Soil Moisture Estimation and Drought Monitoring in Grape Growing Areas based on Time Series Data of MODIS

Yi GAO; Yibo WANG; Xia ZHANG; Shiyu TAO

doi:10.11873/j.issn.1004-0323.2024.5.1237

Journals >Remote Sensing Technology and Application >Volume 39 >Issue 5 >Page 1237 > Article

Remote Sensing Technology and Application
Vol. 39, Issue 5, 1237 (2024)

Soil Moisture Estimation and Drought Monitoring in Grape Growing Areas based on Time Series Data of MODIS

Yi GAO, Yibo WANG, Xia ZHANG, and Shiyu TAO

Author Affiliations

Aerospace Information Research Institute， Chinese Academy of Sciences， Beijing100101， China

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DOI: 10.11873/j.issn.1004-0323.2024.5.1237 Cite this Article

Yi GAO, Yibo WANG, Xia ZHANG, Shiyu TAO. Soil Moisture Estimation and Drought Monitoring in Grape Growing Areas based on Time Series Data of MODIS[J]. Remote Sensing Technology and Application, 2024, 39(5): 1237 Copy Citation Text

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Fig. 1. Location of Jingyang in Shanxi Province and the distribution of concentrated grape planting areas and sample points

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Fig. 2. Flowchart of the research

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Fig. 3. Remote sensing based drought indices and SM temporal changes of grape in Jingyang

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Fig. 4. Spatial distribution of Remote sensing based drought indices and SM in major grape growing areas of Jingyang County from March to October in 2016

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Fig. 5. Correlation matrix between remote sensing drought index and SM at different growth stages from 2009 to 2016

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Fig. 6. Scatterplots of measured versus estimated SM at mid-ripening stage （July）

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Fig. 7. Accuracy of multi-temporal comprehensive estimation model for SM

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Fig. 8. Drought distribution map of grape growing area in northern Jingyang in 2011 based on Model-RSM and SPEI

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Fig. 9. Distribution map of drought frequency in northern Jingyang during key growth periods for grapes from 2009 to 2016

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遥感干旱指标	计算公式	MODIS 产品名称	来源和参考文献
NDWI	$N D W I = \frac{ρ_{2} - ρ_{7}}{ρ_{2} + ρ_{7}}$	MOD09A1	Chen(2005)^[21]
TVDI	$T V D I = \frac{T_{s} - {T_{s}}_{m i n}}{{T_{s}}_{m a x} - {T_{s}}_{m i n}}$	MOD09A1 MOD11A2	Sandholt(2002)^[22]
CWSI	$C W S I = 1 - \frac{E T}{P E T}$	MOD16A2	Jackson(1981)^[23]
VSWI	$V S W I = \frac{N D V I}{L S T} \times 100$	MOD09A1 MOD11A2	McVicar(2001)^[24]
VCI	$V C I = \frac{N D V I_{i} - N D V I_{m i n}}{N D V I_{m a x} - N D V I_{m i n}}$	MOD09A1	Kogan(1995)^[25]
TCI	$T C I = \frac{L S T_{m a x} - L S T_{i}}{L S T_{m a x} - L S T_{m i n}}$	MOD11A2	Kogan(1995)^[25]

Table 1. Table 1Remote sensing drought index indices

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遥感干旱指标	Person相关系数	余弦相似性	欧氏距离
CWSI	0.257 0*	0.786 8	7.151 1
NDWI	0.654 9**	0.937 2	2.801 9
TVDI	-0.009 8	0.916 0	9.690 3
VSWI	-0.081 8	0.916 4	9.735 5
VCI	0.099 5	0.797 5	7.116 8
TCI	0.275 0**	0.824 9	4.989 6

Table 2. Table 2Similarity of temporal changes between remote sensing drought monitoring indicators and SM

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遥感干旱指标	滞后时相	2009年	2010年	2011年	2012年	2013年	2014年	2015年	2016年
VSWI	0	-0.20	0.45	0.21	0.53	0.57	0.29	0.09	0.28
	1	-0.10	0.58	0.36	0.59	0.54	0.47	0.30	0.35
	2	-0.19	0.51	0.23	0.34	0.50	0.44	0.46	0.10
	3	-0.32	0.47	-0.05	0.14	0.40	0.19	0.20	-0.01
CWSI	0	-0.29	-0.09	-0.47	-0.18	-0.01	-0.42	-0.34	-0.26
	1	-0.12	-0.06	-0.44	-0.07	0.02	-0.23	-0.16	-0.19
	2	-0.24	-0.05	-0.28	-0.08	0.07	-0.13	-0.01	-0.13
	3	-0.10	-0.06	-0.35	-0.08	0.08	-0.12	0.13	-0.14
NDWI	0	0.64	0.69	0.59	0.62	0.60	0.75	0.27	0.46
	1	0.49	0.56	0.62	0.52	0.58	0.62	0.24	0.22
	2	0.34	0.54	0.40	0.33	0.49	0.38	0.41	0.17
	3	0.13	0.36	0.25	0.15	0.36	0.24	0.21	0.10