Influence of Soil Properties on Maize and Wheat Nitrogen Status Assessment from Sentinel-2 Data

Abstract

<jats:p>Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are now available to adjust the nitrogen (N) input to the actual crop needs. Many of the methods have been developed for proximal sensors, but increasing attention is being given to satellite-based N management systems, many of which rely on the assessment of the N status of crops. In this study, the reliability of the crop nutritional status assessment through the estimation of the nitrogen nutrition index (NNI) from Sentinel-2 (S2) satellite images was examined, focusing of the impact of soil properties variability for crop nitrogen deficiency monitoring. Vegetation indices (VIs) and biophysical variables (BVs), such as the green area index (GAI_S2), leaf chlorophyll content (Cab_S2), and canopy chlorophyll content (CCC_S2), derived from S2 imagery, were used to investigate plant N status and NNI retrieval, in the perspective of its use for guiding site-specific N fertilization. Field experiments were conducted on maize and on durum wheat, manipulating 4 groups of plots, according to soil characteristics identified by a soil map and quantified by soil samples analysis, with different N treatments. Field data collection highlighted different responses of the crops to N rate and soil type in terms of NNI, biomass (W), and nitrogen concentration (Na%). For both crops, plots in one soil class (FOR1) evidenced considerably lower values of BVs and stress conditions with respect to others soil classes even for high N rates. Soil samples analyses showed for FOR1 soil class statistically significant differences for pH, compared to the other soil classes, indicating that this property could be a limiting factor for nutrient absorption, hence crop growth, regardless of the amount of N distributed to the crop. The correlation analysis between measured crop related BVs and satellite-based products (VIs and S2_BVs) shows that it is possible to: (i) directly derive NNI from CCC_S2 (R2 = 0.76) and either normalized difference red edge index (NDRE) for maize (R2 = 0.79) or transformed chlorophyll absorption ratio index (TCARI) for durum wheat (R2 = 0.61); (ii) indirectly estimate NNI as the ratio of plant nitrogen uptake (PNUa) and critical plant nitrogen uptake (PNUc) derived using CCC_S2 (R2 = 0.77) and GAI_S2 (R2 = 0.68), respectively. Results of this study confirm that NNI is a good indicator to monitor plants N status, but also highlights the importance of linking this information to soil properties to support N site-specific fertilization in the precision agriculture framework. These findings contribute to rational agro-practices devoted to avoid N fertilization excesses and consequent environmental losses, bringing out the real limiting factors for optimal crop growth.</jats:p>

Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data

“…Ref. [ 59 ] also reported the S2 NIR band in conjunction with the S2 red band to be the most important for N conc in winter wheat. The importance of the S2 SWIR bands for N conc was mentioned by [ 101 ].…”

“…The simulation of satellite data based on FS data has been evaluated in previous studies [ 24 , 34 , 59 , 101 ]. Of these, [ 24 , 34 ] performed simulation of S2 bands using an FS with wavelength up to 1000 nm.…”

“…Few studies are available for direct plant N estimation using satellite imagery [ 59 , 60 ]. A limitation for such studies is the need for large and expensive field trials for model training and calibration.…”

The Application of Remote Sensing Techniques and Spectral Analyzes to Assess the Content of Heavy Metals in Soil – A Case Study of Barania Góra Reserve, Poland

“…Growing cereals requires sustainability not only in terms of production but also its environmental impact. The very availability of nitrogen in soil varies spatially within the cultivated plants and depends on the physicochemical properties of the soil, including its morphology [18].…”

Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images

“…Spectral wavebands in green, red, and red edge regions were used to calculate vegetation indices (VIs) to establish the relationship with crop N [7]. VIs and biophysical variables derived from Sentinel-2 satellite images were examined for estimation of plant N status [11]. Loozen et al [12] used satellite-based VIs and environmental variables to map crop N in European forests.…”