TY - JOUR
T1 - Testing Sentinel-2 spectral configurations for estimating relevant crop biophysical and biochemical parameters for precision agriculture using tree-based and kernel-based algorithms
AU - Kganyago, Mahlatse
AU - Adjorlolo, Clement
AU - Sibanda, Mbulisi
AU - Mhangara, Paidamwoyo
AU - Laneve, Giovanni
AU - Alexandridis, Thomas
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - Sentinel-2 spectral configurations, S2-10m and S2-20m, were evaluated for retrieving essential crop biophysical and biochemical parameters and their effect on the performance of three machine learning regression algorithms (MLRAs) in two African semi-arid sites. The results were benchmarked against all spectral bands (S2-All). The results show that the S2-20m was more robust in retrieving Leaf Area Index (LAI) (RMSE cv : 0.58 m2 m−2, 0.47 m2 m−2), while the S2-10m provided optimal retrievals Leaf Chlorophyll a + b (LC ab) (RMSE cv : 6.89 µg cm−2, 7.02 µg cm−2) for the two sites, respectively. In contrast, S2-20m performed better in retrieving Canopy Chlorophyll Content (CCC) in Bothaville to an RMSE cv of 35.65 µg cm−2, while S2-10m yielded relatively lower uncertainties (RMSE cv of 26.84 µg cm−2) in Harrismith. Moreover, various MLRAs were sensitive to the various spectral configurations, and performance varied by site. GPR and XGBoost were more robust, and thus have the most potential for crop biophysical and biochemical parameter retrieval in both sites. Based on the benchmark results, the two configurations can be used independently. The results obtained here are relevant for the rapid development of essential crop biophysical and biochemical parameters for precision agriculture using Sentinel-2’s 10 m or 20 m bands, without the need for resampling.
AB - Sentinel-2 spectral configurations, S2-10m and S2-20m, were evaluated for retrieving essential crop biophysical and biochemical parameters and their effect on the performance of three machine learning regression algorithms (MLRAs) in two African semi-arid sites. The results were benchmarked against all spectral bands (S2-All). The results show that the S2-20m was more robust in retrieving Leaf Area Index (LAI) (RMSE cv : 0.58 m2 m−2, 0.47 m2 m−2), while the S2-10m provided optimal retrievals Leaf Chlorophyll a + b (LC ab) (RMSE cv : 6.89 µg cm−2, 7.02 µg cm−2) for the two sites, respectively. In contrast, S2-20m performed better in retrieving Canopy Chlorophyll Content (CCC) in Bothaville to an RMSE cv of 35.65 µg cm−2, while S2-10m yielded relatively lower uncertainties (RMSE cv of 26.84 µg cm−2) in Harrismith. Moreover, various MLRAs were sensitive to the various spectral configurations, and performance varied by site. GPR and XGBoost were more robust, and thus have the most potential for crop biophysical and biochemical parameter retrieval in both sites. Based on the benchmark results, the two configurations can be used independently. The results obtained here are relevant for the rapid development of essential crop biophysical and biochemical parameters for precision agriculture using Sentinel-2’s 10 m or 20 m bands, without the need for resampling.
KW - Crop biophysical parameters
KW - eXtreme Gradient Boosting
KW - Gaussian process regression
KW - Random Forest
KW - Sentinel-2
UR - http://www.scopus.com/inward/record.url?scp=85142225718&partnerID=8YFLogxK
U2 - 10.1080/10106049.2022.2146764
DO - 10.1080/10106049.2022.2146764
M3 - Article
AN - SCOPUS:85142225718
SN - 1010-6049
JO - Geocarto International
JF - Geocarto International
ER -