使用多源数据集估算2019财年大米净产量

A. Rehman, Muhammad Ayyaz, Farzeen Riaz, Sajid Ali, M. Tanveer, Iqra Manzoor, Hafiz Adnan Ashraf., S. Mahmood
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引用次数: 3

摘要

小农受到各种脆弱风险的威胁,包括恶劣的天气条件、不定期降雨、疾病爆发和市场冲击。农作物保险是减轻这些风险的唯一解决方案。作物产量记录对于预测一个地区的作物产量/面积非常重要,但像巴基斯坦这样的发展中国家的作物产量记录有限。巴基斯坦旁遮普省的作物报告服务(CRS)已经采取了这一举措来保存与作物有关的数据。利用2008-2018年基于CRS的水稻作物数据集,对2019财年水稻产量/面积进行预测。将基于CRS的数据集与遥感数据集相结合,以获得更准确的结果。以水稻作物的光谱响应作为输入,计算每年的NDVI/RVI值。通过对NDVI/RVI和基于CRS的产量进行线性回归,建立回归方程,预测2019年水稻产量为2.09(吨/公顷)。采用监督分类法估计水稻种植面积为139616 hm2。大米净产量估计为219797吨。水稻作物冠层的光谱响应是确定净产量的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Net Rice Production for the Fiscal year 2019 using Multisource Datasets.
Smallholder farmers are threatened by various vulnerable risks which include hostile weather conditions, rainfall at odd times, disease outbreaks and the market shocks. Crop insurance is the only solution to mitigate these risks. Crop yield records are of great importance to predict the crop yield/area into a region but the developing countries like Pakistan, have limited availability of crop yield records. Crop Reporting Service (CRS) in Punjab province of Pakistan has taken this initiative to save crop related data. We obtained the CRS based datasets of rice crop from (2008-2018) to predict the rice yield/area for the fiscal year 2019. The CRS based datasets were incorporated in collaboration with remotely sensed dataset to obtain more accurate results. The spectral responses of rice crop were taken as input to compute NDVI/RVI values of each year. We applied linear regression to NDVI/RVI and the CRS based yield to generate regression equations for prediction of rice yield for the year 2019 which was computed as 2.09 (ton/ha). The area under rice cultivation was estimated using supervised classification that was 139616 hectors. The net rice production was estimated as 219797 tons. Spectral responses of rice crop canopy proved efficient to determine the net productions.
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