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Crop Leaf Chlorophyll Content, Leaf Area Index and Biomass Retrieval...
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Crop Leaf Chlorophyll Content, Leaf Area Index and Biomass Retrieval from Landsat and Sentinel Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 7007

Special Issue Editors

Dr. Varaprasad Bandaru

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (1 paper)

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Research

22 pages, 5070 KiB  
Article
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://doi.org/10.3390/rs12162564 - 9 Aug 2020
Cited by 32 | Viewed by 5835
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 [...] Read more.
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 (r2 = 0.84, RMSE = 0.4 kg/m2 for stem biomass; r2 = 0.87, RMSE = 0.16 kg/m2 for AGB). This was higher than using SOMVI as input for the MGPRS (r2 = 0.71, RMSE = 1.12 kg/m2 for stem biomass; r2 = 0.71, RMSE = 0.56 kg/m2 for AGB) or SGPR (r2 = 0.63, RMSE = 1.08 kg/m2 for stem biomass; r2 = 0.67, RMSE = 1.08 kg/m2 for AGB). Relatively, higher accuracy for leaf biomass was achieved using SOMVI (r2 = 0.83) than using SODVI (r2 = 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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