Smart Agriculture ›› 2021, Vol. 3 ›› Issue (2): 1-14.doi: 10.12133/j.smartag.2021.3.2.202104-SA002
• Topic--Application of Spatial Information Technology in Agriculture • Previous Articles Next Articles
HAN Dong(), WANG Pengxin(
), ZHANG Yue, TIAN Huiren, ZHOU Xijia
Received:
2021-04-15
Revised:
2021-05-25
Online:
2021-06-30
Published:
2021-08-25
corresponding author:
Pengxin WANG
E-mail:hd5877@cau.edu.cn;wangpx@cau.edu.cn
CLC Number:
HAN Dong, WANG Pengxin, ZHANG Yue, TIAN Huiren, ZHOU Xijia. Progress of Agricultural Drought Monitoring and Forecasting Using Satellite Remote Sensing[J]. Smart Agriculture, 2021, 3(2): 1-14.
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URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.2021.3.2.202104-SA002
Table 1
Major remote sensing indices for drought monitoring
类型 | 中文名称 | 英文名称 | 英文缩写 | 定义 |
---|---|---|---|---|
植被干旱指数 | 归一化植被指数 | Normalized Difference Vegetation Index | NDVI | NDVI=(NIR-R)/(NIR+R) (1) |
增强植被指数[ | Enhanced Vegetation Index | EVI | EVI=2.5×(NIR-R)/(NIR+6×R-7.5×B+1) (2) | |
条件植被指数[ | Vegetation Condition Index | VCI | VCI=(NDVIi-NDVImin)/(NDVImax-NDVImin)×100 (3) | |
距平植被指数[ | Anomaly Vegetation Index | AVI | AVI=NDVIi-NDVImean (4) | |
垂直干旱指数[ | Perpendicular Drought Index | PDI | PDI=1/((M2+1)-1/2)×(R+M×NIR) (5) | |
温度干旱指数 | 条件温度指数[ | Temperature Condition Index | TCI | TCI=(TBmax-TBi)/(TBmax-TBmin)×100 (6) |
归一化温度指数[ | Normalized Difference Temperature Index | NDTI | NDTI=(LST∞-LSTi)/(LST∞-LST0) (7) | |
综合植被-温度干旱指数 | 植被健康指数[ | Vegetation Health Index | VHI | VHI=λ×VCI+(1-λ)×(1-TCI) (8) |
温度植被干旱指数[ | Temperature Vegetation Dryness Index | TVDI | TVDI=(Ts-Ts min)/(a+b×NDVI-Ts min) (9) | |
条件植被温度指数[ | Vegetation Temperature Condition Index | VTCI | VTCI=(LSTNDVIi max-LSTNDVIi)/(LSTNDVIi max- LSTNDVIi min) (10) LSTNDVIi max=a+b×NDVIi (11) LSTNDVIi min=a'+b'×NDVIi (12) | |
水分干旱指数 | 水分亏缺指数[ | Water Deficit Index | WDI | WDI=1-ET/PET (13) |
干旱严重程度指数[ | Drought Severity Index | DSI | DSI=(Z-Zmean)/σZ (14) Z=(NDVI-NDVImean)/σNDVI+(ET/PET-(ET/PET)mean)/ σET/PET (15) | |
归一化差异水体 指数[ | Normalized Difference Water Index | NDWI | NDWI=(R-SWIR)/(R+SWIR) (16) | |
短波红外水分胁迫 指数[ | Shortwave Infrared Water Stress Index | SIWSI | SIWSI=(SWIR-NIR)/(SWIR+NIR) (17) | |
水分胁迫指数[ | Mositure Stress Index | MSI | MSI=SWIR/NIR (18) | |
简单比值水分指数[ | Simple Ratio Water Index | SRWI | SRWI=(SWIR-NIR)/(SWIR+NIR) (19) | |
归一化多波段干旱 指数[ | Normalized Multi-band Drought Index | NMDI | NMDI=(NIR-(SWIR1-SWIR2))/(NIR+(SWIR1+ SWIR2)) (20) | |
可见光和短波红外干旱指数[ | Visible and Short-wave Infrared Drought Index | VSDI | VSDI=1-((SWIR-B)+(R-B)) (21) | |
微波干旱指数 | 微波植被指数[ | Microwave Vegetation Indice | MVI | MVI=(TBv(f2)-TBh(f2))/(TBv(f1)-TBh(f1)) (22) |
温度微波植被干旱 指数[ | Temperature Microwave Vegetation Index | TMVDI | TMVDI=(LSTi-LSTmin)/(a+b×MVI-LSTmin) (23) | |
土壤湿度指数[ | Soil Moisture Index | SMI | SMI=(σ0-σ0dry)/(σ0wet-σ0dry)×100 (24) |
Table 2
Major soil moisture retrieval models based on microwave remote sensing
适用范围 | 类型 | 模型 | 英文全称 |
---|---|---|---|
裸土 | 半经验模型[ | Oh | / |
半经验模型[ | Dubois | / | |
物理模型[ | GOM | Geometrical Optics Model | |
物理模型[ | POM | Physical Optics Model | |
物理模型[ | SPM | Small Perturbation Model | |
物理模型[ | SSA | Small Slope Approximation | |
物理模型[ | IEM | Integral Equation Model | |
植被覆盖 | 半经验模型[ | WCM | Water Cloud Model |
物理模型[ | MIMICS | Michigan Microwave Canopy Scattering |
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