Study on monitoring method of agricultural drought in Gansu Province based on Temperature Vegetation Dryness Index

doi:10.11755/j.issn.1006-7639(2024)-01-0027

Abstract

Abstract:

Improving the Temperature Vegetation Dryness Index (TVDI) and clarifying the agricultural drought grade threshold of TVDI is of great significance for improving the ability of TVDI to monitor agricultural drought. Based on MODIS (Moderate Resolution Imaging Spectroradiometer) remote sensing data in the past 19 years, several feature spaces are constructed by using the single-time and multi-time methods, including NDVI (Normalized Difference Vegetation Index) -LST (Land Surface Temperature), EVI (Enhanced Vegetation Index) -LST, RVI (Ratio Vegetation Index) - LST, and SAVI (Soil Adjusted Vegetation Index) -LST. The calculation methods of TVDI are discussed, the applicability of TVDI for agricultural drought monitoring in Gansu Province is analyzed, and classification standards for summer TVDI agricultural drought in Gansu Province are clarified. The research results are as follows: 1) The TVDI calculated from the SAVI-LST feature space is more suitable for agricultural drought monitoring in Gansu Province. The root mean squared error (RMSE) and mean absolute error (MAE) of its fitting relative soil moisture (RSM) decreased by 1%-5% compared with the RMSE and MAE of RSM fitted by NDVI-LST feature space TVDI for RSM, which is used more commonly. 2) TVDI is suitable for agricultural drought monitoring at shallow depths of 10 and 20 cm in non-arid areas such as semi-arid, semi-humid and humid areas in Gansu Province in summer. The RMSE and MAE are approximately 15.6% and 12.6%, and the fitting errors in humid areas are the least, and they are less in semi-humid areas than in semi-arid areas they are the largest. 3) Compared to TVDI drought grades divided by 0.2 intervals and TVDI with uncertain classification criteria, the TVDI agricultural drought grade determined by the linear relationship between TVDI and RSM is more conducive to improving the accuracy of TVDI monitoring agricultural drought.

Key words: temperature vegetation dryness index, drought monitoring, multi-time method, relative soil moisture

摘要：

改进温度植被干旱指数（Temperature Vegetation Dryness Index，TVDI）并明确TVDI的农业干旱等级阈值，对提高TVDI指数监测农业干旱能力有重要意义。利用近19 a的MODIS（Moderate Resolution Imaging Spectro-radiometer，MODIS）遥感数据，基于单时次和多时次方法构建NDVI（Normalized Difference Vegetation Index，NDVI）-LST（Land Surface Temperature，LST）、EVI（Enhanced Vegetation Index，EVI）-LST、RVI（Ratio Vegetation Index，RVI）-LST、SAVI（Soil-Adjusted Vegetation Index，SAVI）-LST等几种特征空间，讨论TVDI计算方法，分析TVDI在甘肃省农业干旱监测中的适用性，并明确甘肃省夏季TVDI农业干旱分级标准。结果表明：（1）基于多时次方法构建的 SAVI-LST特征空间TVDI更适合甘肃省农业干旱监测，其对土壤相对湿度（Relative Soil Moisture，RSM）拟合的均方根误差（Root Mean Squared Error，RMSE）和平均绝对误差（Mean Absolute Error，MAE）比NDVI-LST特征空间TVDI对RSM拟合的RMSE和MAE下降1%~5%；（2）TVDI适用于夏季甘肃省半干旱区、半湿润区、湿润区等非干旱区浅层10、20 cm土壤深度的农业干旱监测，RMSE和MAE约15.6%和12.6%，拟合误差湿润区<半湿润区<半干旱区；（3）利用TVDI与RSM线性关系确定的TVDI农业干旱等级更有利于提高TVDI监测农业干旱的准确性。

关键词: 温度植被干旱指数, 干旱监测, 多时次方法, 土壤相对湿度

CLC Number:

P426.68

SHA Sha, WANG Lijuan, WANG Xiaoping, HU Die, ZHANG Liang. Study on monitoring method of agricultural drought in Gansu Province based on Temperature Vegetation Dryness Index[J]. Journal of Arid Meteorology, 2024, 42(1): 27-38.

沙莎, 王丽娟, 王小平, 胡蝶, 张良. 基于温度植被干旱指数（TVDI）的甘肃省农业干旱监测方法研究[J]. 干旱气象, 2024, 42(1): 27-38.

