Smart Agriculture ›› 2021, Vol. 3 ›› Issue (2): 1-14.doi: 10.12133/j.smartag.2021.3.2.202104-SA002
收稿日期:
2021-04-15
修回日期:
2021-05-25
出版日期:
2021-06-30
发布日期:
2021-08-25
基金资助:
作者简介:
韩 东(1994-),男,博士研究生,研究方向为农业定量遥感。E-mail:通讯作者:
王鹏新
E-mail:hd5877@cau.edu.cn;wangpx@cau.edu.cn
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
摘要:
干旱是影响农业生产的主要气候因素。传统的农业干旱监测主要是基于气象和水文数据,虽然能提供监测点上较为精确的干旱监测结果,但是在监测面上的农业干旱时,仍存在一定的局限。遥感技术的快速发展,尤其是目前在轨的卫星传感器感测的电磁波段涵盖了可见光、近红外、热红外和微波等波段,为区域尺度农业干旱监测提供了新的手段。充分利用卫星遥感数据获得的丰富地表信息进行农业干旱监测和预测具有重要的研究意义。本文从遥感指数方法、土壤含水量方法和作物需水量方法三个方面阐述了基于卫星遥感的农业干旱监测研究进展。农业干旱预测是在干旱监测的基础上进行时间轴的预测,本文在总结干旱监测进展的基础上,进一步简述了以干旱指数方法和作物生长模型方法为主的农业干旱预测研究进展。
中图分类号:
韩东, 王鹏新, 张悦, 田惠仁, 周西嘉. 农业干旱卫星遥感监测与预测研究进展[J]. 智慧农业(中英文), 2021, 3(2): 1-14.
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.
表1
主要干旱监测遥感指数
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) |
表2
基于微波遥感的土壤水分反演主要模型
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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