Decomposition Of Mixed Pixels Research Articles

Sentinel-2A Image Reflectance Simulation Method for Estimating the Chlorophyll Content of Larch Needles with Pest Damage

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management.

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.

Evolution stage identification and spatial pattern analysis of Nitraria tangutorum nebkhas based on Gaofen-2 satellite data

The quantitative extraction and evolution stage identification of the Nitraria tangutorum nebkhas are the basis for the restoration of regional plants and the reconstruction of degraded ecosystems. In this paper, the Nitraria tangutorum nebkha in Dengkou County of China was taken as the research object. Through the spectral and texture information of Gaofen-2 satellite image, the quantitative extraction of Nitraria tangutorum nebkha area and coverage information was completed using methods of gray threshold method, mathematical morphology, FCLSU mixed pixel decomposition, kernel density spatial analysis; the current evolution stage of the Nitraria tangutorum nebkha was identified, and their spatial distribution characteristics were analyzed. The results showed that: (1) The user accuracy and mapping accuracy of Nitraria tangutorum nebkha extracted from Random Forest combined with object-oriented classification method were up to 90.32%. (2) The method proposed can achieve an accuracy of 93.76% in extracting the spatial position of Nitraria tangutorum nebkhas. (3) The evolution of Nitraria tangutorum nebkhas can be divided into three stages: embryonic or developmental stage, stable stage, and declining stage, with a proportion of 60.70%, 20.97%, and 18.33%, respectively; The Nitraria tangutorum nebkhas in the study area is mainly in their embryonic or developmental stage, and the proportion of Nitraria tangutorum nebkhas in the declining stage is also large. It can provide technical and theoretical support for the precise extraction of nebkhas in arid and semi-arid desert areas, the identification of their current evolutionary stages, and the study of their spatial distribution patterns.

Automatic extraction method of green tide based on mixed pixel decomposition feedback adjustment

Automatic extraction method of green tide based on mixed pixel decomposition feedback adjustment

Integrated vegetation cover of typical steppe in China based on mixed decomposing derived from high resolution remote sensing data

Grasslands represent the largest ecosystem in China, accurate and efficient extraction of its integrated vegetation cover (IVC) plays a crucial role in supporting policy decisions. This study presented a method for grassland monitoring via IVC derived from high-resolution satellite data. Taking the multispectral data of Gaofen-1 (GF-1) and Gaofen-6 (GF-6) with 16 m resolution as the main data source, vegetation cover of six representative regions was assessed based on mixed-pixel decomposition model. Using grassland vegetation cover and ratio of grassland area, the IVC in each site was calculated and verified against ground-measured sample data. The results showed that the IVC of grassland was closely related to vegetation habitat driven by regional hydrothermal regime. Yichang grassland, dominated with warm-temperate shrub tussock type, had the highest IVC (80.06 %) due to its favorable hydrothermal conditions. For the main grassland types in Hulunbuir and Gansu Province (temperate meadow steppe and temperate typical steppe), the IVC was 79.38 % and 58.46 %, respectively. In both Xilin-Gol and Nagqu, vegetation cover decreased gradually from east to west, and the IVC was merely 42.83 % and 42.61 %, respectively. Both regions are endowed with less hydrothermal resources to different degrees. Alxa, with a predominately temperate desert landscape, had the lowest IVC of 15.58 % where precipitation is extremely scarce. Based on the grass species of measured samples, the dominant species and biodiversity of different grassland types in Gansu Province and Hulunbuir Municipality of Inner Mongolia Autonomous Region were analyzed. The results showed that the meadow grassland has the richest biodiversity. The temperate mountain meadows in Gansu Province have a high species diversity, with a total of 90 grass species, and the lowland meadows in Hulunbuir have a total of 49 grass species. This study utilizes high-resolution data to conduct large-scale vegetation monitoring, which is a viable alternative for efficient assessment of steppe ecology.

