Sesame crop growth monitoring using time-series green chromatic coordinate (GCC) from sentinel-2 and digital camera data

Introduction

Sesame (Sesamum indicum L.) is an important oilseed crop cultivated widely in tropical and subtropical regions due to its high oil content and nutritional value. The crop performs best under warm climatic conditions and is generally classified as a short-day plant, requiring temperatures of about 25-27 °C for optimal growth and development. Summer cultivation of sesame in regions such as Gujarat is particularly advantageous because of favourable temperature regimes, suitable photoperiod, and availability of irrigation, which together contribute to improved crop establishment and higher productivity (Ranganatha, 2010; Monpara and Vaghasia, 2016) [4,3] . Despite its adaptability, sesame yield is highly sensitive to variations in weather conditions, soil moisture, and crop management practices. Therefore, timely monitoring of crop growth is essential for improving yield and supporting decisionmaking in crop management. Conventional crop monitoring approaches, including field surveys and manual observations, are often labour-intensive, time-consuming, and limited in spatial coverage. These methods may not adequately capture the temporal variability of crop growth across large agricultural areas. Remote sensing techniques offer an efficient alternative by providing continuous, nondestructive, and spatially explicit information about crop conditions. Vegetation indices derived from satellite data, such as the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), have been widely used to assess vegetation vigor, biomass, and crop phenology (Rouse et al., 1974; Huete, 1988; Tucker, 1979) [6,1,8] . With the availability of high-resolution satellite platforms such as Sentinel-2, it is now possible to monitor crop dynamics at finer spatial and temporal scales. In recent years, near-surface remote sensing using digital repeat photography has gained attention as a complementary tool for vegetation monitoring. The Green Chromatic Coordinate (Gcc), derived from RGB images, is a reliable indicator of canopy greenness and phenological transitions. Unlike traditional vegetation indices based on near-infrared reflectance, Gcc relies on visible-band information and provides high temporal resolution observations of vegetation growth. This index captures seasonal changes in leaf emergence, canopy development, and senescence, making it particularly useful for monitoring crop growth stages. Integration of satellite-based vegetation indices with ground-based digital camera observations enhances crop monitoring by combining broad spatial coverage with detailed temporal information. Sentinel-2 imagery offers multispectral data with high spatial resolution, while digital cameras provide continuous observations at the field scale. Time-series analysis of Gcc derived from both sources allows accurate assessment of crop phenology, leaf area development, and canopy cover dynamics. Such integrated approaches have been successfully applied for monitoring crops, estimating productivity, and evaluating growth patterns under varying environmental conditions (Liu et al. 2017) [2] Therefore, the present study aims to monitor sesame crop growth using time-series Green Chromatic Coordinate (Gcc) derived from Sentinel-2 satellite imagery and ground-based digital camera data. The approach enables a detailed assessment of canopy greenness dynamics and provides insights into crop development stages. The integration of satellite and camera-based observations offers a robust framework for improving sesame crop monitoring, supporting precision agriculture, and enhancing yield prediction.

Objectives of the study 1. To analyse the temporal variability of Gcc for the sesame crop using camera and Sentinel-2 data.

2. 2. To assess the spatial distribution of Gcc for crop growth monitoring.

Materials and Methods

Study area

The experiment was conducted at the Instructional Farm, College of Agricultural Engineering and Technology, Junagadh Agricultural University, Junagadh (Fig. 1.). The latitude and longitude of the study area are 21°28'59.19" N to 21°29'02.54" N (North latitude) and 70°26'09.83" E to 70°26'10.45" E (East longitude).

Green Chromatic Coordinate (Gcc)

The Green Chromatic Coordinate (Gcc) is an RGB-based vegetation index commonly used to study plant phenology in forest ecosystems. It is derived from reflectance values recorded in the visible bands of digital images. Gcc serves as a simple and effective indicator of canopy greenness. It is calculated as the ratio of the green digital number (DN) to the total of the red, green, and blue digital numbers. In this study, Gcc was computed using the following formula as described by Sonnentag et al. (2012) [7] Gcc =

Green DN Green DN+Blue DN+Red DN

Gcc is a useful indicator of plant greenness and overall vegetation health. The index produces values ranging from 0 to 1.0, primarily reflecting the level of green vegetation.

Values close to zero generally correspond to non-vegetated surfaces such as rocks and bare soil, while values approaching one indicate dense green leaf vegetation. Therefore, higher Gcc values represent healthier and more actively growing plants.

Estimation of Bio-Physical Variables

Time-series Green Chromatic Coordinate (Gcc) maps were generated from Sentinel-2 satellite and ground-based digital camera images using the raster calculator in QGIS 3.16 to monitor sesame crop growth across different satellite overpass dates from February 22, 2020, to May 19, 2020.

Green chromatic coordinate (Gcc)

The raster calculator in QGIS 3.16 was used for preparing Gcc map for different dates of Satellite overpasses shown in Fig. 3 The minimum Gcc in the study area was 0.24 on April 30, 2020. These show the Gcc values of the vegetation is continuously increasing, then after the Gcc value decreases due to the maturity of the crop, showing low foliage cover and changing the colour of the vegetation. i.e., during the harvesting stage of the crop. The Green chromatic coordinate (Gcc) for different locations of the Sesame crop and temporal variability of Gcc for the period from March 01, 2020 to May 10, 2020, at an interval of 10 days is given in Table 2. The trend of Gcc for the Sesame crop is shown in Fig. 6.

Conclusion

The results showed that the Green Chromatic Coordinate (Gcc), derived from both digital camera images and Sentinel-2 data, is effective for monitoring sesame crop growth. The time-series analysis of Gcc clearly captured different crop phenological stages, with greenness increasing during the vegetative phase and gradually declining as the crop reached maturity and harvesting. Camera-based Gcc was more sensitive to changes in canopy greenness, whereas satellite-derived Gcc effectively represented the spatial and temporal variability of the crop. Overall, Gcc proved to be a simple, non-destructive, and reliable index for spatio-temporal monitoring of sesame crops and has strong potential for precision agriculture applications

References

1. Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. 1988;25:295-309.
2. Liu Y, Hill MJ, Zhang X, Wang Z, Richardson AD, Hufkens K, et al. Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales. Agricultural and Forest Meteorology. 2017;237:311-325.
3. Monpara BA, Vaghasia DR. Optimizing sowing time and row spacing for summer sesame growing in semiarid environments of India. International Journal of Current Research and Academic Review. 2016;4(1):122-131.
4. Ranganatha ARG. Improved technology for maximizing production of sesame. Revised ed. Jabalpur: Project Coordinator, AICRP on Sesame and Niger, ICAR, JNKVV Campus; 2010. p. 1-17.
5. Richardson AD, Hufkens K, Milliman T, Aubrecht DM, Chen M, Gray JM, et al. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data. 2018;5(1):180028.
6. Rouse JW, Hass RH, Schell JA, Deering DW. Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Greenbelt. NASA SP-351; 1974. p. 3010-3017.
7. Sonnentag O, Hufkens K, Teshera-Sterne C, Young AM, Friedl M, Braswell BH, et al. Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology. 2012;152:159-177.
8. Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 1979;8:127-150.