Comparing crop calendars: phenology derived from Sentinel-2 data vs official data: The case of cereals in Andalusia.

crossref(2024)

引用 0|浏览2
暂无评分
摘要
Obtaining specific field calendars for each crop is very useful information for farmers and public administration to understand and manage harvests. This information can be collected manually from each farm, but this approach is highly time and money consuming. It is possible to acquire it more efficiently using phenology estimates obtained through remote sensing. Common Agrarian Policy (CAP) and Geographical Information System of the Common Agrarian Policy (GISCAP) data were used to know the location of principal cereal plots in Andalusia, Spain. It included common wheat, durum wheat, triticale, oat, rye, barley, sorghum, maize and rice.  Several phenometrics from Sentinel-2 were obtained: start of the season (SOS), middle of the season (MOS), length of the season (LOS) and end of the season (EOS). This dataset was correlated and compared with sowing and harvesting data collected by the Spanish government.  The results showed a high correlation between SOS and sowing and between EOS and harvest for most of the studied crops.Sowing for common wheat, durum wheat, triticale, oat, rye, and barley took place between October and December according to government calendars, while SOS generally started one month later, between November and January. However, in these crops, harvest and EOS occurred simultaneously, mostly in June. In the case of sorghum, maize and rice, which are summer cereals, their phenometrics differed from the others. Sowing and SOS for sorghum mostly occurred in April and March, and harvest and EOS in September and October, in typically at the same time. Maize sowing took place in March, SOS in April, and harvest and EOS in September. Finally, rice sowing occurred in May, SOS in June, harvest in October and EOS in November. This study shows that obtaining accurate crop calendars from Sentinel-2 phenological trajectories is feasible, providing valuable information for farmers and public administrations.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要