Research on Inversion of 10-meter Potato Leaf Area Index in Complex Terrain Area of Southwest China

doi:10.11924/j.issn.1000-6850.casb2025-0374

Abstract

Abstract:

To achieve high-accuracy inversion of potato leaf area index (LAI) in the complex terrain of Southwest China and explain factors potentially affecting retrieval accuracy from the perspectives of surface spatial heterogeneity and model parameters, this study focused on potato cultivation areas in Zhaojue and Butuo Counties, Liangshan Prefecture, Sichuan Province. Based on Sentinel-2 imagery, an LAI inversion algorithm integrating the PROSAIL model and a neural network (NN) within SNAP was employed to achieve simple, efficient, and small-sample 10-meter LAI inversion with field validation. A comparison with the 500-m MODIS LAI product was further conducted to assess the advantages of high-resolution LAI in revealing spatial heterogeneity. The results showed that: (1) the 10-meter LAI retrieved using the PROSAIL+NN algorithm exhibited high overall accuracy, with a coefficient of determination (R²) of 0.86 and a root mean square error (RMSE) of 0.28 when compared with field measurements. Regional differences were evident—the retrieval accuracy in Butuo County sample area (RMSE=0.15) exceeded that in Zhaojue County sample area (RMSE=0.38), primarily associated with more complex topography (slopes 1°-14°) and higher surface heterogeneity. (2) Within 500-m pixels, the 10-meter LAI revealed an average within-pixel LAI variance of 2.17, which was 1.6 times the mean LAI of the study area. Furthermore, the maximum and minimum values of the MODIS LAI product were lower by 0.1 and 0.47, respectively, than the corresponding values from this study, and partial null pixels were present. This confirmed that the 10-meter LAI significantly surpasses the traditional 500-meter MODIS LAI product in quantifying spatial heterogeneity and ensuring data completeness. (3) Retrieval accuracy was also constrained by model input parameters: the SNAP default LAI range significantly exceeded the actual potato LAI range, resulting in some anomalous values. Additionally, insufficient representativeness of soil reflectance (ρ_soil) and overestimation of the Band 12 reflectance after atmospheric correction (maximum value 0.7, exceeding the model's valid input range of 0.5) increased uncertainty. The PROSAIL+NN inversion method used in this study enables simple and efficient acquisition of 10-meter LAI products, allowing for precise characterization of the spatial heterogeneity in potato growth across the complex topography of Southwest China. With minimal ground measurements, strong mechanistic underpinnings and transferability, this approach can serve as an effective pathway for crop information acquisition in the mountainous regions of Southwest China, supporting digital agriculture, crop monitoring, and agricultural management.

Key words: leaf area index, remote sensing inversion, potatoes, Sentinel-2, precision validation, southwest mountainous region, digital agriculture

LU Xiaoning, XU Dandan, LIU Ke, XU Weixin, XU Baodong. Research on Inversion of 10-meter Potato Leaf Area Index in Complex Terrain Area of Southwest China[J]. Chinese Agricultural Science Bulletin, 2025, 41(34): 147-156.

Figures/Tables 8

References 26

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样区	样方数量/个	地面测量时间(年-月-日)	Sentinel-2		MODIS
样区	样方数量/个	地面测量时间(年-月-日)	影像获取时间(年-月-日)	影像切片	产品对应时间(年-月-日)	轨道代号
昭觉	9	2018-05-15	2018-05-15	T48RTS	2018-05-09—2018-05-16	h27v06
布拖	14	2018-05-16	2018-05-15	T48RTR	2018-05-09—2018-05-16	h27v06

样区	样方数量/个	地面测量时间(年-月-日)	Sentinel-2		MODIS
样区	样方数量/个	地面测量时间(年-月-日)	影像获取时间(年-月-日)	影像切片	产品对应时间(年-月-日)	轨道代号
昭觉	9	2018-05-15	2018-05-15	T48RTS	2018-05-09—2018-05-16	h27v06
布拖	14	2018-05-16	2018-05-15	T48RTR	2018-05-09—2018-05-16	h27v06

