Solar energy is the most clean renewable energy source and has good prospects for future sustainable development. Installation of solar photovoltaic (PV) systems on building rooftops has been the most widely applied method for using solar energy resources. In this study, we developed an approach to simulate the monthly and annual solar radiation on rooftops at an hourly time step to estimate the solar PV potential, based on rooftop feature retrieval from remote sensing images. The rooftop features included 2D rooftop outlines and 3D rooftop parameters retrieved from high-resolution remote sensing image data (obtained from Google Maps) and digital surface model (DSM, generated from the Pleiades satellite), respectively. We developed the building features calculation method for five rooftop types: flat rooftops, shed rooftops, hipped rooftops, gable rooftops and mansard rooftops. The parameters of the PV modules derived from the building features were then combined with solar radiation data to evaluate solar photovoltaic potential. The proposed method was applied in the Chao Yang District of Beijing, China. The results were that the number of rooftops available for PV systems was 743, the available rooftop area was 678,805 m², and the annual PV electricity potential was 63.78 GWh/year in the study area, which has great solar PV potential. The method to perform precise calculation of specific rooftop solar PV potential developed in this study will guide the formulation of energy policy for solar PV in the future.

Global changes, such as human activities and climate change, increase the odds of worsening drought. The Gravity Recovery and Climate Experiment (GRACE) satellite provides an opportunity to monitor drought levels by the total amount of water, instead of using a small finite set of water cycle elements or indirect indicators. The potential gap lies in the insufficient size of the GRACE record. The database does not meet the requirements of a stationary annual cycle calculated over a relatively long period as recommended by the IPCC, and the disturbance from long-term global changes is often not considered. In this work, a GRACE-based modulated water deficit (GRACE-MWD) process for drought monitoring under the modulated annual cycle (MAC) reference frame in southwest China was proposed. GRACE-MWD achieved a higher ratio of agreement with the standardized precipitation evapotranspiration index at a time scale of 3 months (SPEI03): it ranged from 0.48 to 0.84, while the GRACE-based drought severity index (GRACE-DSI) ranged from 0.48 to 0.68. Compared with remote sensing datasets widely used in drought monitoring, GRACE-MWD data are less affected by seasonality from land-cover categories, which benefit from the MAC reference frame. The ratio-of-agreement metric for the study area showed that GRACE-MWD had a time scale between 7 and 11 months in reference to SPEI and the standardized precipitation index (SPI). The stability of the MAC reference frame to GRACE-MWD was further discussed when GRACE records were extended and was more stable than that of the stationary annual cycle. GRACE-MWD meets global changes via an adaptive reference frame, which is worthy of generalizing to global applications.

Mangroves straddle terrestrial and marine ecosystems, thus are relevant to many United Nations Sustainable Development Goals (SDG), especially to SDG‐13 and SDG‐14, as they are linked to blue carbon strategies and fisheries. To better achieve SDGs, China has been restoring mangroves for their ecological and societal values through strict protection and afforestation, even in areas beyond their natural northern limits. However, small fragmented mangrove patches, especially those in marginal climates, have been historically neglected by existing mangrove maps, which, because they were derived from 30‐m‐resolution Landsat data, are too coarse to resolve them. To overcome this problem, we applied a classification to 10‐m‐resolution Sentinel‐1 and ‐2 images for 2019 and then an enhanced post‐processing to identify missing mangrove patches by utilizing Google Earth images. Thus, we produced the first publicly accessible detailed mangrove map supplementing the main 10‐m‐resolution results with submetre data from Google Earth images. According to general quantitative evaluations, the produced map achieved an overall accuracy of 96.4 ± 0.3% based on validation data sets, an accuracy of 96.2% based on a total of 1,096 field sample plots. By evaluations in marginal climates, the produced map identified six areas with a total of seven mangrove planting areas, which is twice the number of identified areas by current publicly accessible mangrove maps. The produced map also outperformed the competitors in the identification of small mangrove patches in suitable climates, and in the determination of neat and tidy boundaries of large mangrove patches. The map paid attention to these small mangrove patches because they can provide substantial ecosystem services, such as increasing the community diversity and providing habitats for local species. The map does not only serve as a basis for mangrove species mapping, leaf area index estimation and carbon stock assessment, but also improves other mangrove maps by mutual corrections.

Land cover information extraction through object-based image analysis (OBIA) has become an important trend in remote sensing, thanks to the increasing availability of high-resolution imagery. Segmented objects have a large number of features that cause high-dimension and low-sample size problems in the classification process. In this study, on the basis of a partial least squares generalized linear regression (PLSGLR), we propose a group corrected PLSGLR, known as G-PLSGLR, that aims to reduce the redundancy of object features for land cover identifications. Using Gaofen-2 images, the area of interest was segmented and sampled to generate small sample-size training datasets with 51 object features. The features selected by G-PLSGLR were compared against a guided regularized random forest (GRRF) in metrics of reduction rate, feature redundancy, and accuracy assessment of classification. Three indicators of overall accuracy (OA), user’s accuracy (UA), and producer’s accuracy (PA) were applied for accuracy assessment in this paper. The result shows that the G-PLSGLR achieved a reduction rate of 9.27 with a feature redundancy of 0.29, and a value of OA 90.63%. The GRRF achieved a reduction rate of 1.61 with a feature redundancy of 0.42, and a value of OA 85.56%. The PA of each land cover category was more than 95% using features selected by G-PLSGLR, while the PA ranged from 77 to 96% using features selected by GRRF. The UA of G-PLSGLR-selected features ranged from 70 to 80% except for grass land and bare land, which achieved 10% higher UA than GRRF-selected features. The G-PLSGLR method we proposed has the advantages of a large reduction rate, low feature redundancy, and high classification performance, which can be applied in OBIA-based land cover classification.

Mangrove management in China is converting from emphasis on occupied area to intrinsical quality. Mangrove species have varied ecological values, e.g., those having well-developed prop roots are more beneficial for coastal stabilization. The detailed distribution of mangrove species on a national scale remains a challenge, impeding species-specific applications in the ecology and management of mangroves, as well as evaluations of related Sustainable Development Goals. Although local-scale studies have explored various data sources, the variability of plant phenology is the key factor preventing their generalization from local areas to large latitudinal spans (e.g., the coast of China). The separability time period, defined by a separability metric for each scene of the image time series, provides a potential way to tackle the aforementioned problem. We conducted a case study on mapping Kandelia obovata in China based on Sentinel-2 time-series imagery, as it is a representative native mangrove species with the largest latitudinal span and tolerance to low temperatures. The proposed approach considered the separability between K. obovata and its typical co-occurring mangrove species, as well as that between K. obovata and salt marshes. The overall accuracy of the generated K. obovata map in China for 2020 reached 88.5% based on independently collected samples. The proposed approach is transferable to diverse mangrove species that inhabit a vast latitudinal span. The implications for sustainable mangrove management were discussed to reveal the benefits to precise management of mangroves, accurate biomass and carbon estimations accounting for species differences, and effective evaluation of mangrove ecosystem services.