Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images.

Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase.

Terrestrial ecosystem carbon stock (TECS) is critical to socioeconomic development and ecosystem services and is jointly affected by land use and cover and climate change. However, the dynamics of long-term annual TECS levels in urban agglomeration remain largely unknown, and research mostly ignores the spatial heterogeneity of climate factors, compromising sustainable environmental management and land planning strategies. To this end, we integrated field observations of carbon density, land use, and climate factors to map the annual distribution of TECS and analyzed their spatiotemporal variations and policy implications in the urban agglomeration of the middle Yangtze River Basin in China from 1990 to 2020. The results showed that 43,855.47 km² of the land of the urban agglomeration changed from 1990 to 2020, accounting for 12.54% of the study area. The farmland and forest land area fluctuated and reduced, and the construction land area increased significantly. The increase in construction land was mainly from farmland and forest land. The TECS in urban agglomerations underwent a remarkable change, the overall trend fluctuated downward, and the maximum interannual variation was 1560 Tg. The transfer of construction land, farmland, forest land, shrubs, grassland, and other land mainly caused the change in carbon storage. Due to abnormal climate change, the urban agglomeration in some areas illustrated carbon storage with a spatially aggregated distribution. When considering the impact of climate change on carbon density, the TECS changes of land types other than forest land were found to be consistent with the area change but more significant due to climate change. The research results can provide reference data for regional land management policy formulation and realization of “dual carbon” goals.