Management Zone Delineation for Site-Specific Fertilization in Rice Crop Using Multi-Temporal RapidEye Imagery

Christos Karydas,George Iatrou,Spiros Mourelatos,Miltiadis Iatrou

doi:10.3390/rs12162604

Abstract

The objective of this research is to assess the potential of satellite imagery in detecting soil heterogeneity, with a focus on site-specific fertilization in rice. The basic hypothesis is that spectral variation would express soil fertility variations analogously. A 100-ha rice crop, located in the Plain of Thessaloniki, Greece, was selected as the study area for the 2016 cropping season. Three RapidEye images were acquired during critical growth stages of rice cultivation from the previous year (2015). Management zones were delineated with image segmentation of a 15-band multi-temporal composite of the RapidEye images (three dates × five bands), using the Fractal Net Evolution Approach (FNEA) algorithm. Then, an equal number of soil samples were collected from the centroid of each management zone before seedbed preparation. The between-zone variation of the soil properties was found to be 33.7% on average, whereas the within-zone variation 18.2%. The basic hypothesis was confirmed, and moreover, it was proved that zonal applications reduced within-zone soil variation by 18.6% compared to conventional uniform applications. Finally, between-zone soil variation was significant enough to dictate differentiated fertilization recommendations per management zone by 24.5% for the usual inputs.

Highlights

“Precision Agriculture is a management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production” [1]
The objective of this research is to assess the potential of satellite imagery in detecting soil heterogeneity, with a focus on site-specific fertilization in rice
The basic hypothesis is that spectral variation would express soil fertility variations analogously

Summary

Introduction

“Precision Agriculture (or precision farming) is a management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production” [1]. Remote sensing is known to assist a lot in zone delineation based on detecting variability of vegetation, or soil properties, or both [3]. In Mediterranean countries, remote sensing has dominated as the main source of information for precision agriculture applications over yield mapping, as optical satellite data can play a very important role, due to the good weather conditions [4,5]. Using historic satellite or airborne imagery, Zhang et al (2010) [6] developed a web-based mapping application to automatically delineate the optimal number of management zones in the United States of America. Multi-temporal imaging spectroscopy data from the Airborne Prism Experiment (APEX) were utilized by Diek et al (2016) [14] successfully, to increase total mapping area of bare soils in heterogeneous agricultural landscapes. The dynamic mixture of plants, soil, and water results in a very particular spectral response over time [17], and multi-temporal approach for precision agriculture purposes is a necessity

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