Predicting soybean (Glycine max (L.) Merr) grain yield using remote sensing

[摘要] Accurate yield statistics of soybean (Glycine max (L.) Merr) grain are important for planning the management of crops prior to harvests as well as putting together logistics covering transport of grain after harvest. These statistics are essential to farmers, government and other policy-makers for guiding important decisions related to expected yields. Conventional methods currently used in South Africa to obtain such crop yields statistics are unreliable, subjective and labour intensive. As such, they pose a risk on food security as decisions concerning total agricultural production in the country rely on them. Remote sensing as part of precision agriculture technologies can overcome challenges experienced in acquiring crop statistics. Remote sensing techniques offer real-time, objective, accurate and reliable crop statistics that can be used to derive yield information. The present study sought to examine the utility of remote sensing in predicting soybean grain yields. Specifically, the study investigated the utility of hyperspectral remote sensing data for predicting soybean grain yields. To realize this aim, the study was restricted to the following objectives: (i) evaluating the potential of narrow-band indices to predict soybean grain yield (ii) determining the suitable growth stage to predict soybean grain yield using hyperspectral data and (iii) assessing the ability of Sentinel-2 Multispectral Instrument (MSI) to estimate soybean grain yield from resampled hyperspectral data. Firstly, an evaluation of the potential of narrow-band indices in predicting soybean grain yield was achieved by comparing NDVI, SR and EVI, vegetation indices, derived from hyperspectral data. The results showed that the suitable bands to predict soybean grain yield were combinations situated in the red-edge (680-750 nm), NIR and largely on the MIR (1300 to 2399 nm) of the electromagnetic spectrum. Similarly, the results showed that SR better predicted soybean grain yield (R² = 0.843) as compared to NDVI and EVI that yielded an R² = 0.841 and R2= 0.537 respectively. Secondly, as a way of determining the most suitable growth stage for predicting soybean grain yield, the study investigated the flowering, pod formation, and seed filling stages. The results showed that the most suitable growth stage to predict soybean grain yield was during the flowering stage as shown by both the NDVI (R²=0.863) and the SR (R²=0.865). Finally, the study assessed the potential of the new generation multispectral sensor Sentinel-2 MSI compared to Landsat 8 OLI and WorldView-2 in predicting soybean grain yield by resampling the hyperspectral data. The sensitivity testing of the multispectral bands revealed that sensitive spectral bands to soybean grain yield for Sentinel-2 MSI were the blue, red and re-edge bands whereas for Landsat 8 OLI and WorldView-2 included the red, blue and coastal blue bands. Sentinel-2 MSI yielded better results when predicting soybean grain yield than Landsat 8 OLI and WorldView-2. The study demonstrated a huge potential of hyperspectral remote sensing data in predicting soybean grain yields. In addition, the results showed the potential of new generation multispectral sensors to provide useful data in resource-poor countries. The findings of this study also demonstrated the utility of using remote sensing data during the flowering stage to predict soybean grain yield to assist in decision-making and overcome challenges confronting the use of conventional methods.

[发布机构] University of the Free State