Impervious Surface Area Patterns and Their Response to Land Surface Temperature Mechanism in Urban–Rural Regions of Qingdao, China

The expansion of impervious surface area (ISA) in megacities of China often leads to land surface temperature (LST) aggregation effects, which affect living environments by impacting thermal comfort levels, thus becoming an issue of public concern. However, from an urban–rural synchronous comparison perspective, the study of LST responses to ISA changes is still lacking in the central coastal megalopolises of China. To solve this issue, a collaborative methodology of artificial digitization—fully constrained least squares mixed pixel decomposition—split-window algorithm—PCACA model was established for Qingdao using land use dataset and remote sensing images. The conclusions are below. Long time series of land use monitoring indicated that the expansion ratios of urban and rural areas were 131.29% and 43.42% in the past 50 years (i.e., from 1970 to 2020). Within urban and rural areas, a synchronous ISA increase was observed, with ratios of +9.14% (140.55 km2) and +7.94% (28.04 km2), respectively. Higher ratios and area changes were found in the urban regions, and a similar ISA change pattern in both urban and rural regions was captured by the ISA horizontal epitaxial expansion and vertical density enhancement. Further, the horizontal gradient effect displayed that the mean LSTs were 28.75 °C, 29.77 °C and 31.91 °C in the urban areas and 28.73 °C, 29.66 °C and 31.65 °C in the rural areas in low-, medium-, and high-density ISAs. The vertical density effect showed that the LST change was 1.02 °C and 2.14 °C in the urban areas but 0.93 °C and 1.99 °C in the rural areas during the ISA-density transition from low- to medium- and from medium- to high-density, respectively. Potential surface thermal indicators were assessed, and the urban regions displayed higher sensible heat flux (280.13 W/m2) compared to the rural regions (i.e., 274.76 W/m2). The mechanism effect of the ISA changes on LST in the urban and rural regions was revealed. These findings form a new comparative perspective of the urban–rural synchronous change in the central coastal megalopolis of China and can provide a practical reference for relevant studies.

Analysis on the Rationality of Urban Green Space Distribution in Hangzhou City Based on GF-1 Data

With its ecological, economic, and social benefits, urban green space (UGS) plays an important role in urban planning. Accordingly, it is also an important indicator in the evaluation of urban liveability. However, the extraction and statistical analysis of UGS are difficult because urban land use involves complex types and UGS exhibits fragmented distribution and common vegetation extraction models such as the NDVI model and pixel bipartite model. In addition, there are few studies that analyze UGS in Hangzhou with a pixel decomposition model. Therefore, applying the mixed pixel decomposition model with GF-1 data, the following three objectives were set in this study: (1) analyzing the temporal changes of UGS in Shangcheng District, Hangzhou from 2018 to 2020; (2) analyzing the spatial distribution characteristics of UGS in the six main urban areas of Hangzhou in 2020; (3) analyzing the rationality and influencing factors of UGS distribution in Hangzhou. In Shangcheng District, the overall UGS area increased from 2018 to 2020 due to the increase in forest area rather than grassland area. As for the main built-up area in Hangzhou, medium and high coverage of UGS were primarily observed, with an overall high level of greening and a relatively uniform vegetation cover. Only a few areas showed very low UGS coverage. Some differences were observed among administrative regions under the influence of topography, but the overall coverage is high. At the same time, the distribution of UGS in Hangzhou is closely related to policy guidance, the needs of urban residents, and the requirements of economic development. This research not only can provide a new way to analyze UGS features in Hangzhou but also provides scientific guidance for governments in urban planning.

LANDSAT multispectral image analysis of bedrock exposure rate in highly heterogeneous karst areas through mixed pixel decomposition considering spectral variability

