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Dr. Varaprasad Bandaru

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Guest Editor

Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
Interests: agroecosystem modeling; remote sensing; leaf area index; crop phenology; cropland carbon fluxes; crop condition and yield monitoring

Dr. Craig Daughtry

E-Mail Website
Guest Editor

Hydrology and Remote Sensing Laboratory, US Department of Agriculture Agricultural Research Service, Beltsville, MD 20705, USA
Interests: research on the agronomic, physical, and spectral properties of plants and soils; research to assess crop residue cover and soil tillage intensity; research to measure and model the spatial variability of crops and soils at multiple scales
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Special Issue Information

Dear Colleagues,

Leaf chlorophyll content, leaf area index (LAI), and biomass are key biophysical and biochemical parameters indicating the status of crop growth and development. These parameters are used to assess crop water and nutrient status, carbon assimilation rates, surface energy balance, and near-surface climate variables. Remote sensing offers a unique, cost-effective means for providing estimates of leaf chlorophyll, LAI, and biomass over large geographical areas at various spatial and temporal scales.

Given that croplands are typically characterized by fine-scale heterogeneity, high spatial resolution satellite observations are needed to estimate crop-specific biophysical and biogeochemical variables. The NASA Landsat and Copernicus Sentinel missions provide medium-to-high spatial resolution multispectral data. Further, advances in data fusion techniques make it possible to produce spectral products at a finer temporal and spatial resolutions (e.g., Harmonized Landsat Sentinel data product) by leveraging available temporal and spatial information in both Landsat and Sentinel data. The availability of these high spatial and temporal resolution spectral data enables retrieval of individual crop characteristics more accurately and in a timely manner.

There is a wide range of parametric and nonparametric empirically and physically based approaches based on both optical and SAR satellite observations that can be used to determine leaf chlorophyll content, LAI, and biomass. In this Special Issue, we aim to compile the state-of-the-art research in this area, and we invite articles covering different methods using Landsat (optical), Sentinel (optical and SAR), and Landsat–Sentinel fused datasets for retrieval of the chlorophyll content, LAI, and biomass of various crops.

Dr. Varaprasad Bandaru
Dr. Craig Daughtry
Guest Editors

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Keywords

radiative transfer modeling
machine learning and AI
optical and microwave
Landsat and Sentinel
data fusion techniques
crops
leaf chlorophyll
leaf area index
biomass

Published Papers (1 paper)

Research

22 pages, 5070 KiB

Open AccessArticle

Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking

by Yeshanbele Alebele, Xue Zhang, Wenhui Wang, Gaoxiang Yang, Xia Yao, Hengbiao Zheng, Yan Zhu, Weixing Cao and Tao Cheng

Remote Sens. 2020, 12(16), 2564; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162564 - 09 Aug 2020

Cited by 25 | Viewed by 4818

Abstract

Crop biomass is a critical variable to make sound decisions about field crop monitoring activities (fertilizers and irrigation) and crop productivity forecasts. More importantly, crop biomass estimations by components are essential for crop growth monitoring as the yield formation of crops results from the accumulation and transportation of substances between different organs. Retrieval of crop biomass from synthetic aperture radar SAR or optical imagery is of paramount importance for in-season monitoring of crop growth. A combination of optical and SAR imagery can compensate for their limitations and has exhibited comparative advantages in biomass estimation. Notably, the joint estimations of biophysical parameters might be more accurate than that of an individual parameter. Previous studies have attempted to use satellite imagery to estimate aboveground biomass, but the estimation of biomass for individual organs remains a challenge. Multi-target Gaussian process regressor stacking (MGPRS), as a new machine learning method, can be suitably utilized to estimate biomass components jointly from satellite imagery data, as the model does not require a large amount of data for training and can be adjusted to the required degrees of relationship exhibited by the given data. Thus, the aim of this study was to estimate the biomass of individual organs by using MGPRS in conjunction with optical (Sentinel-2A) and SAR (Sentinel-1A) imagery. Two hybrid indices, SAR and optical multiplication vegetation index (SOMVI) and SAR and optical difference vegetation index (SODVI), have been constructed to examine their estimation performance. The hybrid vegetation indices were used as input for the MGPRS and single-target Gaussian process regression (SGPR). The accuracy of the estimation methods was analyzed by in situ measurements of aboveground biomass (AGB) and organ biomass conducted in 2018 and 2019 over the paddy rice fields of Xinghua in Jiangsu Province, China. The results showed that the combined indices (SOMVI and SODVI) performed better than those derived from either the optical or SAR data only. The best predictive accuracy was achieved by the MGPRS using SODVI as input (

r^{2}

= 0.84, RMSE = 0.4 kg/m² for stem biomass;

r^{2}

= 0.87, RMSE = 0.16 kg/m² for AGB). This was higher than using SOMVI as input for the MGPRS (

r^{2}

= 0.71, RMSE = 1.12 kg/m² for stem biomass;

r^{2}

= 0.71, RMSE = 0.56 kg/m² for AGB) or SGPR (

r^{2}

= 0.63, RMSE = 1.08 kg/m² for stem biomass;

r^{2}

= 0.67, RMSE = 1.08 kg/m² for AGB). Relatively, higher accuracy for leaf biomass was achieved using SOMVI (

r^{2}

= 0.83) than using SODVI (

r^{2}

= 0.73) as input for MGPRS. Our results demonstrate that the combined indices are effective by integrating SAR and optical imagery and MGPRS outperformed SGPR with the same input variable for estimating rice crop biomass. The presented workflow will improve the estimation of crops biomass components from satellite data for effective crop growth monitoring. Full article

(This article belongs to the Special Issue Crop Leaf Chlorophyll Content, Leaf Area Index and Biomass Retrieval from Landsat and Sentinel Data)

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