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The coral reef geomorphic zone classification (CRGZC) map can provide a wealth of information for coastal management and protection. Remote sensing plays an important role in CRGZC by virtue of its speed, wide range, and low cost. Although many excellent results have been achieved in this field, there are still some shortcomings. With the development of machine learning, such methods are gradually introduced to CRGZC, yet the research and application of deep learning methods are still relatively few. In this article, based on ICESat-2 data and multispectral images, a deep learning model coupled with convolutional neural network (CNN) and random forest (RF) was proposed for coral reef geomorphic zone classification (CR_CRGZC). First, the priori bathymetry points were extracted from ICESat-2. Then, a near-shore bathymetry map was generated using a log-ratio model. Finally, topographic data and multispectral images were combined to achieve CRGZC through CR_CRGZC. The northeastern part of Coffin Island (CI) and the southern part of Punta Vaquero (PV) in Puerto Rico Island were selected as study areas. By comparing the classification results with those of CNN, RF, and maximum likelihood classification, CR_CRGZC outperformed the other classification methods. By quantitative analysis, the OA and Kappa coefficients of CR_CRGZC were 91.91% and 0.9013 in the CI region; and 89.91% and 0.8735 in the PV region, respectively. Under the same environmental requirements, this approach can map high-precision submeter CRGZC maps, providing a database for dynamic coral reef habitat mapping, which contributes to marine coastal ecosystem protection and coastal underwater topography monitoring.

Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, especially deep learning, can infer a good prediction by integrating multisource datasets such as satellite data, climate data, soil data, and so on. However, there are some bottleneck challenges to improve accuracy. First, the popular remote sensing data used for yield prediction fall into two major groups-time-series data and constant data. Surprisingly little attention has been devoted to deep learning networks which can integrate the two kinds of data effectively; second, both temporal and spatial features play a role in affecting the yields. But most of the existing approaches employed either convolutional neural network (CNN) or recurrent neural network (RNN). CNN cannot learn temporal patterns, while RNN barely can learn spatial characteristics. This work proposed a novel multilevel deep learning model coupling RNN and CNN to extract both spatial and temporal features. The inputs include both time-series remote sensing data, soil property data, and the model outputs yield. We experimented with the model in U.S. Corn Belt states, and used it to predict corn yield from 2013 to 2016 at the county-level. The results approve the effectiveness and advantages of the proposed approach over the other methods. In the future, the model will be used on other crops such as soybean and winter wheat to assist agricultural decision-making.

Accurate bathymetric data is crucial for marine and coastal ecosystems. A lot of studies have been carried out for nearshore bathymetry using satellite data. The approach adopted extensively in shallow water depths estimation has recently been one of empirical models. However, the linear empirical model is simple and only takes limited band information at each bathymetric point into consideration. It may be not suitable for complex environments. In this paper, a deep learning framework was proposed for nearshore bathymetry (DL-NB) from ICESat-2 LiDAR and Sentinel-2 Imagery datasets. The bathymetric points from the spaceborne ICESat-2 LiDAR were extracted instead of in situ measurements. By virtue of the two-dimensional convolutional neural network (2D CNN), DL-NB can make full use of the initial multi-spectral information of Sentinel-2 at each bathymetric point and its adjacent areas during the training. Based on the trained model, the bathymetric maps of several study areas were produced including the Appalachian Bay (AB), Virgin Islands (VI), and Cat Island (CI) of the United States. The performance of DL-NB was evaluated by empirical method, machine learning method and multilayer perceptron (MLP). The results indicate that the accuracy of the DL-NB is better than comparative methods can in nearshore bathymetry. After quantitative analysis, the RMSE of DL-NB could achieve 1.01 m, 1.80 m and 0.28 m in AB, VI and CI respectively. Given the same data conditions, the proposed method can be applied for high precise global scale and multitemporal nearshore bathymetric maps generation, which are beneficial to marine environmental change assessment and conservation.

Aerosol particles originated from anthropogenic emissions, volcanic eruptions, biomass burning, and fossil combustion emissions, and their radiative effect is one of the most uncertain factors in climate change. Meanwhile, aerosol particles in fine particle size could also cause irreversible effects on the human respiratory system. This study attempted to analyse the spatial and temporal variations of global aerosol optical depth (AOD, 550 nm) during 1980–2018 using MERRA-2 aerosol reanalysis products and to investigate the effects of natural/anthropogenic emissions of different types of aerosols on AOD values. The results show that the global annual mean AOD values kept high levels with significant fluctuations during 1980–1995 and showed a consistent decreasing and less volatile trend after 1995. Spatially, the AOD values are relatively higher in the Northern Hemisphere than in the Southern Hemisphere, especially in North Africa (0.329), Northern India (0.235), and Eastern China (0.347), because of the intensive natural/anthropogenic aerosol emissions there. The sulphate-based aerosols emitted by biomass burning and anthropogenic emissions are the main types of aerosols worldwide, especially in densely populated and industrialized regions such as East Asia and Europe. Dust aerosols are also the main aerosol type in desert areas. For example, the AOD and AODP values for the Sahara Desert are 0.3178 and 75.32%, respectively. Both black carbon aerosols (BC) and organic carbon aerosols (OC) are primary or secondary from carbon emissions of fossil fuels, biomass burning, and open burning. Thus, the regions with high BC and OC aerosol loadings are mainly located in densely populated or vegetated areas such as East Asia, South Asia, and Central Africa. Sea salt aerosols are mainly found in coastline areas along the warm current pathway. This study could help relevant researchers in the fields of atmospheric science, environmental protection, air pollution, and ecological environment to understand the global spatial–temporal variations and main driving factors of aerosol loadings.

Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.