针对近红外波段水稻识别能力的问题,选择银川市所属的5个县区为研究区域,采用2016年5月18日、6月16日、7月30日、9月13日4个时相GF-1/WFV影像的近红外波段(0.76～2.526μm)数据,基于决策树分类方法,获取了4个单时相、3个多时相条件下的水稻识别结果,并与全波段数据分类结果进行了比较.单时相5、6、7和9月份近红外波段水稻识别精度分别为83.63％、57.40％、75.82％和62.61％,除5月份精度高于全波段5.75个百分点外,其他时相都低于全波段识别精度,6月份相差最高为30.23个百分点.多时相5/6、5/7、5/6/7/9月份组合,近红外水稻识别精度分别为83.76％、93.93％和94.03％,分别比全波段低5.47,高8.58和0.73个百分点.结果表明,水稻生长早期的5月份、中期的7月份,近红外波段可以作为单时相遥感识别数据源,包括生长早期和中期2个时相在内的多时相近红外波段组合都可以作为遥感识别的数据源,研究结果可以作为GF-1数据水稻遥感识别的依据.%Near-infrared wave bands have double sensitivities on water bodies and crop growth. Using near-infrared wave band on rice identification has notable advantage, and it is a key wave band in crop identification. The study chose 5 counties of Yinchuan City, Ningxia Hui Autonomous Region as the study area and took the near-infrared (770-890 nm) data of GF-1/WFV (wide field view) images on May 18, June 16, July 30, and September 13, 2016 as its data sources. By using decision-tree classification method, the study achieved the rice identification of 7 temporal combinations, including 4 single temporal data in May, June, July, and September, as well as 3 multi-temporal data of May/June, May/July, and May/June/July/September, and made a comparison with supervision classification results of full wave bands (0.45-0.52, 0.52-0.59, 0.63-0.69, 0.77-0.89μm) of corresponding GF-1/WFV data. The rice identification accuracies of near-infrared wave bands of single temporal data in May, June, July, and September were 83.63％, 57.40％, 75.82％ and 62.61％ respectively. Except that the accuracy of May data was 5.75％ higher than the full wave bands, the identification accuracies of other temporal phases were lower than that of full wave bands. The highest accuracy deviation was in June, 30.23％, and the lowest accuracy deviation was in July, 1.58％. The deviations in May and September were 5.75％ and 25.47％ respectively. The rice identification accuracies under 3 multi-temporal near-infrared combinations of May/June, May/July, May/June/July/September were 83.76％, 93.93％, and 94.03％ respectively. The accuracies of near-infrared combinations of May/July, and May/June/July/September were 8.58％ and 0.73％ higher than that of full wave band data results respectively, but the accuracy of May/June was 5.47％ lower. Regardless of near-infrared data or full wave band data, the minimum value, average value and maximum value of single temporal data identification accuracies were 57.40％, 76.31％ and 88.10％ respectively, with the Kappa coefficients of 0.22, 0.44 and 0.64 respectively; the minimum value, average value and maximum value of multi-temporal data identification accuracies were 83.76％, 89.98％ and 94.03％ respectively, with the Kappa coefficients of 0.52, 0.68 and 0.77 respectively; all identification accuracies of single temporal data were lower than the identification accuracies of the multi-temporal data. If using single temporal data as the data source of rice remote sensing identification, the identification accuracy can reach 75.82％ based on the near-infrared WFV data of rice in middle growth period of July, which is consistent with the full wave band data result of this time phase; to reach the accuracy above 88.10％, it is necessary to use full wave band WFV data in September. If taking multi-temporal data of WFV data of temporal combinations of 2 rice growth periods i.e. early period of May and middle period of July as the data source of rice remote sensing identification, the identification accuracy can reach 93.93％, which is close to the highest identification accuracy of multi-temporal data. The study result shows that, for the near-infrared wave band, and in the applications with requirement on the rice identification accuracy of about 75％-85％, the 2 single temporal infrared WFV data in early growth period of May and middle growth period of July can be taken as the data sources of remote sensing identification. To reach the identification accuracy above 86％, it is necessary to choose the combination of near-infrared WFV data in early growth period of May and middle growth period of July as the remote sensing data sources, so as to achieve rice area spatial distribution results with relatively high identification accuracy. After further correction with visual observation, it can be taken as the crop area remote sensing supervision results.
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