TY - JOUR
T1 - PRISMA and Sentinel-2 spectral response to the nutrient composition of grains
AU - Belgiu, M.
AU - Marshall, M.
AU - Boschetti, Mirco
AU - Pepe, Monica
AU - Stein, A.
AU - Nelson, A.
N1 - Funding Information:
The primary source of support for this project was the European Space Agency (ESA) – EO Science for society grant (EOEO-5 Block 4, 4000130277/20/I-DT). The title of the project was “HyNutri: Sensing ‘Hidden Hunger’ with Sentinel-2 and Hyperspectral (PRISMA)” (http://www.hynutri.nl). Additional support came from the PRISCAV project (ASI Contract 2019-5-HH-0). This project acquired the PRISMA imagery and conducted field campaigns to assess PRISMA data quality and pre-processing. We would like to thank Dr. Donato Cillis and Gabriele Dottori of IBF-Servizi for collecting and processing the sample grains. We extend our gratitude to thank Dr. Caroline Lievens and Kathrin Zweers from ITC's Geoscience Laboratory. They received the sample grains and conducted the nutrient analysis.
Funding Information:
The primary source of support for this project was the European Space Agency (ESA) – EO Science for society grant (EOEO-5 Block 4, 4000130277/20/I-DT ). The title of the project was “HyNutri: Sensing ‘Hidden Hunger’ with Sentinel-2 and Hyperspectral (PRISMA)” ( http://www.hynutri.nl ). Additional support came from the PRISCAV project (ASI Contract 2019-5-HH-0). This project acquired the PRISMA imagery and conducted field campaigns to assess PRISMA data quality and pre-processing. We would like to thank Dr. Donato Cillis and Gabriele Dottori of IBF-Servizi for collecting and processing the sample grains. We extend our gratitude to thank Dr. Caroline Lievens and Kathrin Zweers from ITC's Geoscience Laboratory. They received the sample grains and conducted the nutrient analysis.
Publisher Copyright:
© 2023 The Authors
PY - 2023/7/1
Y1 - 2023/7/1
N2 - Micronutrient malnutrition is a global challenge affecting >2 billion people, in particular those with a crop-based diet and limited access to nutrient-rich food sources. Conventional methods for measuring the crop nutrients such as wet chemical analysis of grains are time-consuming and cost-prohibitive and, consequently, unsuitable for the consistent quantification of nutrients across space and time. In this study, we propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest. We collected grain samples for corn, rice, soybean, and wheat from a farm situated in Italy and measured their nutrient concentrations in the lab. These measurements together with the PRISMA and Sentinel-2 images acquired at the main phases of crop development (vegetative, reproductive, maturity) were used as input for two-band vegetation indices (TBVIs) and Partial Least Squares Regression (PLSR) to predict Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Sulphur (S) and Zinc (Zn). Models' performances were assessed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). For PRISMA images, the best prediction results were obtained for P in soybean (R2 = 0.69), K in soybean (R2 = 0.66), Mg in soybean (R2 = 0.58), Fe in soybean (R2 = 0.57), K in wheat (R2 = 0.57), K in corn (R2 = 0.55), P in wheat (R2 = 0.51), S in rice (R2 = 0.58) using TBVIs. In contrast to PRISMA, PLSR outperformed TBVIs when Sentinel-2 images were used as input. For Sentinel-2, the best predictions were obtained for P in soybean (R2 = 0.73), K in wheat (R2 = 0.67), Mg in soybean (R2 = 0.62), Zn in wheat (R2 = 0.56), Fe in soybean (R2 = 0.52), P in wheat (R2 = 0.52). Our study showed that estimating the nutrient composition of crops using remote sensing images has the potential to change how we approach a cost-effective, timely, and spatially explicit representation of the crops' nutritional quality.
AB - Micronutrient malnutrition is a global challenge affecting >2 billion people, in particular those with a crop-based diet and limited access to nutrient-rich food sources. Conventional methods for measuring the crop nutrients such as wet chemical analysis of grains are time-consuming and cost-prohibitive and, consequently, unsuitable for the consistent quantification of nutrients across space and time. In this study, we propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest. We collected grain samples for corn, rice, soybean, and wheat from a farm situated in Italy and measured their nutrient concentrations in the lab. These measurements together with the PRISMA and Sentinel-2 images acquired at the main phases of crop development (vegetative, reproductive, maturity) were used as input for two-band vegetation indices (TBVIs) and Partial Least Squares Regression (PLSR) to predict Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Sulphur (S) and Zinc (Zn). Models' performances were assessed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). For PRISMA images, the best prediction results were obtained for P in soybean (R2 = 0.69), K in soybean (R2 = 0.66), Mg in soybean (R2 = 0.58), Fe in soybean (R2 = 0.57), K in wheat (R2 = 0.57), K in corn (R2 = 0.55), P in wheat (R2 = 0.51), S in rice (R2 = 0.58) using TBVIs. In contrast to PRISMA, PLSR outperformed TBVIs when Sentinel-2 images were used as input. For Sentinel-2, the best predictions were obtained for P in soybean (R2 = 0.73), K in wheat (R2 = 0.67), Mg in soybean (R2 = 0.62), Zn in wheat (R2 = 0.56), Fe in soybean (R2 = 0.52), P in wheat (R2 = 0.52). Our study showed that estimating the nutrient composition of crops using remote sensing images has the potential to change how we approach a cost-effective, timely, and spatially explicit representation of the crops' nutritional quality.
KW - Agriculture
KW - Food security
KW - Hidden hunger
KW - Hyperspectral images
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-HYBRID
KW - UT-Hybrid-D
U2 - 10.1016/j.rse.2023.113567
DO - 10.1016/j.rse.2023.113567
M3 - Article
AN - SCOPUS:85152598694
SN - 0034-4257
VL - 292
JO - Remote sensing of environment
JF - Remote sensing of environment
M1 - 113567
ER -