Figures/Tables 12

Fig.1 The climate zoning and distribution of agrometeorological stations in Gansu Province

Tab.1 Calculation formulas of vegetation indexes

植被指数	计算公式
NDVI	$N D V I = (b 02 - b 01) (b 02 + b 01)$ （Gu et al.，2007）
EVI	$E V I = 2.5 × (b 02 - b 01) (b 02 + 6.0 × b 01 - 7.5 × b 03 + 1)$ （Liu and Huete，1995）
SAVI	$S A V I = (b 02 - b 01) (b 02 + b 01 + L) × (1 + L), L 取 0.5$ （Huete，1988）
RVI_b2b1	$R V I 1 = b 02 b 01$ （Birth and Mcvey，1968）
RVI_b2b7	$R V I 2 = b 02 b 07$
NDWI	$N D W I = (b 02 - b 07) (b 02 + b 07)$ （Gao，1996）

Tab.1 Calculation formulas of vegetation indexes

植被指数	计算公式
NDVI	$N D V I = (b 02 - b 01) (b 02 + b 01)$ （Gu et al.，2007）
EVI	$E V I = 2.5 × (b 02 - b 01) (b 02 + 6.0 × b 01 - 7.5 × b 03 + 1)$ （Liu and Huete，1995）
SAVI	$S A V I = (b 02 - b 01) (b 02 + b 01 + L) × (1 + L), L 取 0.5$ （Huete，1988）
RVI_b2b1	$R V I 1 = b 02 b 01$ （Birth and Mcvey，1968）
RVI_b2b7	$R V I 2 = b 02 b 07$
NDWI	$N D W I = (b 02 - b 07) (b 02 + b 07)$ （Gao，1996）

Fig.2 Monthly changes of vegetation indices and surface temperature in different climatic regions in Gansu Province

Fig.3 Monthly variation of correlation coefficient between TVDI constructed by multi-time method（the left） and single-time method（the right） and RSM at 10 cm depth

Tab.2 Temporal correlation statistics between TVDI for different feature space and monthly RSM at 10 cm depth time series from 2003 to 2012 in different climatic regions

构建方法	气候分区	NDVI特征空间		EVI特征空间		SAVI特征空间		RVI_b2b1特征空间
构建方法	气候分区	r	p	r	p	r	p	r	p
多时次方法	干旱区	-0.12	0.38	-0.01	0.46	-0.10	0.37	-0.03	0.45
	半干旱区	-0.32	0.09	-0.27	0.14	-0.34*	0.09*	-0.30	0.16
	半湿润区	-0.36	0.05	-0.28	0.14	-0.38*	0.03*	-0.30	0.10
	湿润区	-0.26	0.08	-0.24	0.16	-0.29*	0.02*	-0.17	0.34
单时次方法	干旱区	-0.09	0.45	-0.03	0.39	-0.09	0.42	0.01	0.47
	半干旱区	-0.26	0.10	-0.26	0.15	-0.29	0.09	-0.28	0.14
	半湿润区	-0.32	0.04	-0.31	0.07	-0.32	0.14	-0.27	0.16
	湿润区	-0.22	0.15	-0.27	0.11	-0.26	0.08	-0.15	0.35

Fig.4 Monthly variations of correlation coefficients between TVDI and RSM at different depth in different climatic regions in Gansu Province

Tab. 3 Spatial correlation statistics between TVDI for different feature space and RSM at 10 cm depth from June to October

Tab.4 Agricultural drought grade thresholds based on TVDI in summer in Gansu Province

农业干旱等级	6月	7月	8月
无旱	TVDI<0.54	TVDI<0.52	TVDI<0.52
轻旱	0.54≤TVDI<0.62	0.52≤TVDI<0.63	0.52≤TVDI<0.59
中旱	0.62≤TVDI<0.70	0.63≤TVDI<0.75	0.59≤TVDI<0.65
重旱	0.70≤TVDI<0.78	0.75≤TVDI<0.87	0.65≤TVDI<0.72
特旱	TVDI≥0.78	TVDI≥0.87	TVDI≥0.72

Fig.5 Scatter plots of the fitting values and measured values of RSM in Gansu Province in June（a），July（b） and August（c）

Fig.6 Comparison of drought distribution based on TVDI classified according to the table 4（the left），in 0.2 intervals（the middle） and TVDI without classification（the right） and RSM in summer of 2005 in Gansu Province

Fig.7 Precipitation anomaly percentage in June（a），July（b） and August（c） in 2016 in Gansu Province（Unit： %）

Fig.8 Drought grade map based on TVDI in June（a），July（c） and August（e） and the actual measured relative soil moisture values of 0-30 cm depth on June 16（b），July 16（d） and August 16（f） of 2016

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