A framework for multi-sensor satellite data to evaluate crop production losses: the case study of 2022 Pakistan floods

• Published in: Scientific reports Vol. 13; no. 1; p. 4240
• Main Authors: Qamer, Faisal Mueen, Abbas, Sawaid, Ahmad, Bashir, Hussain, Abid, Salman, Aneel, Muhammad, Sher, Nawaz, Muhammad, Shrestha, Sravan, Iqbal, Bilal, Thapa, Sunil
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.03.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 704/106; 704/172; 704/844/2739; Agricultural land; Agricultural production; Cover crops; Crop damage; Crop production; Crops; Extreme weather; Flash floods; Floods; Food security; Humanities and Social Sciences; Monsoons; multidisciplinary; Rainfall intensity; Rehabilitation; Science; Science (multidisciplinary); Sugarcane; Pakistan
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-30347-y

In August 2022, one of the most severe floods in the history of Pakistan was triggered due to the exceptionally high monsoon rainfall. It has affected ~ 33 million people across the country. The agricultural losses in the most productive Indus plains aggravated the risk of food insecurity in the country. As part of the loss and damage (L&D) assessment methodologies, we developed an approach for evaluating crop-specific post-disaster production losses based on multi-sensor satellite data. An integrated assessment was performed using various indicators derived from pre- and post-flood images of Sentinel-1 (flood extent mapping), Sentinel-2 (crop cover), and GPM (rainfall intensity measurements) to evaluate crop-specific losses. The results showed that 2.5 million ha (18% of Sindh’s total area) was inundated out of which 1.1 million ha was cropland. The remainder of crop damage came from the extreme rainfall downpour, flash floods and management deficiencies. Thus approximately 57% (2.8 million ha) of the cropland was affected out of the 4.9 million ha of agricultural area in Sindh. The analysis indicated expected production losses of 88% (3.1 million bales), 80% (1.8 million tons), and 61% (10.5 million tons) for cotton, rice, and sugarcane. This assessment provided useful tools to evaluate the L&D of agricultural production and to develop evidence-based policies enabling post-flood recovery, rehabilitation of people and restoration of livelihood.