Inversion of Rice Field Temperature Based on Satellite Remote Sensing and Automatic Weather Station Data: Taking Anhui Province as an Example

doi:10.11924/j.issn.1000-6850.casb2020-0491

Abstract

Abstract:

With the global warming and frequent occurrence of high temperature events, the probability of rice being affected by high temperatures increases. Rice field temperature is the foundation for studying the high temperature heat damage and its impact on rice. Therefore, the wide-scale inversion of rice field temperature could help relevant departments set up a wide-range rice field temperature data in a short time and carry out research on rice high temperature heat damage and decision making. In this study, the 8-day synthetic data of MOD09A1 was used to extract rice planting areas in Anhui Province. Then the temperature of the AWS (automatic weather station) in the rice planting area and the four LST values such as MOD11A1 and MYD11A1 were used for multiple stepwise regression. The optimal equation for estimating the maximum and average temperature from remote sensing data was obtained, with the R² of 0.728 and 0.825, root mean square errors (RSME) of 2.21 and 1.54, and mean absolute errors (MEA) of 1.73 and 1.15, respectively. Finally, the method of inverting rice field air temperature based on satellite remote sensing information and AWS air temperature data was developed. The method was well applied in the extraction of rice planting areas and the inversion of final temperature in Anhui Province in 2017.

Key words: remote sensing information, rice field, temperature, inversion, weather station, Anhui

CLC Number:

P414.4

Pema Rigzin, Dekey Yongzom, Sonamlang Yangchen, Lha ba, Zhang Xinlei, Dhonyo Dorji, Tashi Ngodup. Inversion of Rice Field Temperature Based on Satellite Remote Sensing and Automatic Weather Station Data: Taking Anhui Province as an Example[J]. Chinese Agricultural Science Bulletin, 2021, 37(2): 88-95.

Figures/Tables 11

References 25

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数据产品	波段	系数
MOD09A1	B1~B7	0.0001
MYD11A1	LST-Night	0.0001
MOD11A1	LST-Day	0.02
MCD12Q1	B1	NA

数据产品	波段	系数
MOD09A1	B1~B7	0.0001
MYD11A1	LST-Night	0.0001
MOD11A1	LST-Day	0.02
MCD12Q1	B1	NA

像元类型	满足条件
水稻像元	LSWI≥EVI-0.05
	2×EVI₍_d_0-_d₄₀₎>EVI_max
	MODIS_QA
	MCD12Q1!=WATER
	~NDVI_all>0.7

像元类型	满足条件
水稻像元	LSWI≥EVI-0.05
	2×EVI₍_d_0-_d₄₀₎>EVI_max
	MODIS_QA
	MCD12Q1!=WATER
	~NDVI_all>0.7

地区	遥感提取值/万hm²	实际统计值/万hm²	误差/%
阜阳	7.38	7.57	2.50
亳州	0.40	0.36	10.86
宿州	0.81	0.75	7.49
淮北	0.02	0.02	11.76
蚌埠	10.35	11.19	7.52
淮南	18.93	20.52	7.76
滁州	34.41	37.88	9.16
合肥	28.13	31.01	9.30
六安	35.06	32.48	7.93
安庆	22.83	25.35	9.94
池州	15.79	14.77	6.91
铜陵	6.76	7.11	4.86
芜湖	15.00	13.71	9.42
马鞍山	11.05	10.25	7.83
宣城	12.82	13.89	7.68
黄山	3.74	3.37	10.98