Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Soil salinization in the Yellow River Delta is a difficult and miscellaneous disease to restrict the development of agricultural economy, and further hinders agricultural production. To explore the retrieval of soil salt content from remote sensing images under the condition of no vegetation coverage, the typical area of the Yellow River Delta was taken as the study area to obtain the hyperspectral of surface features, the multispectral of UAVs and the soil salt content of sample points. Three representative experimental areas with flat terrain and obvious soil salinization characteristics were set up in the study area, and 90 samples were collected in total. By optimizing the sensitive spectral parameters, machine learning algorithms of partial least squares regression (PLSR) and random forest (RF) for inversion of soil salt content were used in the study area. The results showed that: (1) Hyperspectral band of 1972 nm had the highest sensitivity to soil salt content, with correlation r of -0.31. The optimized spectral parameters of shortwave infrared can improve the accuracy of estimating soil salt content. (2) RF model optimized by two different data sources had better stability than PLSR model. RF model performed well in terms of generalization ability and balance error, but it had some over-fitting problems. (3) RF model based on ground feature hyperspectral (R2=0.54, verified RMSE=3.30 g/kg) was superior to the random forest model based on UAV multispectral (R2=0.54, verified RMSE=3.35 g/kg). The combination of image texture features improved the estimation accuracy of multispectral model, but the verification accuracy was still lower than that of hyperspectral model. (4) Soil salt content based on UAV multi-spectral imageries and RF model was mapped in the study area. This study demonstrates that the level of soil salinization in the Yellow River Delta region is significantly different in geographical location. The cultivated land in the study area is mainly light and moderate salinized soil with has certain restrictions on crop cultivation. Areas with low soil salt content are suitable for planting crops in low salinity fields, and farmland with high soil salt content is suitable for planting crops with high salinity tolerance. This study constructed and compared the soil salinity inversion models of the Yellow River Delta from two different sources of data, optimized them based on the advantages of each data source, explored the inversion of soil salinity content without vegetation coverage, and can provide a reference for more accurate inversion of land salinization.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： With the rapid development of economy and people's living standards, people's demands for crops have changed from quantity to quality. The rise and rapid development of remote sensing technology provides an effective method for crop monitoring. Accurately predicting wheat quality before harvest is highly desirable to optimize management for farmers, grading harvest and categorized storage for the enterprise, future trading price, and policy planning. In this research, the main producing areas of winter wheat (Henan, Shandong, Hebei, Anhui and Jiangsu provinces) were chosed as the research areas, with collected 898 samples of winter wheat over growing seasons of 2008, 2009 and 2019. A Hierarchical Linear model (HLM) for estimating grain protein content (GPC) of winter wheat at heading-flowering stage was constructed to estimate the GPC of winter wheat in 2019 by using meteorological factors, remote sensing imagery and gluten type of winter wheat, where remote sensing data and gluten type were input variables at the first level of HLM and the meteorological data was used as the second level of HLM. To solve the problem of deviation in interannual and spatial expansion of GPC estimation model, maximum values of Enhanced Vegetation Index (EVI) from April to May calculated by moderate-resolution-imaging spectroradiometer were computed to represent the crop growth status and used in the GPC estimation model. Critical meteorological factors (temperature, precipitation, radiation) and their combinations for GPS estimation were compared and the best estimation model was used in this study. The results showed that the accuracy of GPC considering three meteorological factors performed higher accuracy (Calibrated set: R2 = 0.39, RMSE = 1.04%; Verification set: R2 = 0.43, RMSE = 0.94%) than the others GPC model with two meteorological factors or single meteorological factor. Therefore, three meteorological factors were used as input variables to build a winter wheat GPC forecast model for the regional winter wheat GPC forecast in this research. The GPC estimation model was applied to the GPC remote sensing estimation of the main winter wheat-producing areas, and the GPC prediction map of the main winter wheat producing areas in 2019 was obtained, which could obtain the distribution of winter wheat quality in the Huang-Huai-Hai region. The results of this study could provide data support for subsequent wheat planting regionalization to achieve green, highyield, high-quality and efficient grain production.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Basic Disciplines of Agriculture submitted time 2017-10-20 Cooperative journals: 《中国生态农业学报》
Abstract：叶面积指数(LAI)是评价作物长势的重要农学参数之一, 利用遥感技术准确估测作物叶面积指数(LAI)对精准农业意义重大。目前, 数码相机与无人机系统组成的高性价比遥感监测系统在农业研究中已取得一些 成果, 但利用无人机数码影像开展作物LAI 估测研究还少有尝试。为论证利用无人机数码影像估测冬小麦LAI的可行性, 本文以获取到的3 个关键生育期(孕穗期、开花期和灌浆期)冬小麦无人机数码影像为数据源, 利用数字图像转换原理构建出10 种数字图像特征参数, 并系统地分析了3 个生育期内两个冬小麦品种在4 种氮水平下的LAI 与数字图像特征参数之间的关联性。结果表明, 在LAI 随生育期发生变化的同时, 10 种数字图像特征参数中R/(R+G+B)和本文提出的基于无人机数码影像红、绿、蓝通道DN 值以及可见光大气阻抗植被指数(VARI)计算原理构建的数字图像特征参数UAV-based VARIRGB 也有规律性变化, 说明冬小麦的施氮差异不仅对LAI 有影响, 也对某些数字图像特征参数有一定影响; 在不同条件(品种、氮营养水平以及生育期)下的数字图像特征参数与LAI 的相关性分析中, R/(R+G+B)和UAV-based VARIRGB 与LAI 显著相关。进而, 研究评价了R/(R+G+B)和UAV-based VARIRGB 构建的LAI 估测模型, 最终确定UAV-based VARIRGB 为估测冬小麦LAI的最佳参数指标。结果表明UAV-based VARIRGB 指数模型估测的LAI 与实测LAI 拟合性较好(R2=0.71,RMSE=0.8, P<0.01)。本研究证明将无人机数码影像应用于冬小麦LAI 探测是可行的, 这也为高性价比无人机遥感系统的精准农业应用增添了新成果和经验。
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