研究样区	LAI_m	LAI_s	标准差	研究样区	LAI_m	LAI_s	标准差
布拖	0.60	0.67	0.03	昭觉	1.20	1.38	0.09
	1.00	0.92	0.04		1.20	1.49	0.15
	1.00	0.27	0.37		1.20	0.58	0.31
	0.70	0.36	0.17		1.20	1.62	0.21
	1.10	0.65	0.23		1.40	1.19	0.11
	0.70	0.46	0.12		1.60	0.15	0.73
	0.70	0.14	0.28		1.60	1.41	0.09
	0.60	0.19	0.21		2.20	1.07	0.56
	0.90	0.29	0.31		1.20	1.80	0.30
布拖	0.60	1.30	0.35	昭觉	-	0.85	-
	0.90	0.34	0.28		1.80	2.30	0.25
	0.60	0.78	0.09		1.60	1.89	0.14
	0.60	0.63	0.01		1.50	0.03	0.73
	0.80	0.63	0.09		1.50	0.21	0.64
	0.60	0.78	0.09		1.00	1.27	0.13
	0.60	0.30	0.15		1.10	1.68	0.29
	0.60	0.49	0.05		1.50	1.31	0.10
	0.60	0.77	0.08		1.20	1.46	0.13
	0.60	0.33	0.13		1.20	0.58	0.31
	0.60	0.26	0.17		1.60	2.00	0.20
	0.60	0.27	0.17		1.30	1.64	0.17
	0.60	0.34	0.13		1.30	0.96	0.17
	0.60	1.44	0.42		1.00	1.48	0.24
	0.60	0.41	0.09		1.10	1.42	0.16
	0.60	1.56	0.48		1.10	1.64	0.27
	0.50	1.01	0.25		0.80	1.86	0.53
	0.50	0.19	0.15		0.90	1.01	0.06
	0.50	0.16	0.17		1.00	0.77	0.11
	0.80	0.14	0.33		1.00	0.33	0.33
	0.60	0.45	0.07		1.30	0.85	0.23
	0.60	0.46	0.07		1.30	1.72	0.21
	0.60	0.83	0.11		0.80	0.19	0.31
	-	0.17	-		0.90	1.67	0.39
	-	0.41	-		0.80	1.63	0.41
	-	0.49	-		0.80	1.32	0.26
	0.50	0.42	0.04		1.10	1.66	0.28
	0.60	0.26	0.17		1.00	0.43	0.28
	0.60	1.41	0.41		0.90	1.98	0.54
	0.60	1.21	0.31		0.80	0.86	0.03
	-	0.71	-		1.20	1.80	0.30
	-	1.47	-		1.20	1.36	0.08
	-	1.70	-		1.30	1.54	0.12
	0.70	0.21	0.24		1.60	1.27	0.17
	0.70	0.35	0.17		1.10	1.00	0.05
	0.70	0.69	0.00		0.80	1.73	0.47
	0.70	1.36	0.33		0.90	1.02	0.06
	0.90	1.02	0.06		1.30	1.36	0.03
	1.00	1.61	0.30		1.30	1.27	0.02
	0.90	1.48	0.29		1.10	1.39	0.15

研究样区	LAI_m	LAI_s	标准差	研究样区	LAI_m	LAI_s	标准差
布拖	0.60	0.67	0.03	昭觉	1.20	1.38	0.09
	1.00	0.92	0.04		1.20	1.49	0.15
	1.00	0.27	0.37		1.20	0.58	0.31
	0.70	0.36	0.17		1.20	1.62	0.21
	1.10	0.65	0.23		1.40	1.19	0.11
	0.70	0.46	0.12		1.60	0.15	0.73
	0.70	0.14	0.28		1.60	1.41	0.09
	0.60	0.19	0.21		2.20	1.07	0.56
	0.90	0.29	0.31		1.20	1.80	0.30
布拖	0.60	1.30	0.35	昭觉	-	0.85	-
	0.90	0.34	0.28		1.80	2.30	0.25
	0.60	0.78	0.09		1.60	1.89	0.14
	0.60	0.63	0.01		1.50	0.03	0.73
	0.80	0.63	0.09		1.50	0.21	0.64
	0.60	0.78	0.09		1.00	1.27	0.13
	0.60	0.30	0.15		1.10	1.68	0.29
	0.60	0.49	0.05		1.50	1.31	0.10
	0.60	0.77	0.08		1.20	1.46	0.13
	0.60	0.33	0.13		1.20	0.58	0.31
	0.60	0.26	0.17		1.60	2.00	0.20
	0.60	0.27	0.17		1.30	1.64	0.17
	0.60	0.34	0.13		1.30	0.96	0.17
	0.60	1.44	0.42		1.00	1.48	0.24
	0.60	0.41	0.09		1.10	1.42	0.16
	0.60	1.56	0.48		1.10	1.64	0.27
	0.50	1.01	0.25		0.80	1.86	0.53
	0.50	0.19	0.15		0.90	1.01	0.06
	0.50	0.16	0.17		1.00	0.77	0.11
	0.80	0.14	0.33		1.00	0.33	0.33
	0.60	0.45	0.07		1.30	0.85	0.23
	0.60	0.46	0.07		1.30	1.72	0.21
	0.60	0.83	0.11		0.80	0.19	0.31
	-	0.17	-		0.90	1.67	0.39
	-	0.41	-		0.80	1.63	0.41
	-	0.49	-		0.80	1.32	0.26
	0.50	0.42	0.04		1.10	1.66	0.28
	0.60	0.26	0.17		1.00	0.43	0.28
	0.60	1.41	0.41		0.90	1.98	0.54
	0.60	1.21	0.31		0.80	0.86	0.03
	-	0.71	-		1.20	1.80	0.30
	-	1.47	-		1.20	1.36	0.08
	-	1.70	-		1.30	1.54	0.12
	0.70	0.21	0.24		1.60	1.27	0.17
	0.70	0.35	0.17		1.10	1.00	0.05
	0.70	0.69	0.00		0.80	1.73	0.47
	0.70	1.36	0.33		0.90	1.02	0.06
	0.90	1.02	0.06		1.30	1.36	0.03
	1.00	1.61	0.30		1.30	1.27	0.02
	0.90	1.48	0.29		1.10	1.39	0.15

研究样区	输入超限占比	输出超限占比	最佳获取占比
昭觉	56	2	42
布拖	52	1	47