Drought monitoring represents a challenge for water and agricultural sector as this natural hazard accelerates water deficiency and leads to adverse environmental and socioeconomic impacts. The use of remote sensing d...Drought monitoring represents a challenge for water and agricultural sector as this natural hazard accelerates water deficiency and leads to adverse environmental and socioeconomic impacts. The use of remote sensing data and geospatial techniques to monitor and map drought severity expanded in the last decades with progressive developments in data sources and processing. This study investigates the correlations among drought indices derived with soil moisture stress (K) obtained from ground data collected from fields cultivated with barley. The study, carried out in Yarmouk basin in the north of Jordan, includes NDVI, PDI, MPDI and PVI derived from Landsat 8-OLI and Sentinel 2-MSI. Results showed different behavior among the indices and throughout the 2016/2017 growing season, with maximum correlation between PDI and MPDI followed by NDVI with PVI. Correlations among the remote sensing indices and K for different soil depths during March-April were significant for most indices with a maximum (R2) of 0.82 for K30-50 and MPDI, followed by K30-50 with NDVI. Drought severity maps for the month of March showed different trends for the different indices, with similarities between MPDI and PDI. The map of drought severity combined from the remote sensing indices and K showed that PDI and soil moisture could significantly explain 56% of variations in spatial patterns of drought, while the combination of MPDI, PDI and NDVI could significantly explain up to 59% of variations in drought severity map. Therefore, the study recommends the adoption of these remotely sensed indices for monitoring and mapping of agricultural droughts.展开更多
文摘Drought monitoring represents a challenge for water and agricultural sector as this natural hazard accelerates water deficiency and leads to adverse environmental and socioeconomic impacts. The use of remote sensing data and geospatial techniques to monitor and map drought severity expanded in the last decades with progressive developments in data sources and processing. This study investigates the correlations among drought indices derived with soil moisture stress (K) obtained from ground data collected from fields cultivated with barley. The study, carried out in Yarmouk basin in the north of Jordan, includes NDVI, PDI, MPDI and PVI derived from Landsat 8-OLI and Sentinel 2-MSI. Results showed different behavior among the indices and throughout the 2016/2017 growing season, with maximum correlation between PDI and MPDI followed by NDVI with PVI. Correlations among the remote sensing indices and K for different soil depths during March-April were significant for most indices with a maximum (R2) of 0.82 for K30-50 and MPDI, followed by K30-50 with NDVI. Drought severity maps for the month of March showed different trends for the different indices, with similarities between MPDI and PDI. The map of drought severity combined from the remote sensing indices and K showed that PDI and soil moisture could significantly explain 56% of variations in spatial patterns of drought, while the combination of MPDI, PDI and NDVI could significantly explain up to 59% of variations in drought severity map. Therefore, the study recommends the adoption of these remotely sensed indices for monitoring and mapping of agricultural droughts.
