As one of the most important staple foods globally, rice sustains nearly half of the world's population. Accurate and timely paddy rice mapping is, thus, essential for rice-related policy-making to ensure food security in the context of anthropogenic, environmental and climate changes. However, paddy rice mapping remains a challenging task since it usually has similar spectral characteristics to other land covers. In this research, for the first time, an entirely new approach, called RiceTColour, was proposed for mapping rice fields within the Commission Internationale de l'Eclairage (CIE) colour space based on their unique spectra during the rice transplanting period as observed in remotely sensed imagery. We demonstrate that transplanted rice fields, representing a mixture of soil, water and rice seedlings, consistently exhibit relatively low spectral values in both SWIR and NIR bands across various geographical locations, leading to their unique dark green colours in the false-colour image composed of SWIR, NIR and Red bands. Based upon this, we transformed these three spectral bands into the CIE colour space where paddy rice was found to be readily and completely separated from the other land covers. Straightforward, but specific classification criteria were established within the CIE colour space to differentiate paddy rice from the other land covers. The proposed RiceTColour, thus, represents a new approach for paddy rice identification, that is mapping paddy rice using the CIE colour space based upon the previous underexplored remotely sensed spectra of paddy fields during the transplanting season. The effectiveness of the proposed method was investigated over five rice-planting regions distributed across different geographical regions, characterised by different climates, rice cropping intensities, irrigation schemes and cultural practices. Specifically, the mapping criteria established in a training site (S1) were directly generalised to the other four sites (S2 to S5) for paddy rice mapping. Experimental results demonstrated that the RiceTColour method consistently achieved the most accurate and balanced classifications across all five sites compared with four benchmark comparators: a SAR-based method, an index-based method and two supervised classifier-based methods. In particular, the RiceTColour method performed relatively stable, producing an overall accuracy exceeding 95% in the training site (S1) as well as the four generalised sites (S2 to S5), which is an encouraging result. Such efficient yet stable rice mapping results across various rice-planting regions suggest a very strong generalisation capability of the proposed RiceTColour method. In consideration of the relatively large planting area of paddy rice fields globally, the proposed parameter-free, efficient, and generalisable RiceTColour method, thus, holds great potential for widespread application in various rice-planting areas worldwide.

中文翻译：

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利用 CIE 色彩空间，根据插秧期间独特的光谱绘制稻田地图的高效且通用的方法


作为全球最重要的主食之一，大米养活着世界近一半的人口。因此，准确、及时的水稻测绘对于制定水稻相关政策至关重要，以确保人为、环境和气候变化背景下的粮食安全。然而，水稻测绘仍然是一项具有挑战性的任务，因为它通常具有与其他土地覆盖相似的光谱特征。在这项研究中，首次提出了一种名为 RiceTColour 的全新方法，用于在国际照明委员会 (CIE) 颜色空间内绘制稻田图，该方法基于远程观察到的水稻插秧期间独特的光谱。感知到的图像。我们证明，代表土壤、水和稻苗混合物的移栽稻田在不同地理位置的短波红外和近红外波段始终表现出相对较低的光谱值，导致其在由SWIR、NIR 和红波段。基于此，我们将这三个光谱带转换到 CIE 色彩空间中，发现水稻很容易且完全与其他土地覆盖物分离。在 CIE 颜色空间内建立了简单但具体的分类标准，以将水稻与其他土地覆盖区分开来。因此，所提出的 RiceTColour 代表了一种水稻识别的新方法，即根据之前未充分探索的移栽季节稻田遥感光谱，使用 CIE 颜色空间绘制水稻图。 该方法的有效性在分布在不同地理区域的五个水稻种植区进行了调查，这些区域具有不同的气候、水稻种植强度、灌溉方案和文化实践。具体来说，在训练站点（S1）中建立的绘图标准直接推广到其他四个站点（S2至S5）进行水稻绘图。实验结果表明，与四种基准比较器（基于 SAR 的方法、基于索引的方法和两种基于监督分类器的方法）相比，RiceTColour 方法在所有五个站点上始终实现了最准确和平衡的分类。特别是，RiceTColour 方法表现相对稳定，在训练站点（S1）以及四个广义站点（S2 至 S5）中总体准确率超过 95%，这是一个令人鼓舞的结果。这种有效而稳定的跨不同水稻种植区的水稻绘图结果表明所提出的 RiceTColour 方法具有非常强的泛化能力。考虑到全球稻田种植面积相对较大，所提出的无参数、高效且具有普适性的RiceTColour方法在全球各稻区具有广泛应用的潜力。