Topical Collection "Feature Papers for Section Biogeosciences Remote Sensing"

A topical collection in Remote Sensing (ISSN 2072-4292). This collection belongs to the section "Biogeosciences Remote Sensing".

Editor

Prof. Dr. Alfredo R. Huete
Website
Collection Editor
School of Life Sciences, The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, 15 Broadway Road Ultimo, NSW 2007, Australia
Interests: biophysical remote sensing; phenology; satellite products;carbon and water fluxes; land-use science; drought studies
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Topical Collection Information

Dear Colleagues,

The Topical Collection of Biogeosciences Remote Sensing is from the Remote Sensing Journal (ISSN 2072-4292), and is dedicated to the publication and discussion of research articles, letters, reviews, and communications on all aspects of remote sensing science and technologies that provide insights and address challenges in the biogeosciences of the Earth system. Remote sensing is a key component to advance the monitoring, modelling, and understanding of land/ocean surface fluxes and biogeochemical cycling, as well as the underlying biological/physical attributes that regulate the interactions among the land, ocean, and atmosphere. We welcome reviews and outstanding articles to this Topical Collection in order to improve the current knowledge on biogeosciences remote sensing. Manuscripts for this important Topical Collection of Remote Sensing will be accepted by the editorial office, the editor-in-chief, and editorial board members by invitation only.

  • Carbon cycle science and applications
  • Biogeochemistry (soil nutrient availability and fluxes)
  • Biogeophysics (energy, water states and fluxes, and skin temperature)
  • Canopy biophysics
  • Ecosystem structure, land cover, soils, and species composition
  • Vegetation dynamics and phenology (leaf to ecosystems)
  • Land/ocean–atmosphere coupling and interactions
  • Data fusion and data assimilation
  • Machine learning
  • Ecological forecasting

Prof. Dr. Alfredo R. Huete
Collection Editor

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 papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection 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 2200 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.

Published Papers (3 papers)

2020

Jump to: 2019

Open AccessArticle
Modeling the Effects of Global and Diffuse Radiation on Terrestrial Gross Primary Productivity in China Based on a Two-Leaf Light Use Efficiency Model
Remote Sens. 2020, 12(20), 3355; https://doi.org/10.3390/rs12203355 - 14 Oct 2020
Abstract
Solar radiation significantly affects terrestrial gross primary productivity (GPP). However, the relationship between GPP and solar radiation is nonlinear because it is affected by diffuse radiation. Solar radiation has undergone a shift from darker to brighter values over the past 30 years in [...] Read more.
Solar radiation significantly affects terrestrial gross primary productivity (GPP). However, the relationship between GPP and solar radiation is nonlinear because it is affected by diffuse radiation. Solar radiation has undergone a shift from darker to brighter values over the past 30 years in China. However, the effects on GPP of variation in solar radiation because of changes in diffuse radiation are unclear. In this study, national global radiation in conjunction with other meteorological data and remotely sensed data were used as input into a two-leaf light use efficiency model (TL-LUE) that simulated GPP separately for sunlit and shaded leaves for the period from 1981 to 2012. The results showed that the nationwide annual global radiation experienced a significant reduction (2.18 MJ m−2 y−1; p < 0.05) from 1981 to 2012, decreasing by 1.3% over this 32-year interval. However, the nationwide annual diffuse radiation increased significantly (p < 0.05). The reduction in global radiation from 1981 to 2012 decreased the average annual GPP of terrestrial ecosystems in China by 0.09 Pg C y−1, whereas the gain in diffuse radiation from 1981 to 2012 increased the average annual GPP in China by about 50%. Therefore, the increase in canopy light use efficiency under higher diffuse radiation only partially offsets the loss of GPP caused by lower global radiation. Full article
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Open AccessArticle
Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn
Remote Sens. 2020, 12(15), 2392; https://doi.org/10.3390/rs12152392 - 26 Jul 2020
Abstract
Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. [...] Read more.
Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. Spectral analysis of UAS acquired spatiotemporal images can be used to develop a statistical model to predict yield based on different phenological stages. Identifying critical vegetation indices (VIs) and significant spectral information could lead to increased yield prediction accuracy. The objective of this study was to develop a yield prediction model at specific phenological stages using spectral data obtained from a corn field. The available spectral bands (red, blue, green, near infrared (NIR), and red-edge) were used to analyze 26 different VIs. The spectral information was collected from a cornfield at Mississippi State University using a MicaSense multispectral red-edge sensor, mounted on a UAS. In this research, a new empirical method used to reduce the effects of bare soil pixels in acquired images was introduced. The experimental design was a randomized complete block that consisted of 16 blocks with 12 rows of corn planted in each block. Four treatments of nitrogen (N) including 0, 90, 180, and 270 kg/ha were applied randomly. Random forest was utilized as a feature selection method to choose the best combination of variables for different stages. Multiple linear regression and gradient boosting decision trees were used to develop yield prediction models for each specific phenological stage by utilizing the most effective variables at each stage. At the V3 (3 leaves with visible leaf collar) and V4-5 (4-5 leaves with visible leaf collar) stages, the Optimized Soil Adjusted Vegetation Index (OSAVI) and Simplified Canopy Chlorophyll Content Index (SCCCI) were the single dominant variables in the yield predicting models, respectively. A combination of the Green Atmospherically Resistant Index (GARI), Normalized Difference Red-Edge (NDRE), and green Normalized Difference Vegetation Index (GNDVI) at V6-7, SCCCI, and Soil-Adjusted Vegetation Index (SAVI) at V10,11, and SCCCI, Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARIgreen) at tasseling stage (VT) were the best indices for predicting grain yield of corn. The prediction models at V10 and VT had the greatest accuracy with a coefficient of determination of 0.90 and 0.93, respectively. Moreover, the SCCCI as a combined index seemed to be the most proper index for predicting yield at most of the phenological stages. As corn development progressed, the models predicted final grain yield more accurately. Full article
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2019