AbstractMixed pixels and spectral variations commonly occur in images of karst areas because these areas have rugged topography and high surface heterogeneity. Consequently, the bedrock exposure rate cannot be accurately estimated using the existing rock index method based on remote sensing and the fixed endmember mixed pixel decomposition model. In order to solve this problem, using LANDSAT operational land imager/thematic mapper (OLI/TM) images as the data source, this study estimated the bedrock exposure rate based on a generalized linear mixed model (GLMM) considering spectral variability. In addition, this approach was compared with existing commonly used methods for estimating bedrock exposure rate. The results show that: (1) GLMM showed the highest performance in estimating bedrock exposure rate, with a total accuracy of 88.05% and a kappa coefficient of 0.845, and the accuracy exceeded 70% for different levels of bedrock exposure rate. However, the total accuracy of other commonly used bedrock exposure methods is was below 66%, being higher only in areas with bedrock exposure rates less than 20% and more than 70%. (2) By applying each method to different terrain scenes, GLMM was found to be more stable than other methods, and the estimated bedrock exposure rate is the closest to the ground reference value with a root mean square error less than 0.093. Other methods, including fully constrained least squares unmixing (FCLSU) and karst bare‐rock index (KBRI), have a certain gap in different scenes, and their accuracy is not high. (3) Without any ground reference data, the accuracy of the direct unmixing result of GLMM is very close to that calculated by the regression model, and the accuracy difference among different grades of bedrock exposure rate is less than 5%, thus outperforming the other methods. GLMM can effectively estimate the bedrock exposure rate at different times using different data. Therefore, GLMM has great potential in extracting rocky desertification information in karst areas. It can also be a reference method for the rapid, accurate, and long‐term evaluation of rocky desertification in karst areas.

Retrieval of Fractional Vegetation Cover from Remote Sensing Image of Unmanned Aerial Vehicle Based on Mixed Pixel Decomposition Method

FVC (fractional vegetation cover) is highly correlated with wheat plant density in the reviving period, which is an important indicator for conducting variable-rate nitrogenous topdressing. In this study, with the objective of improving inversion accuracy of wheat plant density, an innovative approach of retrieval of FVC values from remote sensing images of a UAV (unmanned aerial vehicle) was proposed based on the mixed pixel decomposition method. Firstly, remote sensing images of an experimental wheat field were acquired by using a DJI Mini UAV and endmembers in the image were identified. Subsequently, a linear unmixing model was used to subdivide mixed pixels into components of vegetation and soil, and an abundance map of vegetation was acquired. Based on the abundance map of vegetation, FVC was calculated. Consequently, a linear regression model between the ground truth data of wheat plant density and FVC was established. The coefficient of determination (R2), RMSE (root mean square error), and RRMSE (Relative-RMSE) of the inversion model were calculated as 0.97, 1.86 plants/m2, and 0.677%, which indicates strong correlation between the FVC of mixed pixel decomposition method and wheat plant density. Therefore, we can conclude that the mixed pixel decomposition model of the remote sensing image of a UAV significantly improved the inversion accuracy of wheat plant density from FVC values, which provides method support and basic data for variable-rate nitrogenous fertilization in the wheat reviving period in the manner of precision agriculture.

Application of linear spectral mixed pixel decomposition technology in extracting the spatial distribution of illicit opium poppy cultivation

To determine the spatial distribution of illicit poppy cultivation hyperspectral image processing is employed. The real-world scenario of poppy leaves mixed with other plant leaves is simulated in the laboratory. The linear spectral mixed pixel decomposition method is employed and its application in determining the spatial distribution of poppy cultivation is studied. For this purpose, the method of interactive endmember extraction is used and the object types are limited to: poppy leaves, soil and other plant leaves. This aims at reducing systematic errors as well as human errors that are caused by complex classifaction steps and the large number of samples. The results show that the linear spectral mixed pixel decomposition method successfully separates the target (poppy leaves) from a complex background. The distribution of poppy leaves is clearly visible.

Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction

Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods cannot effectively extract dimensionality-reduced data features. In this paper, different dimensionality reduction and machine learning supervised classification algorithms are explored to determine a suitable combination method of dimensionality reduction and classification for hyperspectral images. Soft and hard classification methods are adopted to achieve the classification of pixels according to diversity. The results show that the data after dimensionality reduction retain the data features with high overall feature correlation, and the data dimension is drastically reduced. The dimensionality reduction method of unified manifold approximation and projection and the classification method of support vector machine achieve the best terrain classification with 99.57% classification accuracy. High-precision fitting of neural networks for soft classification of hyperspectral images with a model fitting correlation coefficient (R2) of up to 0.979 solves the problem of mixed pixel decomposition.

