多源中高分辨率影像协同下时间合成窗口对农作物识别的影响

doi:10.3864/j.issn.0578-1752.2024.02.003

Abstract

Abstract:

【Background】 The decametric-resolution (≤30 m) image is an important data source to identify crop types in South China dominated by fragmented croplands and complex cropping patterns. Due to the relative long revisit frequency of decametric-resolution sensors and persistent rainy/cloud weather in South China, it is critical to integrate multi-source decametric-resolution images using the temporal composition method for the generation of spatiotemporal continuous crop type map. Due to the different temporal resolutions of different satellites, and the significant differences in phenological quaternal rhythms of various crop types, selecting the optimal temporal composition window for integrating multi-source images is vital to map crop type distribution accurately.【Objective】 This study aims to explore the impact of image temporal composition windows on crop type identification, and to provide significant references for large-scale crop type mapping in regions with complex terrain.【Method】 In this study, Landat-8 and Sentinel-2 data were integrated to extract the crop type distribution in the Jianghan Plain, Hubei Province, characterized by the various crop types and cloudy and rainy weather. Then, seven scenarios (15, 20, 25, 30, 40, 50, and 60 d) were established to analyze the effect of different temporal composition windows on crop type identification. Specifically, three aspects, including image coverage rate, spectral-temporal feature curves for different crops and classification accuracies, were combined to understand the performances of different scenarios comprehensively.【Result】 The crop type mapping using 20-day composition window performed the best, with the overall accuracy (OA) of 93.13%. In contrast, the scenarios that used narrower temporal composition window derived lower accuracy of crop type identification (e.g., OA=90.91% for the 15-day composition window), which can be primarily attributed to the low coverage rate of good observations in the study area. Meanwhile, since time series images composited in the wide window blurred the key phenological information for different crops, the classification accuracy of crop type mapping scenarios using wide temporal interval was also lower (e.g., OA=86.06% for the 60-day composition window). Additionally, the effect of temporal interval on different crops classification was ranked as following: other crops>rapeseed>rice>wheat>rice-crayfish. In detail, the reason why the classification performance of other crops was the most sensitive to the temporal composition window can be due to the high intra-class phenological variance of this type. Flowering period is the key phenology window to identify rapeseed, therefore, the classification accuracy of rapeseed decreased while the temporal composition window exceeds 30-day, and rapeseed was easily confused with wheat. Furthermore, because the key phenology window to distinguish rice-crayfish from single-cropping rice (i.e., the flooding stage of rice-crayfish fields) lasted a long period (e.g., from October 2020 to June 2021), the classification accuracy of rice-crayfish was less sensitive to the temporal composition window.【Conclusion】 In general, the 20-day impact of the temporal composition window can take into account the high-quality image coverage and capture of key phenological characteristics of crop identification, but the optimal temporal composition window of different crops identification is affected by the key phenological period of crops. This study provides theoretical reference and method support for selecting the optimal temporal composition window to generate multi-source image time series, which is promising to improve the efficiency and accuracy of large-scale crop type mapping.

Key words: crop type mapping, temporal composition window, random forest, Sentinel-2, Landsat-8

TONG WanTing, WEI HaoDong, YANG JingYa, JIN WenJie, SONG Qian, HU Qiong, YIN GaoFei, XU BaoDong. Exploring the Impacts of Temporal Composition Window for Integrating Multi-Source Decametric-Resolution Images on Crop Type Identification[J].Scientia Agricultura Sinica, 2024, 57(2): 250-263.

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References 56

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植被指数 Vegetation index	公式 Equation	参考文献 Reference
归一化植被指数 NDVI	$N D V I=\frac{\rho_{N I R}-\rho_{R E D}}{\rho_{N I R}+\rho_{R E D}}$	[40]
陆表水分指数 LSWI	$L S W I=\frac{\rho_{\text {NIR }}-\rho_{\text {SWIR } 1}}{\rho_{\text {NIR }}+\rho_{\text {SWIR } 1}}$	[47]
增强型植被指数 EVI	$E V I=\frac{2.5 \times\left(\rho_{\text {SWIR1 }}-\rho_{\text {RED }}\right)}{\rho_{\text {NIR }}+6 \times \rho_{\text {RED }}-7.5 \times \rho_{\text {BLUE }}+1}$	[45]
绿度指数 VIgreen	$\text { VIgreen }=\frac{\rho_{\text {GREEN }}-\rho_{\text {RED }}}{\rho_{\text {GREEN }}+\rho_{\text {RED }}}$	[46]

Exploring the Impacts of Temporal Composition Window for Integrating Multi-Source Decametric-Resolution Images on Crop Type Identification

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