A new method of sample selections for optimizing phenology model based remote sensing data

doi:10.17521/cjpe.2015.0026

Abstract

Abstract: Aims

Phenology model is considered as the most efficient tool to assess the phonological responses of plants to future climate change. Furthermore, as an important component in dynamic ecological models, the performance of phenology model is of significance for the precision in simulating mass and energy exchanges between land and atmosphere. Combining long time series remote sensing data and climate data to construct regional phenology model may be the only way to solve the problem of deficiency in lacking in-situ observational data on phenology and species-specific phenology models. The objective of this study was to develop a new method of sample selections for constructing phenology in the arid zone of Central Asia where only sparse observational data are available.

Methods

Based the phenology data retrieved from 250 m-resolution MODIS images for the period 2000-2010, a new method is developed for constructing the vegetation phenology model in arid zone. A set of rules were built to select the representative pixels of PFTs (plant function types) surrounding the climate station, making sure that the climate of the representative pixels of PFTs could be represented by the observed meteorological data at the station, and then the phenology data on the representative pixels of PFTs were extracted from the MODIS images and the corresponding climatic data were set as the sample for model fitting and assessment. Forty-six representative pixels of PFTs for desert grassland vegetation and broadleaved deciduous forests were selected under the rules. The phenology model parameters were estimated using data from odd-numbered years and the simulation accuracy was assessed with the independent even-year data. Particle swarm optimization algorithm was used to parameterize the pre-selected model. The root-mean-square error and coefficient of determination were used to examine the performance of model with independent data.

Important findings

The best model for desert steppe vegetation was the modified temperature-precipitation model and the best model for deciduous broadleaved forest was the alternative model. Compared with other documented findings, the new method was proven feasible, and the results also suggested that this new method may improve the spatial match of climatic data and vegetation phenology data, and therefore contribute to improvement of the phenology model accuracy.

Key words: arid zone, phenology model, remote sensing, Central Asia

MA Yong-Gang,ZHANG Chi,CHEN Xi. A new method of sample selections for optimizing phenology model based remote sensing data[J]. Chin J Plan Ecolo, 2015, 39(3): 264-274.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2015.0026

https://www.plant-ecology.com/EN/Y2015/V39/I3/264

Figures/Tables 6

References 42

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模型 Model	参数T₀Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型 Model	固定型/游动型 Fixed or moved (mobile)	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	70	11.18	0.55	79	15.42	0.10
Chuine模型 Chuine model	固定 Fixed		11.94	0.51		18.25	0.15
SW模型 SW model	游动 Moved		10.75	0.57		9.68	0.55
SW模型 SW model	固定 Fixed		13.24	0.42		16.27	0.61
Seq模型 Seq model	游动 Moved		14.01	0.37		18.31	0.12
Seq模型 Seq model	固定 Fixed		14.02	0.37		12.06	0.69
Par模型 Par model	游动 Moved		9.38	0.61		10.72	0.69
Par模型 Par model	固定 Fixed		19.36	-		-	-
Al模型 Al model	游动 Moved		9.20	0.74		19.6	0.37
Al模型 Al model	固定 Fixed		-	0.20		-	-
Al-P模型 Al-P model	游动 Moved		6.60	0.68		16.57	0.51
Al-P模型 Al-P model	固定 Fixed		12.40	0.37		-	0.12
T-P修正模型 Modified T-P model	游动 Moved		7.70	0.83		9.87	0.70
T-P修正模型 Modified T-P model	固定 Fixed		10.25	0.77		14.19	0.64
Chuine-P模型 Chuine-P model	游动 Moved		12.32	0.39		14.13	0.27
Chuine-P模型 Chuine-P model	固定 Fixed		15.25	0.33		16.93	-
T-D模型 T-D model	游动 Moved		9.79	0.67		11.77	0.66
T-D模型 T-D model	固定 Fixed		10.64	0.68		15.18	0.68
T-P-D1模型 T-P-D1 model	游动 Moved		7.78	0.69		10.01	0.48
T-P-D1模型 T-P-D1 model	固定 Fixed		8.60	0.77		11.34	0.71
T-P-D2模型 T-P-D2 model	游动 Moved		8.00	0.47		17.21	0.47
T-P-D2模型 T-P-D2 model	固定 Fixed		10.54	0.64		14.50	0.41

