Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt

Abstract

In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91–0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources.

Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt

“…In the Nile Delta region, grain crop production is mainly hindered by issues of water availability and salinity as a result of limited water resources [6]. Detecting stress in agricultural crops across vast areas by traditional methods (e.g., point-sampling) is time consuming and costly and is sometimes unrepresentative in offering a spatial panorama of stress patterns [4,[7][8][9].…”

“…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]. Irrigation and water salinity management procedures must be carefully managed to maximize water use efficiency and to avoid higher salinity levels in the root zone by adding the optimum rates of water [6,15,16]. Observing crop health status is normally dependent upon destructive sampling, which as mentioned earlier is time consuming, costly, and is an unrepresentative process.…”

“…Remote sensing of different platforms has been used to monitor agricultural crops status over the past few decades. It is able to provide instantaneous information over vast cultivated regions of high economic importance, such as wheat and corn [6,24,25]. Such instantaneous information related to crop productivity is fundamentally crucial for experts and decision makers, from small-scale growers to the national authority, offering a step forward for maximizing crop yield whilst using available water resources in a more efficient way.…”

“…Many previous studies have revealed the feasibility of satellite images to quantify various crop characteristics (e.g., biochemical and biophysical). These include, for example, the identification of chlorophyll concentration [6,[26][27][28]; the prediction of yield [29][30][31][32]; measuring leaf area index (LAI) [33][34][35]; the identification of nitrogen status [36]; estimating above-ground biomass [37]; quantifying evapotranspiration [38]; and the detection of crop disease [39,40]. These above-mentioned studies used satellite-based V-SRIs for the identification of crop characteristics, showing the robustness of high resolution satellite imagery (<2 m) to assess crop growth performance and to avoid stress on crops at a local scale.…”

Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

“…Recently, proximal remote sensing has been regarded as the most promising tool for non-destructively and rapidly assessing plant water status indicators in a timely manner. This tool is based on the variation in spectral signatures of the canopy of the electromagnetic spectrum (400-2500 nm), which is strongly associated with several biophysical and biochemical plant characteristics [20][21][22][23][24]. For instance, the spectral signatures of the canopy in the near-infrared (NIR; 700-1300 nm) region are closely associated with the changes that occur in the canopy water status, because the water bands in this region can penetrate deeper into the canopy, thereby allowing to estimate the water status of plants [10,25,26].…”