种植
环境科学
农业
种植制度
植被(病理学)
复种
气候变化
旱地农业
作物轮作
农林复合经营
地理
遥感
生态学
生物
考古
医学
病理
作者
Zunyi Xie,Yan Zhao,Ruizhu Jiang,Miao Zhang,Graeme Hammer,Scott Chapman,Jason Brider,Andries Potgieter
标识
DOI:10.1016/j.rse.2024.114070
摘要
Fallowing is an important strategy for enhancing soil health, water harvesting and crop yields, thus improving sustainability and reducing production risks in dryland farming systems in Australia. However, accurate data regarding the location, frequency, extent, and duration of fallow land is not readily available at high spatio-temporal resolutions before and during a cropping season. As a result, continental maps depicting seasonal dynamics of fallow and cropping lands and their responses to climate change and human activities remain missing. This study set out to develop an automated approach to discriminate fallow areas from cropping areas within Australian broadacre cropping region. This was done by applying a mathematical algorithm of turning point detection technique to a curve fitted vegetation index time series from 2016 to 2022, derived from high spatial (10 m) and temporal (5-days) resolution Sentinel-2 data. Specifically, we aimed to derive: (i) 5-day revisit fallow/cropping map layers; (ii) key attributes of the fallow and cropping periods (e.g., start, end, duration); (iii) cropping intensity and area. The proposed approach yielded a high and significant overall accuracy, surpassing 92% with substantial balanced accuracy (>0.9) against >3000 fields for individual winter and summer seasons across Australia. In addition, this study's temporal and geographical outputs, encompassing seasonal attributes, cropping intensity and cropping areas, robustly enhanced the understanding of seasonal cropping dynamics and historical shifts in dryland farming practices. Findings derived from this analysis aligned well with extreme climatic events and changes in crop activities adopted in response to such events. Future research will aim to extract key seasonal dynamics attributes influenced by cropping practices, enhancing the conversion of high-resolution earth observation data into actionable industry knowledge.
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