遥感技术是当前农业生产领域的重要监测手段。河北省是中国13个小麦主产省份之一，协同多源遥感数据开展该区域冬小麦识别和长势监测研究，对保障国家粮食安全具有重要价值。本文以河北省泊头市为研究区，基于Google Earth Engine平台获取Sentinel-1/2主被动遥感影像，提取并融合光谱特征和极化特征，采用基尼指数重要性评价方法和随机森林分类器，筛选最佳特征组合，实现在冬小麦生长发育阶段早期的遥感识别。获取田块尺度的无人机多光谱影像，提取植被指数和纹理指数，并从中筛选出与冬小麦拔节期、灌浆期的株高、叶面积指数（LAI）地面实测样本数据相关性较强的特征指数，进行特征组合作为长势估测模型的输入量，再利用多种机器学习算法或深度学习算法构建出冬小麦关键生育期的长势估测模型，并优选出最佳方法。最后将基于无人机多光谱影像获取的田块尺度长势估测数据（0.1m栅格）升尺度应用至区域尺度的卫星影像（10m栅格），得到冬小麦在区域尺度下的长势空间分布反演结果图。具体结果如下：

（1）基于单时相遥感影像可以在生长发育早期阶段（12月）实现季节内的冬小麦识别。融合光谱特征与极化特征参与分类，有效提高了冬小麦的识别精度。将从Sentinel-1/2影像中提取的14种指数（MSAVI、PLUS、B8、NDVI、EVI、SAVI、MULTIPY、GNDVI、B2、VV、VH、B3、B4、S2REP）进行特征组合后，识别冬小麦的精度最佳，分类结果的总体精度和F1分数分别为97.09%、94.33%。

（2）多特征结合估算冬小麦关键生育期各长势指标的能力较强，小麦株高可以作为基于传统指数进行LAI反演建模的一个重要补充。利用4种机器学习算法（SVM、BPNN、RF、XGBoost）、2种深度学习算法（CNN、LSTM）构建的无人机长势指标反演模型可基本满足田块尺度上的冬小麦拔节期和灌浆期的株高、LAI估算，其中基于RF算法的回归模型估算冬小麦灌浆期LAI精度最高，R²为0.8628；基于XGBoost估算拔节期、灌浆期株高和拔节期LAI模型效果最好，R²分别为0.7683、0.7170、0.8689。

（3）无人机遥感平台可以作为田块调查和卫星数据之间的桥梁，能够为基于卫星多光谱影像的模型构建和作物长势监测提供足够的地面田块样本。其中，基于“机地反演-星机协同”的反演方法优于“机地建模-卫星反演”的反演方法，估算结果精度更高，特别是拔节期LAI预测值与实测值相关系数达0.6622。

     Remote sensing technology has become an important means in the agricultural production field. Hebei Province is one of the 13 major wheat producing provinces in China, it is of great significance for ensuring national food security to carry out research on winter wheat planting area identification and growth indicator monitoring at the regional scale using multi-source remote sensing data. This paper took Botou City, Hebei Province as the research area. Sentinel-1/2 active and passive remote sensing images were obtained through the Google Earth Engine platform, spectral and polarization features were extracted and integrated. The Gini index importance evaluation method and random forest classifier were employed to select the best feature combination, and achieve remote sensing identification in the early stage of winter wheat growth and development. Subsequently, UAV multispectral imagery at the field scale were obtained. Vegetation indices and texture indices were extracted, and the indicies strongly correlated with ground-based measurements of plant height and leaf area index (LAI) at the jointing and filling stages of winter wheat were screened. Features were combined as input variables for model construction. Various machine learning and deep learning algorithms were applied to estimate growth indicators of winter wheat during the critical growth stages, and the best methods were selected. Finally, the field scale growth estimation data (0.1m grid) obtained from UAV multispectral images were upscaled and applied to regional scale satellite images (10m grid) to derive the spatial distribution inversion results of various growth of winter wheat at the regional scale. The results showed that:

(1) The capacity to accomplish the early identification of winter wheat during the relatively early stages of growth and development (December) could be achieved based on single-phase remote sensing imagery. The combination of spectral and polarization features in classification effectively improved the identification accuracy of winter wheat. The winter wheat recognition accuracy was the best when 14 indices (MSAVI、PLUS、B8、NDVI、EVI、SAVI、MULTIPY、GNDVI、B2、VV、VH、B3、B4、S2REP) extracted from Sentinel-1/2 single-temporal images were combined with features. The overall accuracy and F1_Score of the classification results were the highest, with 97.09% and 94.33%, respectively.

(2) The ability to estimate growth indicators during critical growth stages of winter wheat was strong when multiple features were combined, with plant height serving as an important supplement to LAI inversion modeling based on traditional indices. The UAV inversion models for various growth indicators constructed using four machine learning algorithms, namely, support vector machine (SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), can basically meet the estimation of plant height and LAI during the jointing and filling stages of winter wheat at the field scale. Among them, the regression model based on the RF algorithm has the highest accuracy in estimating LAI during filling stage of winter wheat, with R² values of 0.8628. The XGBoost based estimation of plant height during the jointing stage and filling stage, and LAI during jointing stage has the best performance, with R² values of 0.7636, 0.7170 and 0.8689, respectively.

(3) The UAV remote sensing platform could serve as a bridge between field surveys and satellite data, providing sufficient ground field samples for model construction and crop growth monitoring based on satellite multispectral imagery. In the research on upscaling monitoring of winter wheat growth indicators, UAV-ground inversion and satellite-UAV collaboration was superior to the inversion method based on UAV-ground modeling and satellite inversion, and the estimation accuracy was higher, especially the correlation coefficient between the predicted LAI value during the jointing period and the measured value was 0.6622.

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