Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8

Abstract

Wheat and barley are among the primary food resources of the world population; therefore, their growth and observation are essential in farms to enhance food security worldwide. On top of that, careful observation of the product is essential to find solutions for the issues faced during their production and to reduce the impacts of weather changes. With the advancement of Remote Sensing chnology te , the observation and estimation process has increased. In this study, numbers of spectral vegetation indices was used along with canopy biophysical properties ( LAI ) and biochemical properties (chlorophyll), there calculated from (Landsat 8 and Sentinel-2) satellite data. The wheat and barley samples were collected before were be ready for harvest, and a relation with the vegetarian indices was established using the Multi-Linear Regression module, in which the equations used in predicting the harvest were developed and used to create a graph for expected harvest. The result indicated that there is a strong relationship between the vegetation indices of Sentinel-2 and Landsat images and the actual grain yield with R 2 of 0.77 and 0.71, respectively. The results show that the strongest correlation is observed between the LAI data obtained from Sentinel data and cereal yield data, with an R 2 0.68, and the highest correlation for the indices of Landsat images is observed in the NDWI with R 2 0.59 and the lowest degree of error was in the root mean square error (RMSE) for the Sentinel-2 and Landsat 8 with 0.57 and 1.54. In addition, this study also showed that the least relationship for grain yield prediction was observed between the NDRI for Sentinel-2 (R 2 0.1) and SAVI for Landsat image (R 2 0.47).

Suitability of Satellite Imagery for Surveillance of Maize Ear Damage by Cotton Bollworm (Helicoverpa armigera) Larvae

“…The two satellites showed comparable accuracy in LAI prediction [106]. Sentinel-2 only slightly surpassed Landsat 8 in land-use classification [107][108][109], yield prediction [110,111], monitoring of damages to forest ecosystems, such as burnt areas [112], or pest distribution analysis, e.g., the European spruce bark beetle (Ips typographus L.) [113].…”