Dry biomass estimation of paddy rice with Sentinel-1A satellite data using machine learning regression algorithms

•For the entire growing season, VV is more consistent with rice dry biomass than VH.•For the entire growing season, VHVV produced more accurate rice biomass estimates.•RF outperformed the other algorithms in rice dry biomass estimation with VHVV.•The most accurate rice biomass estimates were recorde...

Full description

Saved in:
Bibliographic Details
Published in:Computers and electronics in agriculture Vol. 176; p. 105674
Main Authors: Mansaray, Lamin R., Zhang, Kangyu, Kanu, Adam Sheka
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.09.2020
Elsevier BV
Subjects:
agriculture
Algorithms
Biomass
China
Crop growth
data collection
Datasets
decision support systems
Decision trees
Dry biomass
electronics
Elongation
Estimates
Estimation
Information retrieval
interferometry
Machine learning
Paddy rice
Parameter estimation
precision agriculture
remote sensing
Rice
Ripening
rough rice
Sentinel-1A
Support vector machines
Vertical polarization
China
Paddy rice
Sentinel-1A
Estimation
Dry biomass
Machine learning
ISSN:0168-1699, 1872-7107
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•For the entire growing season, VV is more consistent with rice dry biomass than VH.•For the entire growing season, VHVV produced more accurate rice biomass estimates.•RF outperformed the other algorithms in rice dry biomass estimation with VHVV.•The most accurate rice biomass estimates were recorded at the reproductive phase.•VH with k-NN produced the best rice biomass estimates at the reproductive phase. Despite the growing use of Sentinel-1 in retrieving crop growth information, its potential for obtaining improved estimates of rice biophysical parameters has not been fully investigated. This study therefore assesses the capabilities of Sentinel-1A temporal datasets, of the interferometric wide-swath (IW) mode that include the vertical transmitted and horizontal received (VH), and vertical transmitted and vertical received (VV) polarizations, and the linear combination of VH and VV (VHVV), to estimate rice dry biomass over a test site located in southeast China. To this end, four machine learning regression algorithms; Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), were applied to assess the aforementioned datasets. The results indicated that for the estimation of rice dry biomass over the entire growing season, VHVV data with RF produced the most accurate estimates with an R2 of 0.73 and an RMSE of 462.4 g/m2. However, this study suggests that a consideration of the rice growth phases could produce more accurate estimates of dry biomass as evident by an R2 of 0.72 and an RMSE of 362.4 g/m2 recorded at the reproductive phase (elongation to milking) with VH data and k-NN. Hence research works that would investigate the estimation of rice dry biomass at the vegetative (transplanting to elongation) and maturity (milking to ripening) phases are required to ascertain whether a growth-phase approach could obtain more accurate rice biomass estimates that better meet precision agriculture data needs.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2020.105674