Identification and yield prediction of sugarcane in the south-central part of Guangxi Zhuang Autonomous Region, China based on multi-source satellite-based remote sensing images

doi:10.6046/zrzyyg.2023093

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Abstract

This study aims to solve the challenges faced in the prediction of sugarcane yield in Guangxi, such as varied crops, complex investigations in the sugarcane planting areas, and difficult acquisition of remote-sensing images caused by the changeable weather. To this end, an improved semantic segmentation algorithm based on Sentinel-2 images was proposed to automatically identify sugarcane planting areas, and an extraction method for representative spectral features was developed to build a sugarcane yield prediction model based on multi-temporal Sentinel-2 and Landsat8 images. First, an ECA-BiseNetV2 identification model for sugarcane planting areas was constructed by introducing an efficient channel attention (ECA) module into the BiseNetV2 lightweight unstructured network. As a result, the overall pixel classification accuracy reached up to 91.54%, and the precision for sugarcane pixel identification was up to 95.57%. Then, multiple vegetation indices of different growth periods of the identified sugarcane planting areas were extracted, and the Landsat8 image-derived vegetation indices were converted into Sentinel-2 image-based ones using a linear regression model to reduce the differences of the indices derived using images from the two satellites. Subsequently, after the fitting of time-series data of the extracted vegetation indices using a cubic curve, the maximum indices were obtained as the representative spectral features. Finally, a yield prediction model was built using multiple machine learning algorithms. The results indicate that the test set of the decision tree model built using the fitted maximum values of the vegetation indices yielded R? of up to 0.759, 4.3%, higher than that (0.792) of the model built using the available actual maximum values. Therefore, this method can effectively resolve the difficulty in developing an accurate sugarcane yield prediction model caused by changeable weather-induced lack of remote sensing images of sugarcane of the key growth periods.

Keywords semantic segmentation vegetation index sugarcane yield prediction satellite remote sensing time-series

ZTFLH:	S127
	TP751
	TP79

Issue Date: 03 September 2024

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	Wei LUO
	Xiuhua LI
	Huojuan QIN
	Muqing ZHANG
	Zeping WANG
	Zhuhui JIANG

Cite this article:

Wei LUO,Xiuhua LI,Huojuan QIN, et al. Identification and yield prediction of sugarcane in the south-central part of Guangxi Zhuang Autonomous Region, China based on multi-source satellite-based remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(3): 248-258.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023093 OR https://www.gtzyyg.com/EN/Y2024/V36/I3/248

Fig.1 Distribution of the selected sugarcane regions

Tab.1 Statistical information of sugarcane yields

Tab.2 Parameters of the selected bands from satellite images

Fig.2 Satellite images of a sugarcane planting region in different growth periods

Tab.3 Basic information of the satellite remote sensing images

Fig.3 Central coordinate distribution of the sugarcane fields in Liangqi farm

Fig.4 Architecture of the efficient channel attention module

Fig.5 Network architecture of ECA-BiseNetV2

Fig.6 Diagram of classification results convert to mask

植被指数	计算公式
RVI	$N I R R E D$
EVI	$2.5 × (N I R - R E D) N I R + 6 R E D - 7.5 B L U E + 1$
ARVI	$N I R - (2 R E D - B L U E) N I R + (2 R E D - B L U E)$
NDVI	$N I R - R E D N I R + R E D$
MSAVI	$2 (N I R + 1) - 2 (N I R + 1) 2 - 8 (N I R - R E D) 2$
OSAVI	$N I R - R E D N I R + R E D + 0.16$

Tab.4 Vegetation indices and calculation formulas

Tab.5 Imaging information of four sets

Fig.7 Curves of loss function during training and overall accuracy in the test set

Tab.6 Evaluating indicators of the best models

Fig.8 Output results of BiseNetV2 and ECA-BiseNetV2

Fig.9 ECA-BiseNetV2 identification diagram

Tab.7 Difference analysis of the values of Landsat8 and Sentinel-2 bands

Tab.8 The linear transformation models of different vegetation indices and their transformation results in test sets

Fig.10 Fitting results of NDVI in 2014 for sugarcane region 1—4

Tab.9 Error analysis of curve equations fitted values and time series data

Tab.10 Correlation analysis between actual maximum values of vegetation indices and yields

Tab.11 Training results of each model using the actual maximum values

Tab.12 Correlation analysis between the maximum fitting values of vegetation index and yields

Tab.13 Training results of each model using the fitted maximum values

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