The crop harvest index (HI) is an important biological parameter to evaluate the level of crop yield and cultivation effectiveness, and it is an important determinant of further improvement of crop yield. It is of great significance for research on the application of crop variety breeding, crop growth simulation, crop management in precision agriculture and crop yield estimation, among other topics. In recent years, Remote sensing has gradually become an effective technical means for obtaining largescale crop HIs because of its advantages in speed, accuracy and coverage area. And unmanned aerial vehicle (UAV) remote sensing technology has also developed rapidly and has become a new means of agricultural remote sensing monitoring. Currently, UAV remote sensing sensors mainly include digital cameras, multispectral cameras and hyperspectral cameras. Among them, hyperspectral cameras have more bands and can obtain band information closely related to crop growth conditions, which can provide a richer source of information for dynamic crop growth monitoring and reliably collect information on dynamic crop HI change. However, there are no relevant reports on the crop HI estimation using UAV hyperspectral remote sensing.
Based on this, in the attached article “Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process”, a group of scientists from Chinese Academy of Agricultural Sciences took winter wheat as the research object and fully considered the changes in crop biomass and the grain filling process from the flowering period to the maturity period to obtain spatial information on the crop dynamic HI (D-HI). The dynamic fG (D- fG) parameter was estimated as the ratio between the aboveground biomass accumulated in different growth periods, from the flowering stage to the maturity stage, and the aboveground biomass in the corresponding periods. Based on the D- fG parameter estimation using unmanned aerial vehicle (UAV) hyperspectral remote sensing (DJI M600 Pro UAV+ Resonon Pika L imaging hyperspectrometer) data, a technical method for obtaining spatial information on the winter wheat D-HI was proposed and the accuracy of the proposed method was verified. A correlation analysis was performed between the normalized difference spectral index (NDSI), which was calculated using pairs of any two bands of the UAV hyperspectral spectrum, and the measured D- fG. Based on this correlation analysis, the center of gravity of the local maximum region of R2 was used to determine the sensitive band center to accurately estimate D- fG. On this basis, remote sensing estimation of the D- fG was realized by using the NDSI constructed by the sensitive hyperspectral band centers. Finally, based on the D- fG remote sensing parameters and the D-HI estimation model, spatial information on the D-HI of winter wheat was accurately obtained.
Study site: The study area is located in Shenzhou County (37.71°~38.16°N, 115.36°~ 115.80°E) Hengshui City, Hebei Province, China, in the main grain-producing region of the North China Plain.
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Fig. 1 Location of the study area and distribution of UAV flight plots.
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Fig.2 Overview of the methods applied in this research.
【Results】
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Table 1. Relationships between D-fG and the NDSI and their accuracy verification.
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Fig. 3 D-HI estimation results based on the sensitive band centers λ(724 nm, 784 nm) (25 May 2021).
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Fig. 4 D-HI estimation results based on the sensitive band centers λ(724 nm, 784 nm) (4 June 2021).
【Conclusion】
The authors concluded that by transforming the static fG parameter into the dynamic D- fG parameter, a method to obtain spatial information for the winter wheat D-HI based on the D- fG remote sensing parameters from UAV hyperspectral data was proposed and verified in this paper. Finally, spatial information for the winter wheat D-HI was accurately estimated. Among them, five pairs of sensitive remote sensing band centers were selected to estimate the D- fG parameter: λ(476 nm, 508 nm), λ(444 nm, 644 nm), λ(608 nm, 788 nm), λ(724 nm, 784 nm) and λ(816 nm, 908 nm). The remote sensing-based D- fG estimates were verified, with an RMSE between 0.0436 and 0.0604, an NRMSE between 10.31% and 14.27% and an MRE between 8.28% and 12.55%. At the same time, for the five pairs of sensitive hyperspectral band centers, the spatial D-HI information estimates were highly accurate, with an RMSE between 0.0429 and 0.0546, an NRMSE between 9.87% and 12.57% and an MRE between 8.33% and 10.90%. The D-HI estimation results based on hyperspectral sensitive band centers λ(724 nm, 784 nm) had the highest accuracy, with RMSE, NRMSE and MRE values of 0.0429, 9.87% and 8.33%, respectively. The estimation results for the D-fG and the D-HI in this study were highly accurate, proving that the proposed method for estimating the spatial information of the winter wheat D-HI based on UAV hyperspectral data was feasible. It has certain reference significance for the estimation of large-scale crop D-HI values based on satellite remote sensing in the future.