Global evaluation of SMAP/Sentinel-1 soil moisture products

MAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.Peer ReviewedPostprint (published version

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

Point clouds with ever-increasing volume are regular data in 3D city modelling, in which building reconstruction is a significant part. The photogrammetric point cloud, generated from UAS (Unmanned Aerial System) imagery, is a novel type of data in building reconstruction. Its positive characteristics, alongside its challenging qualities, provoke discussions on this theme of research. In this paper, patch-wise detection of the points of window frames on facades and roofs are undertaken using this kind of data. A density-based multi-scale filter is devised in the feature space of normal vectors to globally handle the matter of high volume of data and to detect edges. Color information is employed for the downsized data to remove the inner clutter of the building. Perceptual organization directs the approach via grouping and the Gestalt principles, to segment the filtered point cloud and to later detect window patches. The evaluation of the approach displays a completeness of 95% and 92%, respectively, as well as a correctness of 95% and 96%, respectively, for the detection of rectangular and partially curved window frames in two big heterogeneous cluttered datasets. Moreover, most intrusions and protrusions cannot mislead the window detection approach. Several doors with glass parts and a number of parallel parts of the scaffolding are mistaken as windows when using the large-scale object detection approach due to their similar patterns with window frames. Sensitivity analysis of the input parameters demonstrates that the filter functionality depends on the radius of density calculation in the feature space. Furthermore, successfully employing the Gestalt principles in the detection of window frames is influenced by the width determination of window partitioning

Spatial-spectral classification of hyperspectral images based on multiple fractal-based features

Hyperspectral images are efficient tools for discriminating different types of earth's surface materials. Spectral features traditionally perform classification of hyperspectral images, but different studies have proved the efficiency of spatial features as complementary information in increasing the classification accuracy. The fractal geometry can be regarded as a potent tool for spatial data modeling. This study proposes a new classification method based on the integration of fractal and spectral features. For this purpose, three groups of fractal features, including mono-fractal, lacunarity and multi-fractal features are generated from the first few principal components of the hyperspectral image in different window sizes. These features are later stacked with spectral features and then fed to support vector machines classifier. The experiments are conducted on two real hyperspectral images Indian Pines, Pavia University. Final classification accuracy proved the superiority of the proposed classification method against the other competitive spatial-spectral methods

The synergistic use of microwave coarse-scale measurements and two adopted high-resolution indices driven from long-term T-V scatter plot for fine-scale soil moisture estimation

In an attempt to retrieve soil moisture content (SMC) from remote sensing techniques, this article suggests and evaluates a developed approach that overcomes three of the most fundamental limitations of the temperature–vegetation (T-V) scatter plot method that are (1) low accuracy of the T-V scatter plot method in cases that the corresponding scatter plot and its wet and dry edges are not formed appropriately, (2) incompatibility of the T-V index maps in different days, and (3) inability to obtain the absolute SMC values from the T-V indices. The research consists of three main steps. In the first step, the measurements of eight global in situ SMC networks were applied to select the most appropriate and accurate microwave remote sensing mission among from Advanced Microwave Scanning Radiometer 2, Soil Moisture and Ocean Salinity, and Soil Moisture Active Passive. The results outlined the superiority of SMAP’s products in terms of RMSE and correlation coefficient (root mean square difference (RMSE) = 0.3–0.12 m3/m3 and R = 0.53–0.93). At the second step, the 1-year T-V scatter plot was formed and then, two adopted soil moisture indices, namely the annual soil moisture index (ASMIHR) and daily soil moisture index (DSMIHR), were extracted. The ASMIHR was driven from the annual wet and dry edges and a novel concept called “co-moisture line” was introduced to obtain the DSMIHR. In the third step of the proposed method, Disaggregation based on Physical and Theoretical scale Change was applied as the downscaling algorithm with a novelty to identify the parameter of partial derivative of microwave data relative to SMC indices using the relationship between ASMIHR and coarse-scale SMAP pixels. Across four SMAP coarse-scale pixels located in the study area, the corresponding parameter was obtained with the average correlation coefficient 0.7. This step was followed by integrating DSMIHR and SMAP products to provide absolute SMC values at intermediate spatial resolution. The proposed method was evaluated on the agricultural site of Soil Moisture Active Passive Validation Experiment 2016-Manitoba. The ultimate results of the proposed method in terms of absolute SMC values proved promising consistency with field measurements (R = 0.66 and no-bias RMSE = 0.06 m3/m3)

A Robust Fuzzy Neural Network Model for Soil Lead Estimation from Spectral Features

Soil lead content is an important parameter in environmental and industrial applications. Chemical analysis, the most commonly method for studying soil samples, are costly, however application of soil spectroscopy presents a more viable alternative. The first step in the method is usually to extract some appropriate spectral features and then regression models are applied to these extracted features. The aim of this paper was to design an accurate and robust regression technique to estimate soil lead contents from laboratory observed spectra. Three appropriate spectral features were selected according to information from other research as well as the spectrum interpretation of field collected soil samples containing lead. These features were then applied to common Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR) and Neural Network (NN) regression models. Results showed that although NN had adequate accuracy, it produced unstable results (i.e., variation of response in different runs). This problem was addressed with application of a Fuzzy Neural Network (FNN) with a least square training strategy. In addition to the stabilized and unique response, the capability of the proposed FNN was proved in terms of regression accuracy where a Ratio of Performance to Deviation (RPD) of 8.76 was achieved for test samples

