Recently, the Innovation Team of Smart Agriculture at the Institute of Agricultural Resources and Regional Planning (IARRP) of the Chinese Academy of Agricultural Sciences (CAAS) has constructed a Normalized Difference Yellow Vegetation Index (NDYVI) based on high-resolution GF-6 WFV satellite images. The related research titled "The normalized difference yellow vegetation index (NDYVI): A new index for crop identification by using GaoFen-6 WFV data" has been published in the academic journal "Computers and Electronics in Agriculture."

Accurate crop identification through remote sensing is crucial for agricultural activities, such as yield prediction, growth monitoring, and disaster assessment. The yellowing period of crops, characterized by the yellowness of the canopy, serves as a key point where significant changes occur in the temporal spectral reflectance, providing essential spectral information for crop identification. However, current vegetation indices have limitations in extracting the spectral feature of yellowing characteristics. Being sensitive to critical phenological features of crops, GF-6 WFV data has a large potential for capturing yellowing characteristics of crops, however, however, has not been investigated yet.

Yellow crop canopies generally have higher reflectance in spectrum between 0.59–0.63 μm and 0.69–0.73 μm, compared to green canopies during the same period. This study applied the yellow, red-edge, and near-infrared bands in GF-6 WFV images to construct the Normalized Difference Yellow Vegetation Index (NDYVI). The results demonstrate that NDYVI exhibits excellent classification performance in two classification scenarios (rapeseed with winter wheat, and maize with soybean), with overall accuracies much higher than other six vegetation indices. This study provides an effective approach for accurate classification in complex cropping patterns.

Comparison of NDYVI and six vegetation indices in distinguishing crops and land use types

Dr. Wei Yanbing from IARRP is the first author, with Researcher Wu Wenbin and Researcher Lu Miao as co-corresponding authors. This research was supported by the National Key Laboratory for Efficient Utilization of Northern Arid and Semi-Arid Cropland, National Key R&D Program, National Natural Science Foundation of China, and other projects.

Original article link:

https://doi.org/10.1016/j.compag.2024.109417](https://doi.org/10.1016/j.compag.2024.109417