Progress of Agricultural Drought Monitoring and Forecasting Using Satellite Remote Sensing

doi:10.12133/j.smartag.2021.3.2.202104-SA002

Abstract

Abstract:

Agricultural drought is a major factor that affects agricultural production. Traditional agricultural drought monitoring is mainly based on meteorological and hydrological data, and although it can provide more accurate drought monitoring results at the point level, there are still limitations in monitoring agricultural drought at the regional scale. The rapid development of remote sensing technology has provided a new mean of monitoring agricultural droughts at the regional scale, especially since the electromagnetic wavelengths sensed by satellite sensors in orbit now cover visible, near-infrared, thermal infrared and microwave wavelengths. It is important to make full use of the rich surface information obtained from satellite remote sensing data for agricultural drought monitoring and forecasting. This paper described the research progress of agricultural drought monitoring based on satellite remote sensing from three aspects: remote sensing index-based method, soil water content method and crop water demand method. The research progress of agricultural drought monitoring based on remote sensing index-based method was elaborated from five aspects: vegetation drought index, temperature drought index, integrated vegetation and temperature drought index, water drought index and microwave drought index; the research progress of agricultural drought monitoring based on soil water content method was elaborated from two aspects: soil water content retrieval based on visible to thermal infrared data and soil water content retrieval based on microwave data; the research progress of agricultural drought monitoring based on crop water demand method was elaborated from two aspects: agricultural drought monitoring based on crop canopy water content retrieval method and crop growth model method. Agricultural drought forecasting is a timeline prediction based on drought monitoring. Based on the summary of the progress of drought monitoring, the research progress of agricultural drought forecasting by the drought index method and the crop growth model method was further briefly described. The existing agricultural drought monitoring methods based on satellite remote sensing were summarized, and its shortcomings were sorted out, and some prospects were put forward. In the future, different remote sensing data sources can be used to combine deep learning methods with crop growth models and based on data assimilation methods to further explore the potential of satellite remote sensing data in the monitoring of agricultural drought dynamics, which can further promote the development of smart agriculture.

Key words: satellite, remote sensing, agricultural drought, crop growth model, monitor, forecast

CLC Number:

S423

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.

Figures/Tables 3

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^［22］	Enhanced Vegetation Index	EVI	EVI=2.5×（NIR-R）/（NIR+6×R-7.5×B+1）（2）
	条件植被指数^［14］	Vegetation Condition Index	VCI	VCI=（NDVI_i-NDVI_min）/（NDVI_max-NDVI_min）×100 （3）
	距平植被指数^［23］	Anomaly Vegetation Index	AVI	AVI=NDVI_i-NDVI_mean （4）
	垂直干旱指数^［24］	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^［15］	Temperature Condition Index	TCI	TCI=（T_B_max-T_B_i）/（T_B_max-T_B_min）×100 （6）
温度干旱指数	归一化温度指数^［25］	Normalized Difference Temperature Index	NDTI	NDTI=（LST_∞-LST_i）/（LST_∞-LST₀）（7）
综合植被-温度干旱指数	植被健康指数^［26］	Vegetation Health Index	VHI	VHI=λ×VCI+（1-λ）×（1-TCI）（8）
	温度植被干旱指数^［27］	Temperature Vegetation Dryness Index	TVDI	TVDI=（T_s-T_{s min}）/（a+b×NDVI-T_{s min}）（9）
	条件植被温度指数^［16］	Vegetation Temperature Condition Index	VTCI	VTCI=（LST_{NDVIi max}-LST_NDVIi）/（LST_{NDVIi max}- LST_{NDVIi min}）（10） LST_{NDVIi max}=a+b×NDVI_i （11） LST_{NDVIi min}=a'+b'×NDVI_i （12）
水分干旱指数	水分亏缺指数^［28］	Water Deficit Index	WDI	WDI=1-ET/PET （13）
	干旱严重程度指数^［29］	Drought Severity Index	DSI	DSI=（Z-Z_mean）/σ_Z （14） Z=（NDVI-NDVI_mean）/σ_NDVI+（ET/PET-（ET/PET）_mean）/ σ_ET/PET （15）
	归一化差异水体指数^［19］	Normalized Difference Water Index	NDWI	NDWI=（R-SWIR）/（R+SWIR）（16）
	短波红外水分胁迫指数^［20］	Shortwave Infrared Water Stress Index	SIWSI	SIWSI=（SWIR-NIR）/（SWIR+NIR）（17）
	水分胁迫指数^［30］	Mositure Stress Index	MSI	MSI=SWIR/NIR （18）
	简单比值水分指数^［31］	Simple Ratio Water Index	SRWI	SRWI=（SWIR-NIR）/（SWIR+NIR）（19）
	归一化多波段干旱指数^［32］	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^［33］	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^［34］	Microwave Vegetation Indice	MVI	MVI=（T_Bv（f₂）-T_Bh（f₂））/（T_Bv（f₁）-T_Bh（f₁））（22）
	温度微波植被干旱指数^［35］	Temperature Microwave Vegetation Index	TMVDI	TMVDI=（LST_i-LST_min）/（a+b×MVI-LST_min）（23）
	土壤湿度指数^［21］	Soil Moisture Index	SMI	SMI=（σ⁰-σ⁰_dry）/（σ⁰_wet-σ⁰_dry）×100 （24）

Table 1

Table 2

Table 3

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适用范围	类型	模型	英文全称
裸土	半经验模型^［42］	Oh	/
	半经验模型^［51］	Dubois	/
	物理模型^［52］	GOM	Geometrical Optics Model
	物理模型^［53］	POM	Physical Optics Model
	物理模型^［54］	SPM	Small Perturbation Model
	物理模型^［55］	SSA	Small Slope Approximation
	物理模型^［43］	IEM	Integral Equation Model
植被覆盖	半经验模型^［47］	WCM	Water Cloud Model
植被覆盖	物理模型^［44］	MIMICS	Michigan Microwave Canopy Scattering

卫星	波段	空间分辨率/km	数据集时段
AQUA^［60］	X	25	2002.6~2011.9
GCOM-W1^［61］	X	25	2012.7至今
FY-3B/C^［62］	X	25	2011.7至今
MetOp-A/B^［63］	C	25	2007.1至今
SMOS^［64］	L	25	2009.11至今
SMAP^［65］	L	36	2015.3至今
SMAP/Sentinel-1^［66］	L/C	3	2015.3至今

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