Abstract
Abstract Accurate crop planting structure (CPS) information and its relationship with the surrounding special environment can provide strong support for the adjustment of agricultural structure in areas with limited cultivated land resources, and it will help regional food security, social economy, and ecological balance adjustment. However, due to the perennial cloudy, rainy, and scattered arable land in Karst mountainous areas, the monitoring of planting structure by traditional remote sensing methods is greatly limited. In this regard, we focus on synthetic aperture radar (SAR) remote sensing, which can penetrate clouds and rain, without light constraints to image. In this article, based on parcel-based temporal sequence SAR, the CPS in South China karst area was extracted by deep learning technology, and the spatial coupling relationship between CPS and karst rocky desertification (KRD) was analyzed. The results showed that: (a) The overall accuracy of CPS classification was 75.98%, which proved that the geo-parcel-based time series SAR has a good effect for the CPS mapping in the karst mountainous areas; (b) Through the analysis of the spatial relationship between the planting structure and KRD, we found that the lower KRD level caused the simpler CPS and the higher KRD grade caused more complex CPS and more richer landscape types. The spatial variation trend of CPS landscape indicates the process of water shortage and the deepening of KRD in farmland; (c) The landscape has higher connectivity (Contagion Index, CI 0.52–1.73) in lower KRD level and lower connectivity (CI 0.83–2.05) in higher KRD level, which shows that the degree of fragmentation and connection of CPS landscape is positively proportional to the degree of KRD. In this study, the planting structure extraction of crops under complex imaging environment was realized by using the farmland geo-parcels-based time series Sentinel-1 data, and the relationship between planting structure and KRD was analyzed. This study provides a new idea and method for the extraction of agricultural planting structure in the cloudy and rainy karst mountainous areas of Southwest China. The results of this study have certain guiding significance for the adjustment of regional agricultural planting structure and the balance of regional development.
Highlights
Agriculture is the foundation of social and economic development [1], and grain output is an important guarantee for social stability [2]
This study provides a new idea and method for the extraction of agricultural planting structure in the cloudy and rainy karst mountainous areas of Southwest China
In order to reveal the interaction and law between planting structure and rocky desertification, we tried to obtain accurate Crop planting structure (CPS) information by using temporal series synthetic aperture radar (SAR) data-based farmland geo-parcels, used the edge extraction method based on convolutional neural networks (CNN) to automatically extract the farmland plots, and used the recursive neural network (RNN) to identify the crop types
Summary
Agriculture is the foundation of social and economic development [1], and grain output is an important guarantee for social stability [2]. CPS mapping employ remote sensing, which is a relatively effective way to get the spatial distribution information due to the relatively large scale and high frequency acquisition of plant growth information [14,15]. This improves the limitations of traditional surveys and subsidiary decision of CPS optimization. In order to reveal the interaction and law between planting structure and rocky desertification, we tried to obtain accurate CPS information by using temporal series SAR data-based farmland geo-parcels, used the edge extraction method based on convolutional neural networks (CNN) to automatically extract the farmland plots, and used the recursive neural network (RNN) to identify the crop types. In order to guide the fine adjustment of CPS in karst mountainous areas and to balance the relations among regional food security, ecological benefits, and farmers’ economic benefits, this article provides feasible ideas and methods
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