基于时间序列高光谱遥感影像的田块尺度作物产量预测

在精准农业领域，田块尺度土壤理化性质、作物长势、产量等存在极显著的空间异质性。高光谱遥感侧重于光谱维度信息的提取，未充分利用空间与时相信息，限制了植被长势、生物量与产量的监测精度。传统格网采样与地统计空间插值方法，耗时费力、成本高，难以推广;而遥感技术可以获取农作物生理参数的时空异质性信息，可以用于田块尺度的精准管理分区(SSMZ)。以田块尺度棉花地为研究对象，获取时间序列航空高光谱遥感影像，分析不同长势棉花的反射光谱特征，构建光谱指数，综合光谱、时相、空间维度信息，利用面向对象方法进行精准管理分区，建立产量遥感预测模型。结果表明：综合多维信息的面向对象分割方法优于基于象元的方法，可以部分消除遥感与产量数据噪声，提高棉花估产精度;不同植被指数与棉花产量的相关系数排序为一阶微分、NDVI、OSAVI、二阶微分;对于同一尺度、单一时相，一阶微分产量预测模型精度较高，多时相多光谱植被指数也可以得到较高精度;对于同一输入量、不同尺度，较少SSMZ个数的棉花产量预测模型精度更高、稳定性更好，这是由于影像与产量数据的空间定位存在一定的误差造成。研究成果将丰富作物长势、估产方法，提高遥感监测精度，加速无人机遥感在相关领域的应用。

Study on the Prediction of Cotton Yield within Field Scale with Time Series Hyperspectral Imagery

Pixel-based processing method mainly extracts spectral information from hyperspectral remote sensing images,but site specific management zone (SSMZ)delineation and crop yield estimation with images need to take spatiotemporal heterogeneity into account.As the spatial resolution of remote sensing data increases,the so-called “salt-and-pepper”problem of pixel-based classification becomes more serious.The spatiotemporal heterogeneity of soil properties and crop biophysical parameters are mainly delineated with grid sampling and geostatistics interpolation,but the widely used method has some problems:time consu-ming and high cost.Satellite imageries are introduced to delineate SSMZ,but there are also problems needed to be resolved:(1) single date imagery is used to map SSMZ which is difficult to determine the optimal date for SSMZ delineation;(2 )only few SSMZs were mapped,which limited application of site specific fertilizing and management;(3)pixel-based method for SSMZ de-lineation didn't concern the spatial relationship between pixels and site specific management does not implement at pixel level,but at SSMZ level.To improve the accuracy of crop yield estimation,a time-series of hyperspectral airborne images with high spatial resolution (1 m)of a cotton field,which is located in San Joaquin Valley,California US,were acquired and classified by using object-oriented segmentation,then yield predicting models were built,and the accuracy and stability of yield models were valida-ted with determining coefficients R 2 and the root mean square error (RMSE).Results are as follows:(1)object-oriented SSMZ delineating method combines spectral,spatial and temporal information,reduces noises in images and yield data,improves the accuracy of yield prediction;(2)for same SSMZ number,first derivative predicting model is more accurate;(3)for same spec-tral input,models with fewer SSMZs show higher accuracy,which is due to spatial errors of airborne images and yield data.The results will improve monitoring methods for crop growth and yield while accelerate the application of UAV remote sensing in pre-cision agriculture.