Application of Hyperspectral Data in Crop Identification and Classification

Hyperspectral remote sensing technology can capture subtle differences in crops across different spectral ranges by continuously acquiring ground reflectance information across hundreds of narrow bands. This high-dimensional spectral information not only reflects the physical and chemical characteristics of crops but also reveals changes in their state at different growth stages, thus offering unique advantages in crop identification and classification in agriculture.

In crop identification, different crop species exhibit significant differences in their spectral reflectance curves due to variations in leaf structure, pigment composition, and water content. Particularly in the transition region from visible to near-infrared light, namely the "red edge" band (680–750 nm), the reflectance variations of different crops are distinct, serving as a crucial basis for species differentiation. For example, the red edge position of wheat shifts significantly upwards from the jointing to heading stages, while the change in the red edge of maize is relatively gradual, as shown in the figure. These spectral characteristics enable accurate identification of crop types using hyperspectral data within a single image period.

The typical process of hyperspectral crop classification includes four key steps. The first is...Data Acquisition and Preprocessing Data can be collected using hyperspectral satellites, such as Xiguang-1 01 and Xiguang-1 05 (Tianxianpei), GF-5, PRISMA, airborne sensors, or UAV platforms, and high-quality reflectivity data can be obtained through radiometric calibration, atmospheric correction, and other steps. Secondly...Spectral feature extraction The third step is to screen out the most discriminative feature bands from continuous hyperspectral bands or construct spectral indices (such as NDVI, red edge index, chlorophyll index, etc.) to reduce dimensionality and enhance classification performance.Classification model construction Traditional methods include Support Vector Machines (SVM), Random Forests (RF), and Minimum Distance methods. In recent years, deep learning models (such as Convolutional Neural Networks (CNN) and Transformers) have also demonstrated superior performance in processing hyperspectral data. Finally...Accuracy verification and optimization The classification performance is evaluated using metrics such as confusion matrix, overall accuracy, and Kappa coefficient. Furthermore, the classification accuracy is improved by combining spectral mixture decomposition or time-series data fusion.

In practical applications, hyperspectral crop identification and classification can provide crucial support for agricultural management and policy-making. For example, in annual planting structure surveys, hyperspectral data can quickly and accurately map crop distribution, replacing traditional manual surveys and significantly reducing labor costs. In precision agriculture, distinguishing different crop types and their distribution provides spatial basis for precision fertilization and irrigation. In agricultural insurance, hyperspectral classification results can be used to verify crop area and assess damage, improving the fairness and efficiency of compensation. Furthermore, monitoring spectral changes in crops throughout the growing season using multi-period hyperspectral data can further improve the stability and accuracy of classification and enable dynamic identification of the crop growth cycle.