Mapping the Complex Crop Rotation Systems in Southern China Considering Cropping Intensity, Crop Diversity, and Their Seasonal Dynamics

Abstract

Crop rotation increases crop yield, improves soil health, and reduces plant disease. Mapping crop rotation is difficult because crop data from a single time point do not sufficiently represent the dynamics of a system. Studies have tried to map crop rotation by sequentially combining crop maps. However, this produced a large number of meaningless crop sequences, hindering the assessment of rotational benefits at regional scales. Here, we propose a crop rotation classification scheme that integrates temporal information into static crop maps and develop an innovative approach to map crop rotation. We chose a typical multiple cropping region in southern China. Given that the landscape is characterized by high crop diversity (e.g., food crops, cash crops, and permanent crops) and variable cropping intensity, our classification scheme first defines three main rotation systems, i.e., paddy, vegetable, and orchard systems, and then further divides the systems into nine subsystems according to their seasonal dynamics. Finally, we apply time series of Sentinel-1 and Sentinel-2 images to identify the systems by a hierarchical rule-based method. The map of crop rotation systems in 2020 had producer, user, and overall accuracies of 81%, 79%, and 81%, respectively. The results indicate that integrating temporal information into the classification scheme is vital to representing complex rotation systems and that remotely sensed temporal dynamics of crops are useful to characterize these systems. It also shows that crop rotation can be mapped directly rather than aggregating multiple crop layers, thus providing a new perspective for mapping and understanding crop rotation systems.Index terms-Land monitoring, cropping intensity, crop diversity, crop rotation, Sentinel-1/2, Google Earth Engine (GEE) I. INTRODUCTIONROP rotation is the practice of growing a series of crop types on the same land across consecutive seasons or years [1]. It has been found to increase crop yield, improve soil health, and reduce plant disease [2]-[4], suggesting that crop rotation could be considered an option for ensuring food production without compromising the environment. Crop rotation practices can be found all over the world. Amidst concerns over the impacts of increased chemical inputs on soil and water quality, the United States is Manuscript received May 19, 2022.

Design of an Iterative Method for Crop Disease Analysis Incorporating Graph Attention with Spatial-Temporal Learning and Deep Q-Networks

“…Collaboration between researchers, practitioners, and policymakers is needed in establishing standardized protocols of data collection, labeling, and sharing, and this can be beneficial in this regard, fostering the creation of large-scale annotated datasets that can support robust and generalizable models. Moreover, embracing dynamic modeling approaches will go a long way in enhancing the resilience and efficacy of disease detection systems [2,12]. That is, incorporating techniques such as recurrent neural networks (RNNs) and attention mechanisms can enable modeling of temporal dependencies and contextual information, hence improving the accuracy and reliability of disease predictions.…”

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The Sustainable Niche for Vegetable Production within the Contentious Sustainable Agriculture Discourse: Barriers, Opportunities and Future Approaches

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Integrating Multidimensional Feature Indices and Phenological Windows for Mapping Cropping Patterns in Complex Agricultural Landscape Regions

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