Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very promising results in computer-vision tasks, such as image classification and object detection. However, the problem of designing an effective deep-learning model for the fusion of multisource remote-sensing data still remains. To tackle this issue, this paper proposes a modified two-branch convolutional neural network for the adaptive fusion of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the proposed model consists of a HSI branch and a LiDAR branch, sharing the same network structure to reduce the time cost of network design. A residual block is utilized in each branch to extract hierarchical, parallel, and multiscale features. An adaptive-feature fusion module is proposed to integrate HSI and LiDAR features in a more reasonable and natural way (based on “Squeeze-and-Excitation Networks”). Experiments indicate that the proposed two-branch network shows good performance, with an overall accuracy of almost 92%. Compared with single-source data, the introduction of multisource data improves accuracy by at least 8%. The adaptive fusion model can also increase classification accuracy by more than 3% when compared with the feature-stacking method (simple concatenation). The results demonstrate that the proposed network can effectively extract and fuse features for a better urban land-use mapping accuracy.

Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs) derived from TERRA and AQUA MODIS data. Linear regression models were calibrated using LSTs from 2003 to 2011 and validated using LST data from 2012 to 2013, combined with meteorological station data. The results show that these models can provide a robust estimation of measured daily mean air temperature and that models that only accounted for meteorological data from rural regions performed best. Daily mean air temperature maps were generated from each of four MODIS LST products and merged using different strategies that combined the four MODIS products in different orders when data from one product was unavailable for a pixel. The annual average spatial coverage increased from 20.28% to 55.46% in 2012 and 28.31% to 44.92% in 2013.The root-mean-square and mean absolute errors (RMSE and MAE) for the optimal image merging strategy were 2.41 and 1.84, respectively. Compared with the least-effective strategy, the RMSE and MAE decreased by 17.2% and 17.8%, respectively. The interpolation algorithm uses the available pixels from images with consecutive dates in a sliding-window mode. The most appropriate window size was selected based on the absolute spatial bias in the study area. With an optimal window size of 33 × 33 pixels, this approach increased data coverage by up to 76.99% in 2012 and 89.67% in 2013.

Identification of maize seed varieties based on near infrared reflectance spectroscopy and chemometrics

Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQ^v2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQ^v2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQ^v2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQ^v2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems.