Phenology windows and multi-source medium-/high-resolution image extraction for rice-crayfish paddy fields mapping

Rice-crayfish co-culture is a kind of comprehensive ecological agriculture pattern. Rice-crayfish co-culture has expanded rapidly in China in the past decade due to its outstanding ecological and economic benefits. The accurate spatial distribution information of this newly emerging agricultural pattern is crucial for growth monitoring, yield estimation, and water resource management. However, most studies have focused on field-level research on the farmland ecosystem, and rice-crayfish mapping at regional or larger scales has received less attention. In this study, Qianjiang City in Hubei Province, known as the“hometown of crayfish”was selected as the test area. The cloud computing approach was used for all available Landsat 7/8 and Sentinel-2 imagery in 2019 with the Google Earth Engine (GEE) platform. By analyzing farming characteristics and the spectral curves of rice-crayfish fields, we identified the crucial phenology windows and classification features (i.e., flooding and vegetation signals) for rice-crayfish mapping. On the basis of the key phenological characteristics and associated frequency thresholds derived from field samples, we developed a rule-based algorithm for rice-crayfish mapping and generated the rice-crayfish map of Qianjiang City in 2019. To further evaluate the potential of our proposed method, we compared it with the random forest method and a method based on seasonal differences of water bodies. The spectral analysis of time series images showed that the unique phenological characteristics of rice-crayfish co-culture were flooding signal (LSWI > NDVI or LSWI > EVI) in phenology window 1 (from January 1 to April 30), vegetation signal (NDVI > LSWI or EVI > LSWI) in phenology window 2 (from July 15 to September 30), and flooding signal in phenology window 3 (from November 10 to December 31). With the mapping results, the total area for rice-crayfish planting in Qianjiang City in 2019 was estimated to 575.58 km2, and rice-crayfish plots were mainly distributed in the southwest. The producer’s accuracy of the classification result was 90.74%, the user’s accuracy was 94.69%, and the overall accuracy was 95.23%. The method based on phenology window features had fewer commission errors compared with the random forest method and fewer omission errors compared with the method based on water body seasonal differences. Among the three methods, the proposed method presented the highest overall classification accuracy. The rice-crayfish mapping method based on phenology windows, flooding signal, and vegetation signal showed high separability. The method based on phenology windows can be easily generalized to other regions and other images because of its strong physical interpretation for rice-crayfish. On the one hand, this method has relatively low dependence on training samples. On the other hand, as long as the key phenology window can be obtained, other medium-high resolution images, such as GF-1 and GF-6, can achieve high-accuracy mapping results for rice-crayfish. Therefore, the method based on phenology windows can be effectively extended to large areas and long time series. It can provide essential information for rice production management and decision-making in the crayfish industry. © 2022 National Remote Sensing Bulletin. All rights reserved.