Remote Sensing Image Information Extraction and Application Based on Improved Pixel Exchange Algorithm

High resolution images can better reflect the size, shape and structural characteristics of ground objects, but due to factors such as purchase cost and observation period, it is often difficult to meet the practical application needs. Sub-pixel mapping technology can effectively improve the resolution of the results, which solves the above problem to some extent. The relevant algorithms of sub-pixel mapping include mixed pixel decomposition, end pixel extraction, sub-pixel positioning and other sub fields. The most classical sub-pixel positioning methods often use spatial correlation to define the positioning criteria. In this paper, NDWI and OTSU segmentation are used to constrain sub-pixel decomposition to some extent, which improves the performance of PSA algorithm, so as to more accurately extract river shoreline information, improve the resolution of monitoring on shoreline erosion collapse and dynamic change in the bank collapse area, and enhance the applicability of satellite remote sensing in the bank collapse monitoring field.

Review of Remote Sensing Applications in Grassland Monitoring

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.

SURFACE HANDWRITING ENHANCEMENT OF ARTIFACTS BASED ON MANIFOLD LEARNING AND MIXED PIXEL DECOMPOSITION

Abstract. Written information on the surface of cultural relics can record important historical events. Due to the influence of natural and human factors, the surface of cultural relics fades and the words are difficult to identify. Take advantage of the hyperspectral data image and spectral unity and wide spectral range, a cultural relics surface handwriting enhancement method based on manifold learning and mixed pixel decomposition was proposed. First, the minimum noise fraction (MNF) transformation was carried out on the hyperspectral image, and then the top 10 bands were selected for inverse MNF transformation to reduce noise of the hyperspectral image. Then, the reconstructed image was dimensionally reduced by locally linear embedding (LLE) to obtain a gray image with the maximum amount of information. At the same time, the spectral features of the handwriting and background area in the reconstructed image were analysed. The automatic target generation process (ATGP) was adopted for endmember extraction on the reconstructed image to identify the endmember of handwriting. The abundance map of handwriting area was obtained by the fully constrained least squares (FCLS). Finally, the gray image and the abundance map of the handwriting region were weighted together to obtain the handwriting enhanced image. The true color image was synthesized from the reconstructed image, Then the true color image and the handwriting enhancement image were fused to obtain the handwritting fusion image. The hyperspectral image of a faded text in Shuozhou City, Shanxi Province, China, was used as an example for verification, and the results showed that the method can effectively enhance the text on the surface of the artifacts.

Methods of Sandy Land Detection in a Sparse-Vegetation Scene Based on the Fusion of HJ-2A Hyperspectral and GF-3 SAR Data

Accurate identification of sandy land plays an important role in sandy land prevention and control. It is difficult to identify the nature of sandy land due to vegetation covering the soil in the sandy area. Therefore, HJ-2A hyperspectral data and GF-3 Synthetic Aperture Radar (SAR) data were used as the main data sources in this article. The advantages of the spectral characteristics of a hyperspectral image and the penetration characteristics of SAR data were used synthetically to carry out mixed-pixel decomposition in the “horizontal” direction and polarization decomposition in the “vertical” direction. The results showed that in the study area of the Otingdag Sandy Land, in China, the accuracy of sandy land detection based on feature-level fusion and single GF-3 data was verified to be 92% in both cases by field data; the accuracy of sandy land detection based on feature-level fusion was verified to be 88.74% by the data collected from Google high-resolution imagery, which was higher than that based on single HJ-2A (74.17%) and single GF-3 data (88.08%). To further verify the universality of the feature-level fusion method for sandy land detection, Alxa sandy land was also used as a verification area and the accuracy of sandy land detection was verified to be as high as 88.74%. The method proposed in this paper made full use of the horizontal and vertical structural information of remote sensing data. The problem of mixed pixels in sparse-vegetation scenes in the horizontal direction and the problem of vegetation covering sandy soil in the vertical direction were both well solved. Accurate identification of sandy land can be realized effectively, which can provide technical support for sandy land prevention and control.

