Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

Abstract

Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as input for rangeland carrying capacity and stocking rate models. The estimation of leaf N has been successful using hyperspectral and commercial high spatial resolution satellite data such as WorldView-2 and RapidEye. Empirical methods have been used successfully to estimate leaf N, on the basis that it correlates with leaf chlorophyll. As such, leaf N was estimated using red edge based indices. The new Sentinel-2 sensor has two red edge bands, is freely available, and could further improve the estimation of leaf N at a regional scale. The objective of this study is to develop red edge based Sentinel-2 models derived from an analytical spectral device (ASD) spectrometer to map and monitor leaf N using Sentinel-2 images. Field work for leaf N and ASD data were collected in 2014 (December) in and around Kruger National Park, South Africa. ASD data were resampled to the Sentinel-2 spectral configuration using the spectral response function. The Sentinel-2 data for various dates were acquired from the European Space Agency (ESA) portal. The Sentinel-2 atmospheric correction (Sen2Cor) process was implemented. Simple empirical regression was used to estimate leaf N. High leaf N prediction accuracy was achieved at the ASD level and the best model was inverted on Sentinel-2 images to explain leaf N distribution at a regional scale over time. The spatial distribution of leaf N is influenced by the underlying geological substrate, fire frequency and other environmental variables. This study is a demonstration of how ASD data can be used to calibrate Sentinel-2 for leaf N estimation and mapping.

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

“…Regarding precision fertilization management, two different approaches, both fairly similar to those described above for yield, have been developed. On one hand, the use of radiative transfer models to obtain chlorophyll content [75][76][77], and on the other hand, using empirically correlated vegetation indexes and bands to assess the nitrogen status of the crop [78][79][80][81]. This strategy generally includes the three Sentinel-2 RE bands (705, 740, and 783 nm, Figure 2), which are very important for the retrieval of chlorophyll content [82].…”

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

“…The importance of crude protein in livestock diets has been studied for many decades [21]. The use of remote sensing, field and laboratory spectroscopy is also well established in the literature [22][23][24]. Pasture nutrient status is a field of study that has recently re-emerged since the development and affordability of UAV technologies.…”

Vegetation traits of pre-Alpine grasslands in southern Germany

“…Remotely sensed data from unmanned aircraft systems (UAS) and satellites are one option to overcome this gap in the mapping and assessment of vegetation traits on a larger spatial scale 13 . In the last years, several studies made use of the potential of remotely sensed data to map grassland vegetation traits like above-ground biomass [14][15][16][17][18][19][20][21] and quality parameters [22][23][24][25][26][27] . Geo-referenced in-situ data are of utmost importance for the development of remote sensing-based models for the estimation of grassland vegetation traits, for both calibration and validation purposes.…”