Jump to: 2020

Open AccessArticle
Response of Tallgrass Prairie to Management in the U.S. Southern Great Plains: Site Descriptions, Management Practices, and Eddy Covariance Instrumentation for a Long-Term Experiment
Remote Sens. 2019, 11(17), 1988; https://doi.org/10.3390/rs11171988 - 23 Aug 2019
Cited by 1
Abstract
Understanding the consequences of different management practices on vegetation phenology, forage production and quality, plant and microbial species composition, greenhouse gas emissions, and water budgets in tallgrass prairie systems is vital to identify best management practices. As part of the Southern Plains Long-Term [...] Read more.
Understanding the consequences of different management practices on vegetation phenology, forage production and quality, plant and microbial species composition, greenhouse gas emissions, and water budgets in tallgrass prairie systems is vital to identify best management practices. As part of the Southern Plains Long-Term Agroecosystem Research (SP-LTAR) grassland study, a long-term integrated Grassland-LivestOck Burning Experiment (iGLOBE) has been established with a cluster of six eddy covariance (EC) systems on differently managed (i.e., different burning and grazing regimes) native tallgrass prairie systems located in different landscape positions. The purpose of this paper is to describe this long-term experiment, report preliminary results on the responses of differently managed tallgrass prairies under variable climates using satellite remote sensing and EC data, and present future research directions. In general, vegetation greened-up and peaked early, and produced greater forage yields in burned years. However, drought impacts were greater in burned sites due to reductions in soil water availability by burning. The impact of grazing on vegetation phenology was confounded by several factors (e.g., cattle size, stocking rate, precipitation). Moreover, prairie systems located in different landscapes responded differently, especially in dry years due to differences in water availability. The strong correspondence between vegetation phenology and eddy fluxes was evidenced by strong linear relationships of a greenness index (i.e., enhanced vegetation index) with evapotranspiration and gross primary production. Results indicate that impacts of climate and management practices on vegetation phenology may profoundly impact carbon and water budgets of tallgrass prairie. Interacting effects of multiple management practices and inter-annual climatic variability on the responses of tallgrass prairie highlight the necessity of establishing an innovative and comprehensive long-term experiment to address inconsistent responses of tallgrass prairie to different intensities, frequencies, timing, and duration of management practices, and to identify best management practices. Full article
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