Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, Pakistan
Abstract Long-term meteorological droughts disrupt hydrological balances and lead to agricultural droughts that affect crop yield. This study uses remote sensing techniques to analyze agricultural droughts in Punjab, Pakistan, over two decades. From MODIS satellite data, three drought indices, such...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-06095-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849769051220344832 |
|---|---|
| author | Ghani Rahman Samavia Khalid Sana Arshad Muhammad Farhan Ul Moazzam Hyun-Han Kwon |
| author_facet | Ghani Rahman Samavia Khalid Sana Arshad Muhammad Farhan Ul Moazzam Hyun-Han Kwon |
| author_sort | Ghani Rahman |
| collection | DOAJ |
| description | Abstract Long-term meteorological droughts disrupt hydrological balances and lead to agricultural droughts that affect crop yield. This study uses remote sensing techniques to analyze agricultural droughts in Punjab, Pakistan, over two decades. From MODIS satellite data, three drought indices, such as vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI), were generated to identify drought years and assess agricultural impacts during the rabi and kharif cropping seasons from 2001 to 2020. Standardized Yield Residual Series (SYRS) and Standardized Drought Residual Series (SDRS) were used to evaluate the impact of agriculture droughts on rabi crops (wheat, barley, gram) and kharif crops (sugarcane, rice, maize, cotton) and to compute Crop Drought Resilience (CDR). Results showed that Punjab experienced extreme to mild droughts from 2001 to 2018, notably in 2002 and 2008, with yield losses of 39% for rice, 34% for sugarcane, and 25% for wheat. The Mann-Kendall (MK) test indicated a significant (p < 0.001) upward trend in VHI for both cropping seasons, with trend breakpoints in 2009 and 2010. Stepwise linear regression found VHI was most predictive for gram yield (R2 = 0.49), while VCI was most predictive for sugarcane (R2 = 0.56) and rice (R2 = 0.29). Polynomial regression demonstrated that SYRSgram is highly influenced by all drought indices, especially SDRSVHI (R2 = 0.49), followed by SDRSVCI (R2 = 0.44) and SDRSTCI (R2 = 0.28). SYRSsugarcane and SYRSrice crops were primarily affected by SDRSVCI, with correlation coefficients of R2 = 0.62 for sugarcane and R2 = 0.33 for rice. This study concludes that gram, sugarcane, and wheat exhibit high to moderate non-resilience under extreme drought conditions, highlighting the vulnerability of these crops to climate variability. These findings are essential for developing targeted adaptation strategies to mitigate yield loss and ensure sustainable agriculture. |
| format | Article |
| id | doaj-art-0121b48e5bc24f5fa78a7ff138eb1e39 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0121b48e5bc24f5fa78a7ff138eb1e392025-08-20T03:03:36ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-06095-6Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, PakistanGhani Rahman0Samavia Khalid1Sana Arshad2Muhammad Farhan Ul Moazzam3Hyun-Han Kwon4Department of Geography, University of GujratDepartment of Geography, University of GujratDepartment of Geography and Geoinformatics, The Islamia University of BahawalpurDépartement des Sciences de l’environnement, Université du Québec à Trois-RivièresDepartment of Civil and Environmental Engineering, Sejong UniversityAbstract Long-term meteorological droughts disrupt hydrological balances and lead to agricultural droughts that affect crop yield. This study uses remote sensing techniques to analyze agricultural droughts in Punjab, Pakistan, over two decades. From MODIS satellite data, three drought indices, such as vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI), were generated to identify drought years and assess agricultural impacts during the rabi and kharif cropping seasons from 2001 to 2020. Standardized Yield Residual Series (SYRS) and Standardized Drought Residual Series (SDRS) were used to evaluate the impact of agriculture droughts on rabi crops (wheat, barley, gram) and kharif crops (sugarcane, rice, maize, cotton) and to compute Crop Drought Resilience (CDR). Results showed that Punjab experienced extreme to mild droughts from 2001 to 2018, notably in 2002 and 2008, with yield losses of 39% for rice, 34% for sugarcane, and 25% for wheat. The Mann-Kendall (MK) test indicated a significant (p < 0.001) upward trend in VHI for both cropping seasons, with trend breakpoints in 2009 and 2010. Stepwise linear regression found VHI was most predictive for gram yield (R2 = 0.49), while VCI was most predictive for sugarcane (R2 = 0.56) and rice (R2 = 0.29). Polynomial regression demonstrated that SYRSgram is highly influenced by all drought indices, especially SDRSVHI (R2 = 0.49), followed by SDRSVCI (R2 = 0.44) and SDRSTCI (R2 = 0.28). SYRSsugarcane and SYRSrice crops were primarily affected by SDRSVCI, with correlation coefficients of R2 = 0.62 for sugarcane and R2 = 0.33 for rice. This study concludes that gram, sugarcane, and wheat exhibit high to moderate non-resilience under extreme drought conditions, highlighting the vulnerability of these crops to climate variability. These findings are essential for developing targeted adaptation strategies to mitigate yield loss and ensure sustainable agriculture.https://doi.org/10.1038/s41598-025-06095-6Vegetation health indexSeasonal cropsDrought resilience assessmentYield loss prediction |
| spellingShingle | Ghani Rahman Samavia Khalid Sana Arshad Muhammad Farhan Ul Moazzam Hyun-Han Kwon Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, Pakistan Scientific Reports Vegetation health index Seasonal crops Drought resilience assessment Yield loss prediction |
| title | Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, Pakistan |
| title_full | Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, Pakistan |
| title_fullStr | Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, Pakistan |
| title_full_unstemmed | Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, Pakistan |
| title_short | Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, Pakistan |
| title_sort | remote sensing based spatiotemporal assessment of agricultural drought and its impact on crop yields in punjab pakistan |
| topic | Vegetation health index Seasonal crops Drought resilience assessment Yield loss prediction |
| url | https://doi.org/10.1038/s41598-025-06095-6 |
| work_keys_str_mv | AT ghanirahman remotesensingbasedspatiotemporalassessmentofagriculturaldroughtanditsimpactoncropyieldsinpunjabpakistan AT samaviakhalid remotesensingbasedspatiotemporalassessmentofagriculturaldroughtanditsimpactoncropyieldsinpunjabpakistan AT sanaarshad remotesensingbasedspatiotemporalassessmentofagriculturaldroughtanditsimpactoncropyieldsinpunjabpakistan AT muhammadfarhanulmoazzam remotesensingbasedspatiotemporalassessmentofagriculturaldroughtanditsimpactoncropyieldsinpunjabpakistan AT hyunhankwon remotesensingbasedspatiotemporalassessmentofagriculturaldroughtanditsimpactoncropyieldsinpunjabpakistan |