The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVIII-M-1-2023
21 Apr 2023
 | 21 Apr 2023


B. J. Chew, W. Wiratama, and M. H. Goh

Keywords: canopy nitrogen prediction, satellite imagery, remote sensing, Sentinel 2, vegetation indices

Abstract. The optimization of nitrogen (N) management is becoming a key challenge to enhance crop yield production while protecting the environment. Analysis of canopy N content in crop plants is used as insights for fertilization management, in which actions can be taken to optimize N fertilizer usage. Traditionally, lab chemical processing is used to measure the crop plant’s nutrient content. However, the collection of leaf samples from the field is labour intensive, and it would be costly to increase sampling frequency. Thus, this approach may not be the most optimal for large plantations. Remote sensing applications in agriculture have been widely studied. This study aims to evaluate the potential of using Sentinel 2 imagery to predict canopy N content, as an alternative wide scale method as compared to traditional methods. A cotton plantation with about 50 square km area in the state of Mato Grosso, Brazil, was used as the case study. About 180 samples across the cotton plantation were collected between March and April 2022 and the N contents of the crop plants were measured using lab chemical processes. Sentinel 2 images within 15 days of the sampling dates were retrieved from ESA’s Copernicus Open Access Hub. This study proposes a Random Forest (RF) regression algorithm for the generation of an N prediction model. About 52 vegetation indices (VIs) were extracted as the features for model training, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). RF model allows easy measurement of the relative importance of each feature with respect to the prediction to achieve a good performance. Validation is done by using mean absolute error (MAE) and mean absolute percentage error (MAPE) to evaluate the prediction accuracy against the ground truth, which resulted to be 3.418 g/kg and 9.29% respectively. Finally, this study analyses the performance of the canopy N prediction model and assesses its ability as an alternative to traditional lab chemical sampling processes.