模型 Model	参数T₀Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型 Model	固定型/游动型 Fixed or moved (mobile)	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	70	11.18	0.55	79	15.42	0.10
Chuine模型 Chuine model	固定 Fixed		11.94	0.51		18.25	0.15
SW模型 SW model	游动 Moved		10.75	0.57		9.68	0.55
SW模型 SW model	固定 Fixed		13.24	0.42		16.27	0.61
Seq模型 Seq model	游动 Moved		14.01	0.37		18.31	0.12
Seq模型 Seq model	固定 Fixed		14.02	0.37		12.06	0.69
Par模型 Par model	游动 Moved		9.38	0.61		10.72	0.69
Par模型 Par model	固定 Fixed		19.36	-		-	-
Al模型 Al model	游动 Moved		9.20	0.74		19.6	0.37
Al模型 Al model	固定 Fixed		-	0.20		-	-
Al-P模型 Al-P model	游动 Moved		6.60	0.68		16.57	0.51
Al-P模型 Al-P model	固定 Fixed		12.40	0.37		-	0.12
T-P修正模型 Modified T-P model	游动 Moved		7.70	0.83		9.87	0.70
T-P修正模型 Modified T-P model	固定 Fixed		10.25	0.77		14.19	0.64
Chuine-P模型 Chuine-P model	游动 Moved		12.32	0.39		14.13	0.27
Chuine-P模型 Chuine-P model	固定 Fixed		15.25	0.33		16.93	-
T-D模型 T-D model	游动 Moved		9.79	0.67		11.77	0.66
T-D模型 T-D model	固定 Fixed		10.64	0.68		15.18	0.68
T-P-D1模型 T-P-D1 model	游动 Moved		7.78	0.69		10.01	0.48
T-P-D1模型 T-P-D1 model	固定 Fixed		8.60	0.77		11.34	0.71
T-P-D2模型 T-P-D2 model	游动 Moved		8.00	0.47		17.21	0.47
T-P-D2模型 T-P-D2 model	固定 Fixed		10.54	0.64		14.50	0.41

模型名称 Model name	参数 T₀ Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型名称 Model name	固定型/游动型 Fixed or moved	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	15	9.70	0.71	18	18.30	0.46
Chuine模型 Chuine model	固定 Fixed		11.70	0.42		16.20	0.41
SW模型 SW model	游动 Moved		12.80	0.22		13.95	0.56
SW模型 SW model	固定 Fixed		13.25	0.13		13.73	0.51
Seq模型 Seq model	游动 Moved		12.60	0.31		17.50	0.21
Seq模型 Seq model	固定 Fixed		12.56	0.31		15.88	0.59
Par模型 Par model	游动 Moved		11.90	0.40		12.80	0.57
Par模型 Par model	固定 Fixed		11.90	0.44		12.63	0.58
Al模型 Al model	游动 Moved		8.00	0.82		12.10	0.63
Al模型 Al model	固定 Fixed		14.29	0.62		-	0.29
Al-P模型 Al-P model	游动 Moved		-	-
Al-P模型 Al-P model	固定 Fixed		-	-
T-P修正模型 Modified T-P model	游动 Moved		-	0.44		-	-
T-P修正模型 Modified T-P model	固定 Fixed		-	0.10		-	-
Chuine-P模型 Chuine-P model	游动 Moved		-	-
Chuine-P模型 Chuine-P model	固定 Fixed		-	-
T-D模型 T-D model	游动 Moved		12.66	0.34		17.75	0.33
T-D模型 T-D model	固定 Fixed		13.10	-		19.32	-
T-P-D1模型 T-P-D1 model	游动 Moved		-	0.44		-	-
T-P-D1模型 T-P-D1 model	固定 Fixed		-	-		-	-
T-P-D2模型 T-P-D2 model	游动 Moved		-	0.22		-	-
T-P-D2模型 T-P-D2 model	固定 Fixed		-			-	-

模型名称 Model name	参数 T₀ Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型名称 Model name	固定型/游动型 Fixed or moved	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	15	9.70	0.71	18	18.30	0.46
Chuine模型 Chuine model	固定 Fixed		11.70	0.42		16.20	0.41
SW模型 SW model	游动 Moved		12.80	0.22		13.95	0.56
SW模型 SW model	固定 Fixed		13.25	0.13		13.73	0.51
Seq模型 Seq model	游动 Moved		12.60	0.31		17.50	0.21
Seq模型 Seq model	固定 Fixed		12.56	0.31		15.88	0.59
Par模型 Par model	游动 Moved		11.90	0.40		12.80	0.57
Par模型 Par model	固定 Fixed		11.90	0.44		12.63	0.58
Al模型 Al model	游动 Moved		8.00	0.82		12.10	0.63
Al模型 Al model	固定 Fixed		14.29	0.62		-	0.29
Al-P模型 Al-P model	游动 Moved		-	-
Al-P模型 Al-P model	固定 Fixed		-	-
T-P修正模型 Modified T-P model	游动 Moved		-	0.44		-	-
T-P修正模型 Modified T-P model	固定 Fixed		-	0.10		-	-
Chuine-P模型 Chuine-P model	游动 Moved		-	-
Chuine-P模型 Chuine-P model	固定 Fixed		-	-
T-D模型 T-D model	游动 Moved		12.66	0.34		17.75	0.33
T-D模型 T-D model	固定 Fixed		13.10	-		19.32	-
T-P-D1模型 T-P-D1 model	游动 Moved		-	0.44		-	-
T-P-D1模型 T-P-D1 model	固定 Fixed		-	-		-	-
T-P-D2模型 T-P-D2 model	游动 Moved		-	0.22		-	-
T-P-D2模型 T-P-D2 model	固定 Fixed		-			-	-

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