficiency of the model. It was shown that the support vector machine (SVM) can be extended for spectral unmixing, based on the advantage that the SVM model can accommodate the variations within a relative pure class by using multiple pure samples instead of a single endmember for one class. In the SVM model, class label errors are considered in constraints. However, the errors concerned in spectral unmixing are the unmixing residue instead of the class label ones. This letter presents a method of imposing unmixing residue constraints on the least squares SVM unmixing model. The related problems, including deducing the closed-form solution and substituting the single endmember for multiple ones, were studied together. Experiments showed that the new SVM model was superior to the original SVM as well as the traditional LSMM in terms of unmixing residue, fractional abundance, and confused matrix criterions.

Subpixel mapping (SPM) is a promising technique to increase the spatial resolution of land cover maps. Markov random field (MRF)-based SPM has the advantages of considering spatial and spectral constraints simultaneously. In the conventional MRF, only the spectral information of one observed coarse spatial resolution image is utilized, which limits the SPM accuracy. In this letter, supplementary information from subpixel shifted remote sensing images (SSRSI) is used with MRF to produce more accurate SPM results. That is, spectral information from SSRSI is incorporated into the likelihood energy function of MRF to provide multiple spectral constraints. Simulated and real images were tested with the subpixel/pixel spatial attraction model, Hopfield neural networks (HNNs), HNN with SSRSI, image interpolation then hard classification, conventional MRF, and proposed MRF with SSRSI based SPM methods. Results showed that the proposed method can generate the most accurate SPM results among these methods.

Spectral unmixing is one of the important techniques for hyperspectral data processing. The analysis of spectral mixing is often based on a linear, fully constrained (FC) (i.e., nonnegative and sum-to-one mixture proportions), and least squares criterion. However, the traditional iterative processing of FC least squares (FCLS) linear spectral mixture analysis (LSMA) (FCLS-LSMA) is of heavy computational burden. Recently developed geometric LSMA methods decreased the complexity to some degree, but how to further reduce the computational burden and completely meet the FCLS criterion of minimizing the unmixing residual needs to be explored. In this paper, a simple distance measure is proposed, and then, a new geometric FCLS-LSMA method is constructed based on the distance measure. The method is in line with the FCLS criterion, free of iteration and dimension reduction, and with very low complexity. Experimental results show that the proposed method can obtain the same optimal FCLS solution as the traditional iteration-based FCLS-LSMA, and it is much faster than the existing spectral unmixing methods, particularly the traditional iteration-based method.

In this paper, a new sub-pixel mapping algorithm is proposed based on sub-pixel/sub-pixel spatial attraction model (SSSAM). Different from the original sub-pixel/pixel spatial attraction model (SPSAM), the SSSAM considers the spatial distribution of each sub-pixel within neighbor pixels, when calculating the spatial attractions for sub-pixels within the centre pixel. Then the attractions are used to determine the class values of these sub-pixels. Two experiments on three artificial images and one real remote sensing image are processed. Both of the results show that compared with traditional SPSAM, the proposed method can produce sub-pixel mapping results with higher accuracy.