Details
| Original language | English |
|---|---|
| Article number | 1132 |
| Journal | Environmental Monitoring and Assessment |
| Volume | 196 |
| Issue number | 11 |
| Publication status | Published - 30 Oct 2024 |
Abstract
Early detection of agricultural drought can alert farmers and authorities, enhancing the resilience of the food sector. A framework is proposed for developing a novel regional agricultural drought index (RegCDI) by combining remotely sensed vegetation health, soil moisture and crop water stress via a transparent Shannon’s entropy weighting method. The framework consists of the selection of suitable datasets based on their regional performance, the aggregation of selected drought indicators, the validation of the combined index against crop yield, and the testing of predictive capabilities. The creation and performance of RegCDI are demonstrated for the drought prone Indian state of Odisha. MODIS surface reflectance is selected for crop water stress and GLDAS-2 for assessing soil moisture deficits and vegetation health. Three selected indicators (SMCI, TCI, and SIWSI-1) are combined into RegCDI for Odisha. The performance of RegCDI is evaluated (a) against other popular drought indices and (b) by comparing with seasonal crop yields. RegCDI is used to identify drought hotspots based on drought severity, duration, and propensity over the study area. A reforecast evaluation of RegCDI (up to three months ahead) showed that the indicators based on soil moisture deficit and crop water stress could predict drought conditions up to two months ahead with no less than 80% accuracy. This demonstrated the potential of the RegCDI framework and its component indicators for early warning of drought in Odisha.
Keywords
- Agricultural drought, Drought early warning, Drought hotspot, Remote sensing, Shannon’s entropy method
ASJC Scopus subject areas
- Environmental Science(all)
- Environmental Science(all)
- Pollution
- Environmental Science(all)
- Management, Monitoring, Policy and Law
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Environmental Monitoring and Assessment, Vol. 196, No. 11, 1132, 30.10.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Agricultural drought monitoring and early warning at the regional scale using a remote sensing-based combined index
AU - Satapathy, Trupti
AU - Dietrich, Jörg
AU - Ramadas, Meenu
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/10/30
Y1 - 2024/10/30
N2 - Early detection of agricultural drought can alert farmers and authorities, enhancing the resilience of the food sector. A framework is proposed for developing a novel regional agricultural drought index (RegCDI) by combining remotely sensed vegetation health, soil moisture and crop water stress via a transparent Shannon’s entropy weighting method. The framework consists of the selection of suitable datasets based on their regional performance, the aggregation of selected drought indicators, the validation of the combined index against crop yield, and the testing of predictive capabilities. The creation and performance of RegCDI are demonstrated for the drought prone Indian state of Odisha. MODIS surface reflectance is selected for crop water stress and GLDAS-2 for assessing soil moisture deficits and vegetation health. Three selected indicators (SMCI, TCI, and SIWSI-1) are combined into RegCDI for Odisha. The performance of RegCDI is evaluated (a) against other popular drought indices and (b) by comparing with seasonal crop yields. RegCDI is used to identify drought hotspots based on drought severity, duration, and propensity over the study area. A reforecast evaluation of RegCDI (up to three months ahead) showed that the indicators based on soil moisture deficit and crop water stress could predict drought conditions up to two months ahead with no less than 80% accuracy. This demonstrated the potential of the RegCDI framework and its component indicators for early warning of drought in Odisha.
AB - Early detection of agricultural drought can alert farmers and authorities, enhancing the resilience of the food sector. A framework is proposed for developing a novel regional agricultural drought index (RegCDI) by combining remotely sensed vegetation health, soil moisture and crop water stress via a transparent Shannon’s entropy weighting method. The framework consists of the selection of suitable datasets based on their regional performance, the aggregation of selected drought indicators, the validation of the combined index against crop yield, and the testing of predictive capabilities. The creation and performance of RegCDI are demonstrated for the drought prone Indian state of Odisha. MODIS surface reflectance is selected for crop water stress and GLDAS-2 for assessing soil moisture deficits and vegetation health. Three selected indicators (SMCI, TCI, and SIWSI-1) are combined into RegCDI for Odisha. The performance of RegCDI is evaluated (a) against other popular drought indices and (b) by comparing with seasonal crop yields. RegCDI is used to identify drought hotspots based on drought severity, duration, and propensity over the study area. A reforecast evaluation of RegCDI (up to three months ahead) showed that the indicators based on soil moisture deficit and crop water stress could predict drought conditions up to two months ahead with no less than 80% accuracy. This demonstrated the potential of the RegCDI framework and its component indicators for early warning of drought in Odisha.
KW - Agricultural drought
KW - Drought early warning
KW - Drought hotspot
KW - Remote sensing
KW - Shannon’s entropy method
UR - http://www.scopus.com/inward/record.url?scp=85208164698&partnerID=8YFLogxK
U2 - 10.1007/s10661-024-13265-y
DO - 10.1007/s10661-024-13265-y
M3 - Article
C2 - 39476296
AN - SCOPUS:85208164698
VL - 196
JO - Environmental Monitoring and Assessment
JF - Environmental Monitoring and Assessment
SN - 0167-6369
IS - 11
M1 - 1132
ER -