Abstract
Monitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spectral) as well as in situ spectra measurements can help decision takers to have deep insight on crop stress in a certain region. The research attempts to examine remote sensing dataset for forecasting wheat crop (Sakha 61) characteristics including the leaf area index (LAI), plant height (plant-h), above ground biomass (AGB) and Soil Plant Analysis Development (SPAD) value of wheat across non-stress, drought and salinity-induced stress in the Nile Delta region. In this context, the ability of in situ spectroradiometry measurements and QuickBird high resolution images was evaluated in our research. The efficiency of Random Forest (RF) and Artificial Neural Network (ANN), mathematical models was assessed to estimate the four measured wheat characteristics based on vegetation spectral reflectance indices (V-SRIs) extracted from both approaches and their interactions. Field surveys were carried out to collect in situ spectroradiometry measurements concomitant with the acquisition of QuickBird imagery. The results demonstrated that several V-SRIs extracted from in situ spectroradiometry data and the QuickBird image correlated with the LAI, plant-h, AGB, and SPAD value of wheat crop across the study site. The determination coefficient (R2) values of the association between V-SRIs of in situ spectroradiometry data and various determined wheat characteristics varied from 0.26 to 0.85. The ANN-GSIs-3 was found to be the optimum predictive model, demonstrating a greater relationship between the advanced features and LAI. The three features of V-SRIs comprised in this model were strongly significant for the prediction of LAI. The attained results indicated high R2 values of 0.94 and 0.86 for the training and validation phases. The ANN-GSIs-3 model constructed for the determination of chlorophyll in the plant which had higher performance expectations (R2 = 0.96 and 0.92 for training and validation datasets, respectively). In conclusion, the results of our study revealed that high resolution remote sensing images such as QuickBird or similar imagery, and in situ spectroradiometry measurements have the feasibility of providing necessary crop monitoring data across non-stressed and stressed (drought and salinity) conditions when integrating V-SRIs with ANN and RF algorithms.
Highlights
The scarcity of freshwater resources is considered an essential consideration in both arid and semi-arid environments and the accessibility of water resources of low quality has become a more important consideration in supplementing supply [1,2,3,4]
The results further showed the effectiveness of QuickBird satellite images in detecting wheat characteristics
We evaluated the performance of vegetation spectral reflectance indices (V-SRIs) obtained from both in situ spectroradiometry measurements and high resolution QuickBird images to quantify leaf area index (LAI), plant-h, above ground biomass (AGB), and Soil Plant Analysis Development (SPAD) values of wheat across healthy, water and salinity-induced stress in the Nile Delta region
Summary
The scarcity of freshwater resources is considered an essential consideration in both arid and semi-arid environments and the accessibility of water resources of low quality (e.g., drainage water, wastewater, and brackish water) has become a more important consideration in supplementing supply [1,2,3,4]. Precise and fast assessment and monitoring ways for crop health status to quantify crop characteristics can enhance site-specific management to obtain a higher crop productivity in comparison to traditional monitoring techniques. In this regard, remote sensing of different platforms may offer a reliable tool in precision agriculture [10,11,12,13]. Drought and salinity stress are considered major inhibitors of strategic crop production (e.g., wheat, corn and rice), and more energies to spot their effects for irrigation management strategies are compulsory as several studies have quantitatively evaluated the potential of remote sensing to identify cultivated areas that are suffering from water and/or salinity stress [14]. A reliable alternative is the utilization of remote sensing of different platforms as a fast and robust tool that integrates the crop response to the negative effects of drought and salinity
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call