A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network

© 1980-2012 IEEE. Due to the tradeoff between spatial and temporal resolutions commonly encountered in remote sensing, no single satellite sensor can provide fine spatial resolution land surface temperature (LST) products with frequent coverage. This situation greatly limits applications that require LST data with fine spatiotemporal resolution. Here, a deep learning-based spatiotemporal temperature fusion network (STTFN) method for the generation of fine spatiotemporal resolution LST products is proposed. In STTFN, a multiscale fusion convolutional neural network is employed to build the complex nonlinear relationship between input and output LSTs. Thus, unlike other LST spatiotemporal fusion approaches, STTFN is able to form the potentially complicated relationships through the use of training data without manually designed mathematical rules making it is more flexible and intelligent than other methods. In addition, two target fine spatial resolution LST images are predicted and then integrated by a spatiotemporal-consistency (STC)-weighting function to take advantage of STC of LST data. A set of analyses using two real LST data sets obtained from Landsat and moderate resolution imaging spectroradiometer (MODIS) were undertaken to evaluate the ability of STTFN to generate fine spatiotemporal resolution LST products. The results show that, compared with three classic fusion methods [the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the spatiotemporal integrated temperature fusion model (STITFM), and the two-stream convolutional neural network for spatiotemporal image fusion (StfNet)], the proposed network produced the most accurate outputs [average root mean square error (RMSE) 0.971]

Multi-Time Scale Analysis of Urbanization in Urban Thermal Environment in Major Function-Oriented Zones at Landsat-Scale: A Case Study of Hefei City, China

Urbanization and increasing demand for natural resources and land have affected the urban thermal environment. This is an important hot topic in urban climate research. In this study, we obtained multi-time scale land surface temperatures (LST) at the Landsat scale in Hefei, China, from 2011 to 2020. The evolution of the surface urban heat island (SUHI) was analyzed, and the contribution index (CI), urban thermal field variation index (UTFVI), and landscape pattern were evaluated to analyze the thermal environment mechanism of a major function-oriented zone (MFOZ). In addition, we explored the role and mechanism of different MFOZs in a thermal environment. Our results show that the multi-time scale differences in the SUHI were obvious, with the phenomenon of heat islands being concentrated in the main city zone. There are significant multi-time scale differences in the CI of different landscapes under the MFOZ. The UTFVI analysis of the MFOZ shows that the livability of the cities in the core optimization zone (COZ) and modern urbanization and industrialization cluster development zone (IDZ) is poor. MFOZ planning moderately alleviated the urban thermal environment of the entire study area, especially in the agricultural development zone (ADZ) and ecological conservation zone (ECZ). This study can guide the planning of the MFOZ and guide decision-makers in selecting governance zones when planning policies or dividing the key restoration areas of the thermal environment

Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network

Geostationary satellite land surface temperature (GLST) data are important for various dynamic environmental and natural resource applications for terrestrial ecosystems. Due to clouds, shadows, and other atmospheric conditions, the derived LSTs are often missing a large number of values. Reconstructing the missing values is essential for improving the usability of the geostationary satellite LST data. However, current reconstruction methods mainly aim to fill the values of a small number of invalid pixels with many valid pixels, which can provide useful land surface temperature values. When the missing data extent becomes large, the reconstruction effect will worsen because the relationship between different spatiotemporal geostationary satellite LSTs is complex and highly nonlinear. Inspired by the superiority of the deep convolutional neural network (CNN) in solving highly nonlinear and dynamic problems, a multiscale feature connection CNN model is proposed to fill missing LSTs with large missing regions. The proposed method has been tested on both FengYun-2G and Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager geostationary satellite LST datasets. The results of simulated and actual experiments show that the proposed method is accurate to within about 1 °C, with 70% missing data rates. This is feasible and effective for large regions of LST reconstruction tasks

Wetland Change Detection Using Cross-Fused-Based and Normalized Difference Index Analysis on Multitemporal Landsat 8 OLI

