【目的】无人机及卫星遥感技术已经被成功地应用在土壤有机碳等属性的估算研究，但对土壤质地的估算相对较少，因此开展该方面的研究具有重要的应用价值。本研究以青海湟水流域互助县卓扎滩村、湟中区拦隆口镇泥隆口村和鲁沙尔镇白土庄村3个田块为研究对象，探索了无人机和卫星遥感技术在田间尺度上估算土壤质地含量的应用潜力。【方法】基于无人机平台分别搭载2台高光谱相机对各农田土壤进行无人机成像、利用地物光谱仪收集土壤野外原位光谱、采集土壤样品，共获得2022—2024年838份土壤样品；此外，获得相近时间的GF1/2/7 (Gaofen1/2/7)、Sentinel-2 A和ZY1-02D (Ziyuan 1-02D)影像数据；实验室测试各样品的土壤粒径并采集土壤室内光谱数据。在上述数据的支持下，利用XGBoost (Extreme Gradient Boosting)方法分别对土壤质地含量与实验室、野外原位、无人机、GF、ZY1-02D和Sentinel-2光谱进行统计建模与估算，进一步对土壤质地含量进行空间分布制图。【结果】① 在XGBoost方法的模型结果中，无人机影像光谱的最优模型精度RPD为2.441，GF1/2/7、ZY1-02D和Sentinel-2卫星影像的最优模型精度RPD分别为1.815、1.601和1.561； ② 基于无人机和卫星影像的估算精度要低于地物光谱仪测得的光谱估算精度，具体为：实验室光谱>野外原位光谱>无人机影像光谱>GF1/2/7卫星影像光谱>ZY1-02D卫星影像光谱>Sentinel-2卫星影像光谱；在土壤质地含量的估算结果中，黏粒含量的精度最高， RPD最高可达2.70，粉粒次之，RPD为2.24，砂粒含量的精度最低， RPD为1.91； ③ 砂粒和黏粒含量与土壤光谱反射率呈现出负相关关系，而粉粒含量则呈正相关关系，砂粒、粉粒和黏粒的敏感波段主要集中在近红外波段范围（780~2 400 nm）； ④ 砂粒、粉粒和黏粒的含量在3年内变化较小，表现出相对稳定的状态，3个田块通过制图结果得到的土壤质地含量主要为：67%<砂粒≤83%、10.6%<粉粒≤19.1%和3.2%<黏粒≤6.6%。【结论】对田间尺度来说，无人机影像是土壤质地含量制图的最佳数据源，能够有效服务于精准农业管理，而GF1/2/7与ZY1-02D卫星影像基本满足质地制图的要求， Sentinel-2卫星影像对田块尺度制图则较为粗糙。

[Objectives] Unmanned Aerial Vehicle (UAV) and satellite remote sensing technologies have been successfully applied to estimate soil organic carbon and other attributes. However, their application to soil texture estimation remains relatively limited, highlighting the need for further research in this area. This study focuses on three farmland plots located in Zhuozhatan Village (Huzhu County), Nilongkou Village (Lalongkou Town, Huangzhong District), and Baitu Village (Lushar Town, Huangzhong District) within the Huangshui River Basin of Qinghai Province. It explores the potential of UAV and satellite remote sensing technologies for estimating soil texture content at the field scale. [Methods] Using UAV platforms equipped with two hyperspectral cameras, field-scale imaging of farmland soils was conducted. Additionally, a field spectrometer was used to collect in-situ soil spectra, and a total of 838 soil samples were collected from 2022 to 2024. Satellite imagery was also obtained for the same time periods, including GF1/2/7 (Gaofen 1/2/7), Sentinel-2A, and ZY1-02D (Ziyuan 1-02D). Laboratory analyses determined soil particle size distribution and acquired indoor soil spectral data. Based on these datasets, statistical modeling and soil texture content estimation were performed using the XGBoost (Extreme Gradient Boosting) method for laboratory, field in-situ, UAV, GF, ZY1-02D, and Sentinel-2 spectral data. Spatial distribution maps of soil texture content were then generated. [Results] ① Among the XGBoost model results, the highest model accuracy for UAV image spectra achieved an RPD (Ratio of Performance to Deviation) of 2.441, while the optimal RPD values for GF1/2/7, ZY1-02D, and Sentinel-2 satellite imagery were 1.815, 1.601, and 1.561, respectively. ② The estimation accuracy based on UAV and satellite imagery was lower than that derived from field spectrometer measurements. The accuracy ranking was as follows: laboratory spectra > field in-situ spectra > UAV image spectra > GF1/2/7 satellite image spectra > ZY1-02D satellite image spectra > Sentinel-2 satellite image spectra. Among soil texture components, clay content estimation showed the highest accuracy (RPD = 2.70), followed by silt (RPD = 2.24) and sand (RPD = 1.91). ③ Sand and clay content exhibited a negative correlation with soil spectral reflectance, whereas silt content displayed a positive correlation. The sensitive bands for sand, silt, and clay content were primarily concentrated in the near-infrared region (780~2 400 nm). ④ The content of sand, silt, and clay exhibited minor variations over three years, demonstrating relative stability. The mapping results for the three plots showed soil texture contents predominantly in the following ranges: 67% < sand ≤ 83%, 10.6% < silt ≤ 19.1%, and 3.2% < clay ≤ 6.6%. [Conclusions] At the field scale, UAV imagery was identified as the most effective data source for soil texture content mapping, providing strong support for precision agricultural management. While GF1/2/7 and ZY1-02D satellite imagery were found to be sufficient for texture mapping, Sentinel-2 satellite imagery was too coarse for field-scale mapping.