Variation of the land surface temperature field in rare-Earth ore mining areas based on temperature downscaling

During the mining process of ionic rare earth elements (REE), mining disturbances, such as land surface excavation, stripped vegetation, injection holes, and tailings discharge, have led to large-scale vegetation degradation and land desertification, ultimately resulting in land surface temperature (LST) differentiation. The surface thermal anomaly is an important ecological parameter, and its spatiotemporal variation has become a powerful tool for identifying surface ecological disturbance. Because the REE mining areas have the characteristics of scattered ore and small areas, it is of great value to obtain LST data with strong practicality and high spatial resolution for sustainable ecological environment monitoring. In this study, the Lingbei mining area in Dingnan County, Ganzhou, China was selected as the study area. Via the use of Landsat and GaoFen-1 (GF-1) remote sensing images, and based on data fusion and the mixed-pixel decomposition method, the temperature downscaling with image fusion and spectral unmixing (TDIFSU) model and the temperature downscaling with data fusion and spectral unmixing (TDDFSU) model were constructed. In addition, they were compared with the commonly used temperature-downscaling disaggregation procedure for radiometric LST (DisTrad) model to explore the accuracy and applicability of different downscaling models for REE mining areas. Finally, based on remote sensing data from the past 20 years, a suitable downscaling model was selected and the temperature difference index (TDI) was used to analyze the spatiotemporal evolutions of ecological disturbances caused by REE mining. The results showed TDDFSU model had the highest accuracy and the smallest mean absolute error (MAE) and root mean square error (RMSE), which were 1.062 K and 1.386 K in February and 0.877 K and 1.094 K in October, respectively, but it had limitations in spatiotemporal study. The accuracy of TDIFSU model was the second, and TDIFSU model based on Landsat data has more advantages in long time series research. DisTrad model has the lowest accuracy. The most suitable model can be selected according to the time span and accuracy; The LST obtained by downscaling can reflect the actual land surface in more detail, and the boundaries between different ground objects were clearer; The high-temperature areas were mainly concentrated in the vicinity of long-term ore occurrences covered with a large amount of tailings. Medium-temperature areas were mainly bare soil and in situ leaching vegetation areas, and low-temperature areas were mainly natural and reclaimed vegetation areas; During the monitoring of the LST in Lingbei mining area for nearly 20 years, the temperature differences caused by different mining activities were revealed, and the land surface disturbance situation has improved after the state actively carried out REE mining environmental control.

Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing

Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the L2,1 norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method.

Estimation of Rape-Cultivated Area Based on Decision Tree and Mixed Pixel Decomposition

The timely and accurate information of rape-cultivated area in Jianghan Plain is of great significance for crop yield estimation and food security in China. MODIS-EVI time series images were used to estimate rape-cultivated area of Jianghan Plain in 2015–2017 based on decision tree and mixed pixel decomposition methods. By analyzing the features of EVI time series curves of different objects, rape distribution was preliminarily extracted by establishing the decision tree model; however, due to the problem of mixed pixels, there are heterogeneous objects in a single pixel, which will affect the extraction accuracy of crop area by decision tree method. To solve this problem, according to EVI spectral curve of the endmembers, the linear spectral mixture model was adopted to further improve the precision of crop extraction, and rape-cultivated area was extracted accurately by using computed rape endmember abundance. The results showed that the average estimation accuracy of rape area was 96.6% with city-level R2 (coefficient of determination) greater than 0.93. It demonstrated that the vegetation index time series data with medium resolution can accurately extract the rape-cultivated area, which can provide reference for rape monitoring over a large scale.

Landsat-8 and Gaofen-1 image-based inversion method for the downscaled land surface temperature of rare earth mining areas

Land surface temperature is an important indicator for characterizing the habitat conditions and ecological disturbances of ion-adsorbed rare earth mining areas. Because the ions in an ion-adsorbed type rare earth mining area are scattered and the area of a single mining site is small, it is important to obtain practical surface temperature data for monitoring the ecological environment of the area. In this study, Landsat-8 and Gaofen-1(GF-1) satellite images were used as data sources. In view of the characteristics of rare earth mining areas, and the thermal infrared data were used to retrieve the land surface temperature (LST), a land surface temperature downscaling model TDFSU based on remote sensing data fusion and mixed pixel decomposition is proposed. ultimately verifying the quantitative and qualitative accuracy of the downscaled results. The results show that the model can effectively improve the spatial resolution of surface temperature and reflect the spatial differences of surface temperature. This model has high feasibility and applicability in ionic rare earth mining areas.

Application of mixed pixel decomposition in passive ranging based on oxygen absorption

Application of mixed pixel decomposition in passive ranging based on oxygen absorption