The Application of Ground-Based and Satellite Remote Sensing for Estimation of Bio-Physiological Parameters of Wheat Grown Under Different Water Regimes

Nada Mzid,Rossella Albrizio,Mohamed Houssemeddine Sellami,Giuseppe De Mastro,Vito Cantore,Mladen Todorovic

doi:10.3390/w12082095

Abstract

Remote sensing technologies have been widely studied for the estimation of crop biometric and physiological parameters. The number of sensors and data acquisition methods have been increasing, and their evaluation is becoming a necessity. The aim of this study was to assess the performance of two remote sensing data for describing the variations of biometric and physiological parameters of durum wheat grown under different water regimes (rainfed, 50% and 100% of irrigation requirements). The experimentation was carried out in Policoro (Southern Italy) for two growing seasons. The Landsat 8 and Sentinel-2 images and radiometric ground-based data were acquired regularly during the growing season with plant biometric (leaf area index and dry aboveground biomass) and physiological (stomatal conductance, net assimilation, and transpiration rate) parameters. Water deficit index was closely related to plant water status and crop physiological parameters. The enhanced vegetation index showed slightly better performance than the normalized difference vegetation index when plotted against the leaf area index with R2 = 0.73. The overall results indicated that the ground-based vegetation indices were in good agreement with the satellite-based indices. The main constraint for effective application of satellite-based indices remains the presence of clouds during the acquisition time, which is particularly relevant for winter–spring crops. Therefore, the integration of remote sensing and field data might be needed to optimize plant response under specific growing conditions and to enhance agricultural production.

Highlights

In the last decade, the development of earth observation technology, especially satellite remote sensing, has made massive remotely sensed data available for research and various applications [1,2].Agricultural remote sensing is a highly specialized field to generate images and spectral data in volume and complexity to drive decisions for agricultural development
Three water regimes were adopted, i.e., rainfed (RF), deficit (I50 ), and full (I100 ) irrigation; where I50 and I100 corresponded to 50% and 100% of irrigation requirements, respectively
The parameters of the linear regressions linking the measured gas exchange variables, with thermal (WDI) and spectral (NDVI, Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI)) Vegetation indices (VIs) are presented in Figures 2 and 3, respectively

Summary

Introduction

The development of earth observation technology, especially satellite remote sensing, has made massive remotely sensed data available for research and various applications [1,2].Agricultural remote sensing is a highly specialized field to generate images and spectral data in volume and complexity to drive decisions for agricultural development. Agricultural systems can be made resource-efficient by integrating tools, technologies, and information management systems that come under Precision Agriculture (PA) Such a concept implies observation, measurement, and response. Remote sensing is the cornerstone of modern precision agriculture [4] It aims to optimize farm inputs, to improve efficiency of water/nutrient application, and to realize site-specific crop field management strategies that account for within-field variability of soil, early detection of plant abiotic/biotic stresses, and the effects of applied treatments. This is done by focusing on the best management practice at the right rate and time and in the right place [5]

Objectives

Methods

Results

Conclusion