In response to the 2020 COVID-19 pandemic, policymakers worldwide adopted unprecedented measures to limit disease spread, with major repercussions on economic activities and the environment. Here we provide empirical evidence of the impact of a lockdown policy on satellite-measured agricultural land greenness in Badung, a highly populated regency of Bali, Indonesia. Using machine learning and satellite data, we estimate what the Enhanced Vegetation Index (EVI) of cropland would have been without a lockdown. Based on on this counterfactual, we estimate a significant increase in the EVI over agricultural land after the beginning of the lockdown period. The finding is robust to a placebo test. Based on evidence from official reports and international press outlets, we suggest that the observed increase in EVI might be caused by labour reallocation to agriculture from the tourism sector, hardly hit by the lockdown measures. Our results show that machine learning and satellite data can be effectively combined to estimate the effects of exogenous events on land productivity.

为应对 2020 年 COVID-19 大流行，世界各地的政策制定者采取了前所未有的措施来限制疾病传播，这对经济活动和环境产生了重大影响。在这里，我们提供了锁定政策对印度尼西亚巴厘岛人口稠密地区巴东的卫星测量农业土地绿化影响的经验证据。使用机器学习和卫星数据，我们估计了在没有封锁的情况下农田的增强植被指数 (EVI) 会是多少。基于这一反事实，我们估计在封锁期开始后，农业用地的 EVI 显着增加。这一发现对安慰剂测试是可靠的。根据官方报告和国际新闻媒体的证据，我们认为观察到的 EVI 增加可能是由于劳动力从旅游业重新分配到农业造成的，几乎没有受到封锁措施的影响。我们的结果表明，机器学习和卫星数据可以有效结合，以估计外生事件对土地生产力的影响。