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The escalating evolution of aquaculture has wielded a profound and far-reaching impact on regional sustainable development, ecological equilibrium, and food security. Currently, most aquaculture mapping efforts mainly focus on coastal aquaculture ponds rather than diverse inland aquaculture areas. Recognizing all types of aquaculture areas and accurately classifying different types of aquaculture areas remains a challenge. Here, on the basis of the Google Earth Engine (GEE) and the time-series Sentinel-1 and -2 data, we developed a novel hierarchical framework extraction method for mapping fine inland aquaculture areas (aquaculture ponds + rice-crawfish fields) by employing distinct phenological disparities within two temporal windows (T1 and T2) in Qianjiang, so-called “Home of Chinese Crawfish”. Simultaneously, we evaluated the classification performance of four distinct machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Gradient Boosting (GTB), as well as 11 feature combinations. Following an exhaustive comparative analysis, we selected the optimal machine learning classifier (i.e., the RF classifier) and the optimal feature combination (i.e., feature combination after an automated feature selection method) to classify the aquaculture areas with high accuracy. The results underscore the robustness of the proposed methodology, achieving an outstanding overall accuracy of 93.8%, with an F1 score of 0.94 for aquaculture. The result indicates that an area of 214.6 ± 10.5 km2 of rice-crawfish fields, constituting approximately 83% of the entire aquaculture area in Qianjiang, followed by aquaculture ponds (44.3 ± 10.7 km2, 17%). The proposed hierarchical framework, based on significant phenological characteristics of varied aquaculture types, provides a new approach to monitoring inland freshwater aquaculture in China and other regions of the world.

The phenology-based approach has proven effective for paddy rice mapping due to the unique flooding and transplanting features of rice during the early growing season. However, the method may be greatly affected if no valid observations are available during the flooding and rice transplanting phase. Here, we compare the effects of data availability of different sensors in the critical phenology phase, thereby supporting paddy rice mapping based on phenology-based approaches. Importantly, our study further analyzed the effects of the spatial pattern of the valid observations related to certain factors (i.e., sideslips, clouds, and temporal window lengths of flooding and rice transplanting), which supply the applicable area of the phenology-based approach indications. We first determined the flooding and rice transplanting phase using in situ observational data from agrometeorological stations and remote sensing data, then evaluated the effects of data availability in this phase of 2020 in China using all Landsat-7 and 8 and Sentinel-2 data. The results show that on the country level, the number of average valid observations during the flooding and rice transplanting phase was more than ten for the integration of Landsat and Sentinel images. On the sub-country level, the number of average valid observations was high in the cold temperate zone (17.4 observations), while it was relatively lower in southern China (6.4 observations), especially in Yunnan–Guizhou Plateau, which only had three valid observations on average. Based on the multicollinearity test, the three factors are significantly correlated with the absence of valid observations: (R2 = 0.481) and Std.Coef. (Std. Err.) are 0.306 (0.094), −0.453 (0.003) and −0.547 (0.019), respectively. Overall, these results highlight the substantial spatial heterogeneity of valid observations in China, confirming the reliability of the integration of Landsat-7 and 8 and Sentinel-2 imagery for paddy rice mapping based on phenology-based approaches. This can pave the way for a national-scale effort of rice mapping in China while further indicating potential omission errors in certain cloud-prone regions without sufficient optical observation data, i.e., the Sichuan Basin.

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.