Shadow-Based Hierarchical Matching for the Automatic Registration of Airborne LiDAR Data and Space Imagery

The automatic registration of LiDAR data and optical images, which are heterogeneous data sources, has been a major research challenge in recent years. In this paper, a novel hierarchical method is proposed in which the least amount of interaction of a skilled operator is required. Thereby, two shadow extraction schemes, one from LiDAR and the other from high-resolution satellite images, were used, and the obtained 2D shadow maps were then considered as prospective matching entities. Taken as the base, the reconstructed LiDAR shadows were transformed to image shadows using a four-step hierarchical method starting from a coarse 2D registration model and leading to a fine 3D registration model. In the first step, a general matching was performed in the frequency domain that yielded a rough 2D similarity model that related the LiDAR and image shadow masks. This model was further improved by modeling and compensating for the local geometric distortions that existed between the two heterogeneous data sources. In the third step, shadow masks, which were organized as segmented matched patches, were the subjects of a coinciding procedure that resulted in a coarse 3D registration model. In the last hierarchical step, that model was ultimately reinforced via a precise matching between the LiDAR and image edges. The evaluation results, which were conducted on six datasets and from different relative and absolute aspects, demonstrated the efficiency of the proposed method, which had a very promising accuracy on the order of one pixel

Enhancing the Locational Perception of Soft Classified Satellite Imagery Through Evaluation and Development of the Pixel Swapping Technique

Spatial component is the key and most likely the first element of map making so that accurate spatial information improves the locational perception of map users. In this regard, soft classified satellite imagery conveys class proportions within pixels; however spatial distribution of the sub-pixels remains unknown. So, different visualization techniques (e.g. pie-chart representation of the proportions) are suggested to communicate the detailed land cover information. However, in each of which, the perception of actual spatial location of sub-pixels is definitely difficult for map users. Recently, the Super Resolution Mapping (SRM) techniques have been developed for optimization of the sub-pixels spatial arrangement based on the concepts of spatial dependency. These are relatively new methods which a comprehensive study on their performance and also their decisive parameters is a central issue for sub-pixel land cover mapping. In this research, the binary Pixel Swapping (PS) algorithm, as a prominent SRM algorithm, is developed for multivariate land cover mapping and the accuracy of the proposed method is evaluated in two procedures of independent and dependent of the soft classification error. Likewise, the impact of some parameters (e.g. zoom factor, neighborhood level and weighting function) is investigated on the efficiency of the algorithm. According to the results, the overall accuracy of the PS technique is extremely dependent on the accuracy of its input data (outputs of the soft classification). Furthermore, as a key result of this chapter, it is indicated that by increasing the zoom factor, the overall accuracy of the algorithm decreases. Also, the second level of neighborhood and inverse/square inverse distance functions has demonstrated the highest accuracies. Considering lower values than 5 for zoom factor, overall accuracy of the algorithm is determined higher than 90 % in procedure of optimizing the sub-pixels spatial arrangement

Epipolar Resampling of Cross-Track Pushbroom Satellite Imagery Using the Rigorous Sensor Model

Epipolar resampling aims to eliminate the vertical parallax of stereo images. Due to the dynamic nature of the exterior orientation parameters of linear pushbroom satellite imagery and the complexity of reconstructing the epipolar geometry using rigorous sensor models, so far, no epipolar resampling approach has been proposed based on these models. In this paper for the first time it is shown that the orientation of the instantaneous baseline (IB) of conjugate image points (CIPs) in the linear pushbroom satellite imagery can be modeled with high precision in terms of the rows- and the columns-number of CIPs. Taking advantage of this feature, a novel approach is then presented for epipolar resampling of cross-track linear pushbroom satellite imagery. The proposed method is based on the rigorous sensor model. As the instantaneous position of sensors remains fixed, the digital elevation model of the area of interest is not required in the resampling process. Experimental results obtained from two pairs of SPOT and one pair of RapidEye stereo imagery with different terrain conditions shows that the proposed epipolar resampling approach benefits from a superior accuracy, as the remained vertical parallaxes of all CIPs in the normalized images are close to zero

Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2

Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands

Variable environmental conditions cause different spectral responses of scene endmembers. Ignoring these variations affects the accuracy of fractional abundances obtained from linear spectral unmixing. On the other hand, the correlation between the bands of hyperspectral data is not considered by conventional methods developed for dealing with spectral variability. In this paper, a novel approach is proposed to simultaneously mitigate spectral variability and reduce correlation among different endmembers in hyperspectral datasets. The idea of the proposed method is to utilize the angular discrepancy of bands in the Prototype Space (PS), which is constructed using the endmembers of the image. Using the concepts of PS, in which each band is treated as a space point, we proposed a method to identify independent bands according to their angles. The proposed method comprised two main steps. In the first step, which aims to alleviate the spectral variability issue, image bands are prioritized based on their standard deviations computed over some sets of endmembers. Independent bands are then recognized in the prototype space, employing the angles between the prioritized bands. Finally, the unmixing process is done using the selected bands. In addition, the paper presents a technique to form a spectral library of endmembers’ variability (sets of endmembers). The proposed method extracts endmembers sets directly from the image data via a modified version of unsupervised spatial–spectral preprocessing. The performance of the proposed method was evaluated by five simulated images and three real hyperspectral datasets. The experiments show that the proposed method—using both groups of spectral variability reduction methods and independent band selection methods—produces better results compared to the conventional methods of each group. The improvement in the performance of the proposed method is observed in terms of more appropriate bands being selected and more accurate fractional abundance values being estimated