Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

Abstract

Assessing the spatial variation of soil and crop properties is the basis for site specific management of crop practices in precision agriculture applications. To this aim, proximal and remote spectral vegetation indices are increasingly replacing soil analysis. In this study the spatial variation of soil properties, proximal and remote spectral vegetation indices were compared in a winter wheat (Triticum aestivum L.) crop grown in a 4.15 ha field in northern Italy. Soil analysis (particle size distribution, pH, carbonates, C, total N, available P, exchangeable cations and electrical conductivity) was geo-referentially carried out; the proximal indices chlorophyll content by N-Tester and normalised difference vegetation index through GreenSeeker were determined in three dates during stem elongation; the remote indices PurePixel TM chlorophyll index and PurePixel TM vegetation index were determined through the Landsat 8 satellite in three dates during the same wheat stage. Dry biomass yield (DBY), grain yield (GY) and yield components were determined at harvest. Soil, proximal and remote data were submitted to principal component analysis (PCA), and the retained PCs were clustered to delineate areas at low, intermediate and high yield potential, based on soil parameters (CLUsp), proximal (CLUpi), and remote vegetation indices (CLUri). DBY and GY were significantly correlated with several soil parameters and vegetation indices. Spatial distribution of soil and crop data consistently depicted a low performing area (GY<3 Mg ha -1 ) and a high performing one (GY>8 Mg ha -1 ). CLUsp determined a lower GY difference between low and high performing area (+60%), compared to CLUpi and CLUri (almost +100%). In CLUsp and CLUpi the low and high performing area were of similar size (25 and 29% for the two respective areas in CLUsp; 25 and 33% in CLUpi), whereas in CLUri they were quite different (16 and 46%). Lastly, yield potential levels determined by vegetation indices (CLUpi and CLUri) exhibited a better degree of agreement with DBY and GY levels, than soil parameters (CLUsp). In exchange for this, the above referred soil parameters are quite consistent in time, allowing soil data to be used for more years. On concluding, PCA followed by clustering resulted in a robust delineation of field areas at different yield potential. This is the premise for developing research driven strategies of practical use.

Applying the NDVI from satellite images in delimiting management zones for annual crops

“…The NDVI data showed lower CV values (i.e., ranging from 2 to 12 %) than those verified for productivity. These results are consistent with those obtained in other studies (Zerbato et al, 2016;Barbanti et al, 2017), and may be due to the fact that NVDI values range from 0 to 1. Another factor that contributes to lower variation of NDVI data is associated with the phenological stages of crops at the moment of the image acquisitions.…”

Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices

“…The similar spatial patter exhibited by yield maps across the five years ( Figure 2) is echoed in a previous survey on a narrower portion (4.15 ha) of the same field [74]. In that work, soils in the northern part were shown to be quite sandy and poor in organic matter.…”

Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

“…Nevertheless, despite of the strongly significant correlation, it is based only on 14 data points, implying that relationships have to be handled with care. It is a higher correlation than retrieved in previous studies, where near-remotely sensed NDVI data were compared to NDVI from Sentinel-2 and Landsat 8 [59,60]. A likely reason for the very strong correlation is that this study was carried out in a very open subarctic woodland (in parts nearly treeless and then considered as heath) where understory vegetation contributes very much to the NDVI detected by the satellites.…”