Monitoring of Rice Growth Phases Using Multi-Temporal Sentinel-2 Satellite Image

Abstract

Rice is the primary source of nutrition food of more than half of the world’s population, and it is hugely important in the global economic growth, food security, water use, and climate change. The need for satellite systems to monitor rice crops and assist in rice crop management is gaining in popularity. The European Space Agency’s (ESA) launched Sentinel-2 A + B twin platform’s which enhanced the temporal, spatial, and spectral resolution, opening the way for their widely use in crop monitoring. Aside from the technical features of the Sentinel-2 A and B constellation, the easily accessible type of information they generate as well as the appropriate support software have been significant improvements for rice crop monitoring. In this study, the spectral reflectance has been analysed to find how far their potential in determining rice growth phases. The highest spectrum in reflectance was observed in the near infrared (NIR) region (842 nm). Because of the structure of mesophyll cells tissues and the inner backscatter of air spaces, moisture content, and air–water abstraction layers within the leaves, the reflectance in the NIR region seems to be much larger than in the visible band. The multi-temporal vegetation index namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) have derived from ten Sentinel-2 images cover the entire rice season. These indices have been tested to determine the rice growth phases over the rice season. The spatial distribution of each tested indices is displayed in the map output. The maps are then analysed and compared to determine the potential of each index in determining rice growth phases. It was discovered in this study that there was a quadratic correlation between all of the tested indices and rice age. The Normalized Difference Vegetation Index (NDVI) is the most accurate vegetation index for estimating rice growth phases, followed by SAVI and NDMI.

Application of RGB UAV images to identify spectral patterns and estimate rice production

“…This is because the difference in spectral values between plant ages in the red band was greater than in the blue or green bands. Although the use of RGB wavelengths such as in Sentinel-2A is not as good as near-infrared (NIR) in its ability to distinguish the age of rice plants (Kawamura et al, 2018;Munibah et al, 2019;Hisham et al, 2022). Our result shows that RGB UAV was reliable in distinguishing the spectral value pattern in a single rice cycle.…”

Improving the Estimation of Rice Crop Damage from Flooding Events Using Open-Source Satellite Data and UAV Image Data

“…Although the S2 NDVI is the most accurate vegetation index for estimating rice growth stages [24], it relies on optic sensors, making it highly susceptible to measurement bias and errors caused by cloud cover [36]. As such, RGB images from S2 were checked for all six flooding events, including dates before and after each event, to verify the presence of clouds.…”

“…To estimate crop damage at a specific growth stage, we conducted an NDVI sensitivity analysis on the S2 NDVI image obtained prior to the typhoon event. The analysis involved incrementing the NDVI values by 0.1 and comparing them to the recorded data values provided in the damage report, following the guidance of previous studies by Gonzalez-Betancourt et al and Hasniati et al [24,46], which emphasized the relation of the NDVI to rice growth stages. We multiplied the resulting image by the net flood, as determined in Section 2.4.2., to obtain an image showing the estimated flooded rice damaged during that specific growth stage.…”

“…Thus, in this study, the NDVI was used as the index to monitor crop growth, as the reported damages were specific to particular growth stages of the crops when they were damaged. Moreover, the S2 NDVI is the most accurate vegetation index for estimating rice growth stages [24].…”