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Black and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time series, low cost, and high efficiency, has provided a new area for water quality detection. Much archived remote sensing satellite data can be further processed and used as a data source for black and odorous water detection. In this paper, Gaofen-2 remote sensing data with a spatial resolution of 1 m is leveraged as the data source. To enrich the data source in the northern coastal zone of China, we have built a high-quality remote sensing dataset, called the remote sensing images for black and odorous water detection (RSBD) dataset, which is collected from the Gaofen-2 satellite in Yantai, China. In addition, we propose a network with an encoder-decoder discriminant structure for black and odorous water detection. In the network, an augmented attention module is designed to capture a more comprehensive semantic feature representation. Further, the median balancing loss function is adopted to solve the imbalance issues. Experimental results demonstrate that the network is superior to other state-of-the-art semantic segmentation methods on our dataset. [ABSTRACT FROM AUTHOR]

Accurate carbon (C) stock estimation is crucial for C sequestration research, environmental protection, and policy formulation related to C management. Although research on C stock in forests, oceans, soil, and desert has received increasing attention, relatively few studies have focused on urban C stock. Moreover, the current mainstream methods for C stock assessment, including field surveys and satellite mapping, are characterised by notable limitations, including being labour-intensive and having limited real-time data acquisition capabilities. Therefore, this paper aims to assess urban C stock and proposes a novel two-stage estimation model based on deep learning and unmanned aerial vehicle (UAV) remote sensing. The first stage is that tree areas recognition via YOLOv5 and achieved 0.792 precision, 0.814 recall, and 0.805 mAP scores, respectively. In the second stage, a grid generation strategy and a Convolutional Neural Network (CNN) regression model were developed to estimate C stock based on recognised tree areas (R² = 0.711, RMSE = 26.08 kg). Three regions with a minimum of 300 trees in each area were selected as validation sets. The experimental results, in terms of R² and RMSE in kg, were (0.717, 0.711, 0.686) and (27.263, 27.857, 28.945), respectively. [ABSTRACT FROM AUTHOR]

To assess the status and change trend of forest in China, an indicator framework was developed using SDG sub-indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDG indicators. The main modification include the use of moderate and high spatial resolution satellite data, as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass (AGB). This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover, while time series Landsat data is used to estimate forest AGB across the whole territory of China. The study calculate two SDG sub-indicators: SDG15.1.1 for forest area and SDG15.2.1 for sustainable forest management. The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021, accounting for about 23.51% of the land area. The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha, and the overall trend of forest AGB change in China was positive, albeit with some spatial differences. [ABSTRACT FROM AUTHOR]

This paper presents an integrated study on the effects of human activity, climate change and crustal movement on the changes of the middle reaches of the Tarim River in China. Remote sensing images and rainfall data from 38 stations from 1970 to 2021 were used for the research. The precise point positioning (PPP) approach was applied to ascertain the crustal movement of the study area using 256 Global Positioning System (GPS) station data of the recent 20 years. The results show that in the past 50 years the studied reaches of the river has moved north at maximum 2940 m or 57.9 m on average, and human activity may have played a dominant role in this change though extreme events of climate may have also taken a part in it. However, before the Anthropocene, its northeast migration is likely associated with crustal movement as the basin bottom inclines to northeast. [ABSTRACT FROM AUTHOR]

Timely and accurate survey of construction and demolition waste (C&DW) distribution is of significance for managing C&DW and enhancing the quality of the urban environment, especially in rapidly urbanizing country of China. Automatic C&DW recognition using high-resolution remote sensing images is an extremely important surveying method. Existing C&DW recognition methods can only obtain a rough spatial distribution of C&DW. Mapping precise C&DW distribution on large-scale remote sensing images is still challenging. In this paper, to solve the problem of precise C&DW recognition, a novel deep learning algorithm based on high-resolution remote sensing images is proposed, which we refer to as the cross-channel multi-scale gated fusion network (CCMGNet). CCMGNet extracts depth features of C&DW from RGB and NIR bands by using two independent encoding streams. A gated fusion layer and a multi-scale attention module are designed to effectively fuse the features from two encoding streams. We manually labelled the sample set used for training and testing on 26 remote sensing images captured by the Gaofen-2 (GF-2) satellite. These images were taken in nine typical cities in China over the time period from 2019 to 2021. The results showed that the proposed method is effective in recognizing C&DW, realizing 89.42% and 84.52% in precision and IoU, and is superior to other state-of-the-art deep learning algorithms and existing C&DW recognition methods. The effectiveness of all significant components in CCMGNet was confirmed by ablation experiments. The proposed method was applied to extract C&DW regions in large-scale remote sensing images, with an example of a GF-2 image of Beijing. The high extraction efficiency and satisfactory visual effect demonstrate the potential of the proposed method for surveying the spatial distribution of C&DW. The novel method will play a crucial role in automatically surveying the spatial distribution and size of C&DW across the country. [ABSTRACT FROM AUTHOR]

Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn't vary in adjacent space and earth surface property doesn't vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the 'Beijing', 'Dalanzadgad', 'NCU_Taiwan' and 'Kanpur' stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR 'Deep Blue' aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively. [ABSTRACT FROM AUTHOR]

The seawater tracing index that characterizes the sea surface feature can be calculated using the optimal band combination of China GF-4 high-resolution multispectral data, and the distribution of sea surface currents can be retrieved from the sequential band combination data. In this paper, the methods of monitoring sea surface currents are presented in detail by processing multispectral remote sensing images generated by China GF-4 Earth Observation satellite. The sea surface currents obtained from GF-4 imagery data are compared with the sea surface currents derived from FES2014 global ocean tide model, providing a validation of the proposed methods. [ABSTRACT FROM AUTHOR]

The reflectance anisotropy of land surface serves as an important bridge between surface biophysical parameters and remote sensing observations. It can characterize by the linear kernel-driven bidirectional reflectance distribution function (BRDF), which is the combination of several kernel functions and kernel weights. These kernel weights can be estimated by remote sensing; however, the stability of current kernel weights products is still challenging, especially in urban areas with complex aerosol properties and heterogeneous surfaces. In this paper, we propose a method for robust estimation of kernel weights from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface spectral reflectance products (MxD09GA) data based on the constrained least-squares method (CLSM) and a priori knowledge. The kernel weights data were obtained by the CLSM from 2014 to 2017 in Beijing region of China. Validations were carried out using the MxD09GA and BRDF/Albedo products (MCD43A1). The results show that the time series of kernel weights by the CLSM show small variability over different land cover types. The kernel weights estimated by the CLSM can clearly show the phenological signal and fitting ability of surface spectral reflectance is better than that of the MCD43A1 products in Beijing urban area. Experimental results demonstrate that the CLSM has the potential for the robust estimation of kernel weights in urban areas. [ABSTRACT FROM AUTHOR]

Satellite image classification is the core of satellite image interpretation. The traditional shallow learning classification algorithm is difficult to extract remote sensing image features effectively, resulting in low classification accuracy. The deep neural network can automatically realize the feature analysis. However, due to the similarity of the optical properties of the cloud and snow, the existing deep learning model is difficult to obtain satisfactory results for the cloud and snow classification in the plateau. Meanwhile, too many parameters of deep learning lead to slow detection speed, which is difficult to meet the real-time requirements of detection. Furthermore, the deep learning method is prone to model degradation. In view of the existing problems, this paper proposes a dilated multi-scale cascade forest method to realize the classification of satellite cloud images. Multi-scale scanning increases the diversity of feature vector extraction and improves the separability of features. The dilated structure is designed to expand the feature receptive field, it improves the efficiency of feature extraction without losing the texture and spatial correlation information of the original image. In addition, the number of layers of network cascading does not need to be customized, it can be determined automatically according to the performance of the system. The proposed method effectively improves the accuracy of cloud/snow classification. Experiments on the imagery of HuanJing satellites A and B (HJ-1A/1B) in China demonstrate that the proposed model can get better classification results than existing studies. The time complexity of the proposed model is lower than existing works, hence it is practical in real-time and accurate remote sensing image classification. [ABSTRACT FROM AUTHOR]

China faces increasingly serious water scarcity due to the uneven distribution of available water resources, rapid economic development, and water pollution. The current war on water pollution by the Chinese government requires nationwide water quality information at high spatiotemporal resolution that can be obtained by only remote sensing (RS) methods. However, it is challenging to remotely retrieve such information from turbid Case-2 waters. This paper reviews four aspects of the major achievements in remotely sensed coastal and inland water quality in China. Specifically, achievements in atmospheric correction prior to water quality retrieval, progress in water-related sensor design, developments (improvements) to existing Case-2 water algorithms, and advances in oil spill and harmful algal bloom monitoring. Major challenges are identified, including: 1) a large mismatch exists between the water quality information required and RS datasets due to a lack of professional inland water sensors; 2) planned monitoring and field experiments for studying the optical properties of inland waters are scarce; and 3) RS of urban black odorous waters and international rivers is of great urgency. This review may provide scientific guidelines for obtaining information about coastal and inland waters and assist water resource managers and aquatic ecologists in controlling water pollution. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing can capture the complicated and variable characteristics of inland waters; thus, it is suited for the water quality assessment of Case-2 waters, and it has the potential to attain high estimation accuracy. In the present study, four improved models adapted from published approaches (three-band index, ΔΦ, BNDBI and TCARI) were investigated to estimate chlorophyll-a (chl-a) for the case of Dianshan Lake, China. Calibration and validation were provided from in situ measured chl-a and field hyperspectral measurements. The improved three-band (ITB) model, ΔΦ model, and BNDBI model yielded satisfactory results and enabled the estimation of chl-a for inland Case-2 waters with coefficients of determination (R2) reaching 0.75, 0.76, and 0.86, respectively. In particular, the TCARI/OSAVI model presented the highest accuracy (R2 = 0.94) compared to the other models. All of the results provide strong evidence that the hyperspectral models presented in this paper are promising and applicable to estimate chl-a in eutrophic inland Case-2 waters. [ABSTRACT FROM AUTHOR]

