Application of nanosatellites PlanetScope data to monitor crop growth

Svitlana Kokhan,Anatoliy Vostokov,K Jóźwiakowski,T Ciesielczuk

doi:10.1051/e3sconf/202017102014

Abstract

In this research, an approach to monitor crop growth and development is presented using time series satellite data of high spatial resolution. Monitoring of winter wheat phenology based on images of PlanetScope constellations is considered. By applying various PlanetScope data processing types and ground based GreenSeeker data, differences of NDVI values at two variants of crop fertilization are determined. In particular, the following approaches were used in the research: obtaining the Top of Atmosphere Reflectance (TOA), the Planet Surface Reflectance (SR), and receiving NDVI image in Python using a Rasterio module. It was estimated that NDVI values derived from the surface reflectance imagery were significantly correlated to the ground data of a manual active GreenSeeker optical sensor (p < 0.05). The proposed simplified technique, based on PlanetScope NDVI time series, demonstrates the possibilities to monitor temporal changes in crop growth.

Highlights

Remote sensing methods provide actual and valuable information about crop growth being non-destructive mean to obtained repeated data from a field
The results show better accuracy for PlanetScope images and fusion methods than for Sentinel-2
Correlations between NDVI values derived from spectral reflectance products, such as Top of Atmosphere Reflectance (TOA) reflectance, surface reflectance calculated in Python, surface spectral reflectance (SR), and GreenSeeker data, were estimated

Summary

Introduction

Remote sensing methods provide actual and valuable information about crop growth being non-destructive mean to obtained repeated data from a field. A.B. Baloloy [15] developed models for predicting biomass for mangroves based on the values of the vegetation indices NDVI, SAVI, GNDVI (Green Normalized Difference Vegetation Index) and SR (Simple Ratio) obtained from the Sentinel-2, RapidEye and PlanetScope satellite systems. M. Gašparović et al [16] developed a new method for Sentinel-2 and PlanetScope imagery fusion based on an assessment of the accuracy of classification of vegetation cover by NDVI values.

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Results

Conclusion