Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation-temperature condition index covariates.

Pixel-based and object-based classifications are two commonly used approaches in extracting land cover information from remote sensing images. However, they each have their own inherent merits and limitations. This study, therefore, proposes a new classification method through the integration of pixel-based and object-based classifications (IPOC). Firstly, it employs pixel-based soft classification to obtain the class proportions of pixels to characterize the land cover details from pixel-scale properties. Secondly, it adopts area-to-point kriging to explore the class spatial dependence between objects for each pixel from object-based soft classification results. Thirdly, the class proportions of pixels and the class spatial dependence of pixels are fused as the class occurrence of pixels. Last, a linear optimization model on objects is built to determine the optimal class label of pixels within each object. Two remote sensing images are used to evaluate the effectiveness of IPOC. The experimental results demonstrate that IPOC performs better than the traditional pixel-based hard classification and object-based hard classification methods. Specifically, the overall accuracy of IPOC is 7.64% higher than that of pixel-based hard classification and 4.64% greater than that of object-based hard classification in the first experiment, while the overall accuracy improvements in the second experiment are 3.59% and 3.42%, respectively. Meanwhile, IPOC produces less salt and pepper effect than the pixel-based hard classification method and generates more accurate land cover details and small patches than the object-based hard classification method.

Spatiotemporal remote sensing image fusion (STF) is a promising way to obtain remote sensing data with both fine spatial and temporal resolutions. Gradual and abrupt changes in land surface reflectance images are the main challenges in existing STF methods. Advanced deep learning techniques present powerful ability in learning image-changed information. Therefore, this article proposes a novel spatiotemporal image fusion method using multiscale two-stream convolutional neural networks (STFMCNNs). Multiscale two-stream convolutional neural networks are proposed to capture different sizes of objects in feature learning from a coarse spatial resolution image and two pairs of coarse and fine spatial resolution (FR) images at other dates. Meanwhile, temporal dependence and temporal consistency are explored as complementary information for STFMCNN. Moreover, a local fusion method is developed to characterize local variation by combining two predicted images derived from each stream. Two experiments on different real images are conducted to demonstrate the effectiveness of STFMCNN. Results show that STFMCNN outperformed three existing methods by predicting more accurate FR images with more preserved changed information.

Timely griddedgross domestic product (GDP) data is a fundamental indicator in many applications. It is critical to characterize the complex relationship between GDP and its auxiliary information for accurately estimating gridded GDP. However, few knowledge is available about the performance of deep learning approaches for learning this complex relationship. This article develops a novel convolutional neural network based GDP downscaling approach (GDPnet) to transform the statistical GDP data into GDP grids by integrating various geospatial big data. An existing autoencoder-based downscaling approach (Resautonet) is employed to compare with GDPnet. The latest county-level GDP data of China and the multiple geospatial big data are adopted to generate the 1-km gridded GDP data in 2019. Due to the different related auxiliary data of each GDP sector, the two downscaling approaches are first separately built for each GDP sector and then the results are merged to the gridded total GDP data. Experimental results show that the two deep learning approaches had good predictive power with R ² over 0.8, 0.9, and 0.92 for the three sectors tested by county-level GDP data. Meanwhile, the proposed GDPnet outperformed the existing Resautonet. The average R ² of GDPnet was 0.034 higher than that of Resautonet in terms of county-level GDP test data. Furthermore, GDPnet had higher accuracy ( R ² = 0.739) than Resautonet ( R ² = 0.704) assessed by town-level GDP data. In addition, the proposed GDPnet is faster (about 78% running time) than the Resautonet. Hence, the proposed approach provides a valuable option for generating gridded GDP data.