In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification
In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification
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DOI：
10.3390/agriculture9010017
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发表时间：
2019-01-01
影响因子：
3.6
通讯作者：
Mohiuddin, Hossain
中科院分区：
文献类型：
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作者：
Rahman, Md. Shahinoor;Di, Liping;Mohiuddin, Hossain
田间作物类型信息对于多种类型的研究和应用至关重要。美国农业部 (USDA) 通过农田数据层 (CDL) 提供有关美国农田作物类型的信息。然而，CDL 仅在作物生长季节结束后的年底提供。因此，CDL无法支持作物损失估算、产量估算和粮食定价等当季研究和决策。美国农业部主要依靠实地调查和农民报告来获取真实数据来训练图像分类模型，这也是 CDL 延迟发布的主要原因之一。这项研究旨在使用可信像素作为地面事实来训练分类模型。可信像素是遵循特定作物轮作模式的像素。这些可信像素用于训练图像分类模型，对季节性陆地卫星图像进行分类，以识别主要作物类型。研究和测试了六种不同的分类算法，为本研究选择最佳算法。随机森林算法在所选算法中脱颖而出。这项研究对爱荷华州五月至八月中旬的陆地卫星场景进行了分类。 2017年5月、6月和7月分类结果与CDL的总体一致性分别为84%、94%和96%。分类准确性是通过从现场收集的 683 个地面实况数据点进行评估的。 5月、6月和7月单日期多波段图像分类的总体准确率分别为84%、89%和92%。结果还表明，与单日期图像分类相比，通过多日期图像分类可以实现更高的准确度 (94-95%)。
Crop type information at the field level is vital for many types of research and applications. The United States Department of Agriculture (USDA) provides information on crop types for US cropland as a Cropland Data Layer (CDL). However, CDL is only available at the end of the year after the crop growing season. Therefore, CDL is unable to support in-season research and decision-making regarding crop loss estimation, yield estimation, and grain pricing. The USDA mostly relies on field survey and farmers' reports for the ground truth to train image classification models, which is one of the major reasons for the delayed release of CDL. This research aims to use trusted pixels as ground truth to train classification models. Trusted pixels are pixels which follow a specific crop rotation pattern. These trusted pixels are used to train image classification models for the classification of in-season Landsat images to identify major crop types. Six different classification algorithms are investigated and tested to select the best algorithm for this study. The Random Forest algorithm stands out among selected algorithms. This study classified Landsat scenes between May and mid-August for Iowa. The overall agreements of classification results with CDL in 2017 are 84%, 94%, and 96% for May, June, and July, respectively. The classification accuracies have been assessed through 683 ground truth data points collected from the fields. The overall accuracies of single date multi-band image classification are 84%, 89% and 92% for May, June, and July, respectively. The result also shows higher accuracy (94-95%) can be achieved through multi-date image classification compared to single date image classification.