Global change has a significant impact on the lives of humankind. Earth observation can help to better understand our earth and cope with global change. With the availability of more reliable environmental data sets, digital earth is becoming a popular way to monitor the Earth and provide information to researchers and decision makers on environment protection, disaster mitigation, and social benefits. Therefore, accessing data with lowering costs is essential for digital earth. Nevertheless, there are big challenges in ensuring the feasibility of access to Chinese remote sensing data. This paper outlines some of the main challenges in realizing data sharing, provides an analysis of the core reasons leading to these challenges, and proposes recommendations to overcome the challenges. Amongst the main challenges are differences in data policy to gain access to satellite data, diverse data formats, and delivery mechanisms. The major challenge for the decision makers is to define a more open policy and for the scientist the challenge is to implement these polices for the benefit of all. This paper proposes that governments should adopt policies encouraging more open distribution and access to their data, in order to generate an improved digital earth with increased benefits to human society. [ABSTRACT FROM PUBLISHER]

Digitizing the land surface temperature (Ts) and surface soil moisture (mv) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrievingTsandmvusing the dual-polarizedTbof Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situTsandmvin southern China showed the average root mean square errors (RMSE) ofTsandmvretrievals reach 2.42 K (R2= 0.61,n= 351) and 0.025 g cm−3(R2= 0.68,n= 663), respectively. The results were also validated using global in situTs(n= 2362) andmv(n= 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R2= 0.86) and 0.039 g cm−3(R2= 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging SpectroradiometerTs(R2= 0.57; RMSE = 2.91 k) and ECV_SMmv(R2= 0.51; RMSE = 0.045 g cm−3), respectively. Overall, this paper indicates an effective way to jointly modelingTsandmvusing passive microwave remote sensing. [ABSTRACT FROM AUTHOR]

In this paper, we introduced major challenges in mapping croplands, settlements, water and wetlands, and discussed challenges in the use of multi-temporal and multi-sensor data. We then summarized some of the on-going efforts in improving qualities of global land cover maps. Existing technologies provide sufficient data for better map making if extra efforts can be made instead of harmonizing and integrating various global land cover products. Developing and selecting better algorithms, including more input variables (new types of data or features) for classification, having representative training samples are among conventional measures generally believed effective in improving mapping accuracies at local scales. We pointed out that data were more important in improving mapping accuracies than algorithms. Finally, we proposed a new paradigm for global land cover mapping, which included a view of vegetation classes based on their types and form, canopy cover and height. The new paradigm suggests that a universally applicable training sample set is not only possible but also effective in improving land cover classification at the continental and global scales. To ensure an easy transition from traditional land cover mapping to the new paradigm, we recommended that an all-in-one data management and analysis system be constructed. [ABSTRACT FROM AUTHOR]

Techniques for mapping and monitoring vegetation status under human disturbance (e.g., grazing livestock) can contribute to sustainable development of grasslands. The main objective of this paper is to investigate whether the effects of differences in grazing intensity (measured by stocking rate (SR)) could be discriminated using in situ spectral data. The experiment was conducted with controlled SRs on the Sino-German experimental farm in the Xilingol grassland, Inner Mongolia, P.R. China. Canopy spectral measurements were made on experimental paddocks that varied by SRs, terrain and management type. Eight vegetation indices (VIs) were calculated from ASD field spectrometer data for each paddock. We modelled the variability within and between paddocks using a linear mixed model to isolate the effect of grazing intensity on VI response. All VIs showed significant negative relationships to grazing intensity that were ameliorated on paddocks with haying compared to traditional direct grazing. The best VIs for detecting differences in paddocks due to the levels of SR were photochemical reflectance index (PRI), broadband normalized difference vegetation index (bNDVI) and narrowband NDVI (nNDVI1). The responses of VIs to vegetation conditions as affected by different SRs offer promise for the use of airborne and satellite remote sensing to map vegetation status and to evaluate the effects of grazing intensities at regional scales. [ABSTRACT FROM AUTHOR]

The normalized difference water indices (NDWIs) were successfully used in map land surface water mapping (LSWM) from Landsat series multispectral images. This paper evaluates the potential of the recent Landsat satellite (Landsat-8) Operational Land Imager (OLI) multispectral images for LSWM using three NDWI models. We tested the accuracy and robustness of the three OLI NDWI models in the Yangtze River Basin and the Huaihe River Basin in China. The results demonstrate that the three OLI NDWI models achieve an overall accuracy of more than 95%, a kappa coefficient of 0.89 and a producer’s accuracy of 95% for LSWM. The results also demonstrate that the NDWI model using the green band (Band 3) and the SWIR1 band (Band 6) (referred to as NDWIO6,3) of the OLI sensor has a higher LSWM accuracy than the other two NDWI models. [ABSTRACT FROM AUTHOR]