Colegio de Postgraduados, Campus Montecillo, Carretera México-Texcoco, Km. 36.5, Montecillo, Texcoco 56230, Estado de México, Mexico.
Sitio Experimental Metepec, Instituto Nacional de Investigaciones Forestales y Agropecuaria (INIFAP), Vial Adolfo López Mateos, Km. 4.5 Carretera Toluca Zitácuaro, Zinacantepec 51350, Estado de México, Mexico.
Sensors (Basel). 2022 Aug 16;22(16):6106. doi: 10.3390/s22166106.
The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Methodologies have been proposed for the study of crops. Most of them require field survey for ground truth data and a single crop map is generated for the whole season at the end of the crop cycle and for the next crop cycle a new field survey is necessary. Here, we present models for recognizing maize ( L.), beans ( L.), and alfalfa ( L.) before the crop cycle ends without current-year field survey for ground truth data. The models were trained with an exhaustive field survey at plot level in a previous crop cycle. The field surveys begin since days before the emergence of crops to maturity. The algorithms used for classification were support vector machine (SVM) and bagged tree (BT), and the spectral information captured in the visible, red-edge, near infrared, and shortwave infrared regions bands of Sentinel 2 images was used. The models were validated within the next crop cycle each fifteen days before the mid-season. The overall accuracies range from 71.9% (38 days after the begin of cycle) to 87.5% (81 days after the begin cycle) and a kappa coefficient ranging from 0.53 at the beginning to 0.74 at mid-season.
作物分布信息对农业环境评估很有用，但对粮食安全和农业政策管理者来说尤为重要。这些信息的获取速度，尤其是在大面积范围内，对决策者来说很重要。已经提出了用于研究作物的方法。其中大多数方法需要进行实地调查以获取地面实况数据，并且在作物周期结束时为整个季节生成单个作物图，在下一个作物周期需要进行新的实地调查。在这里，我们提出了在不进行当年实地调查获取地面实况数据的情况下，在作物周期结束之前识别玉米（L.）、豆类（L.）和紫花苜蓿（L.）的模型。这些模型是使用前一个作物周期中在小区水平上进行的详尽实地调查进行训练的。实地调查从作物出现前几天开始，一直持续到成熟。用于分类的算法是支持向量机（SVM）和袋装树（BT），并使用 Sentinel-2 图像可见、红边、近红外和短波红外区域波段捕获的光谱信息。在接下来的作物周期中，每个十五天在中期之前对模型进行验证。总体准确率范围从周期开始后 38 天的 71.9%到周期开始后 81 天的 87.5%，kappa 系数从开始时的 0.53 到中期时的 0.74。