Wetlands are one of the most important ecosystems on the Earth and play a critical role in regulating regional climate, preventing floods, and reducing flood severity. However, it is difficult to detect wetland changes in multitemporal Landsat 8 OLI satellite images due to the mixed composition of vegetation, soil, and water. The main objective of this study is to quantify change to wetland cover by an image-to-image comparison change detection method based on the image fusion of multitemporal images. Spectral distortion is regarded as candidate change information, which is generated by the spectral and spatial differences between multitemporal images during the process of image cross-fusion. Meanwhile, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were extracted from the cross-fused image as a normalized index image to enhance and increase the information about vegetation and water. Then, the modified iteratively reweighted multivariate alteration detection (IR-MAD) is applied to the generally fused images and normalized difference index images, providing a good evaluation of spectral distortion. The experimental results show that the proposed method performed better to reduce the detection errors due to the complicated areas under different ground types, especially in cultivated areas and forests. Moreover, the proposed method was tested and quantitatively assessed and achieved an overall accuracy of 96.67% and 93.06% for the interannual and seasonal datasets, respectively. Our method can be a tool to monitor changes in wetlands and provide effective technical support for wetland conservation

Physical-Based Spatial-Spectral Deep Fusion Network for Chlorophyll-a Estimation Using MODIS and Sentinel-2 MSI Data

Satellite-derived Chlorophyll-a (Chl-a) is an important environmental evaluation indicator for monitoring water environments. However, the available satellite images either have a coarse spatial or low spectral resolution, which restricts the applicability of Chl-a retrieval in coastal water (e.g., less than 1 km from the shoreline) for large- and medium-sized lakes/oceans. Considering Lake Chaohu as the study area, this paper proposes a physical-based spatial-spectral deep fusion network (PSSDFN) for Chl-a retrieval using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 Multispectral Instrument (MSI) reflectance data. The PSSDFN combines residual connectivity and attention mechanisms to extract effective features, and introduces physical constraints, including spectral response functions and the physical degradation model, to reconcile spatial and spectral information. The fused and MSI data were used as input variables for collaborative retrieval, while only the MSI data were used as input variables for MSI retrieval. Combined with the Chl-a field data, a comparison between MSI and collaborative retrieval was conducted using four machine learning models. The results showed that collaborative retrieval can greatly improve the accuracy compared with MSI retrieval. This research illustrates that the PSSDFN can improve the estimated accuracy of Chl-a for coastal water (less than 1 km from the shoreline) in large- and medium-sized lakes/oceans

Building Extraction in Very High Resolution Imagery by Dense-Attention Networks

Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder⁻decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red⁻green⁻blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods

An adaptive offloading framework for license plate detection in collaborative edge and cloud computing

Abstract With the explosive growth of edge computing, huge amounts of data are being generated in billions of edge devices. It is really difficult to balance detection efficiency and detection accuracy at the same time for object detection on multiple edge devices. However, there are few studies to investigate and improve the collaboration between cloud computing and edge computing considering realistic challenges, such as limited computation capacities, network congestion and long latency. To tackle these challenges, we propose a new multi-model license plate detection hybrid methodology with the tradeoff between efficiency and accuracy to process the tasks of license plate detection at the edge nodes and the cloud server. We also design a new probability-based offloading initialization algorithm that not only obtains reasonable initial solutions but also facilitates the accuracy of license plate detection. In addition, we introduce an adaptive offloading framework by gravitational genetic searching algorithm (GGSA), which can comprehensively consider influential factors such as license plate detection time, queuing time, energy consumption, image quality, and accuracy. GGSA is helpful for Quality-of-Service (QoS) enhancement. Extensive experiments show that our proposed GGSA offloading framework exhibits good performance in collaborative edge and cloud computing of license plate detection compared with other methods. It demonstrate that when compared with traditional all tasks are executed on the cloud server (AC), the offloading effect of GGSA can be improved by 50.31%. Besides, the offloading framework has strong portability when making real-time offloading decisions