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Remote Sensing
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Understanding Biosphere-Atmosphere Interactions with Remote Sensing
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Understanding Biosphere-Atmosphere Interactions with Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 19084

Special Issue Editors

Dr. Praveena Krishnan

E-Mail Website
Guest Editor
NOAA ARL Atmospheric Turbulence and Diffusion Division, Oak Ridge, TN 37830, USA
Interests: atmospheric boundary layer processes; biosphere-atmosphere interactions; micrometeorology; remote sensing
Dr. Shusen Wang

E-Mail Website
Guest Editor
Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, ON K1A 0E4, Canada
Interests: water cycle and water resources; land surface process; remote sensing; modeling

Special Issue Information

Dear Colleagues,

Remote sensing observations are critical to elucidate the fundamental physical, chemical, and biological processes needed to quantify biosphere–atmospheric interactions from local to global scales. Significant progress in the development and advances in remote sensing techniques, such as, light detection and ranging (LiDAR), thermal infrared (TIR), multispectral, hyperspectral and solar-induced chlorophyll fluorescence (SIF) sensors capable of unprecedented spectral and spatiotemporal resolution, offer new insights into the quantitative remote sensing of the biosphere. We invite manuscripts from original research that synthesizes and advances our understanding of the energy, water, carbon, and trace gas exchange processes, drivers, coupling, interactions, teleconnections, and feedbacks in the biosphere-atmosphere interface across all spatial and temporal scales. Contributions dealing with remote sensing technologies and applications of passive or active sensors onboard any platforms including ground/airborne/UAV/satellite or its combinations with modeling efforts or reanalysis are welcome.

Potential topics include but are not limited to:

  • Surface radiation, energy, water, and trace gas processes and interactions:
    • Biophysical and biochemical processes;
    • Ecosystem structure and function and its role in carbon-water coupling;
    • Vegetation physiological function and dynamics;
    • Controls on biosphere-atmosphere interactions;
  • Seasonal to interannual variability ;
  • Natural and anthropogenic disturbances, extreme events ;
  • Boundary-layer processes;
  • Validation of remote sensing products from data from multiple platforms;
  • New methods and algorithms;
  • Scaling issues;
  • Multiple earth observation (EO) data integrations;
  • Field campaigns using active and/or passive remote sensing.

Dr. Praveena Krishnan
Dr. Shusen Wang
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

  • Biosphere-atmosphere interactions
  • Surface fluxes
  • Carbon and water cycles
  • Biophysical and biochemical process
  • Vegetation physiological function
  • Biometeorology
  • Land surface change
  • Climate variability

Published Papers (6 papers)

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Editorial

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3 pages, 161 KiB  
Editorial
Editorial for the Special Issue “Understanding Biosphere–Atmosphere Interactions with Remote Sensing”
by Praveena Krishnan and Shusen Wang
Remote Sens. 2023, 15(2), 332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020332 - 05 Jan 2023
Viewed by 895
Abstract
The terrestrial biosphere interacts with the free atmosphere through the exchange of momentum, energy and mass [...] Full article
(This article belongs to the Special Issue Understanding Biosphere-Atmosphere Interactions with Remote Sensing)

Research

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19 pages, 3300 KiB  
Article
Daily Evapotranspiration Estimations by Direct Calculation and Temporal Upscaling Based on Field and MODIS Data
by Yazhen Jiang, Junrui Wang and Yafei Wang
Remote Sens. 2022, 14(16), 4094; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164094 - 21 Aug 2022
Cited by 3 | Viewed by 1657
Abstract
Daily evapotranspiration (ET) integration is essential to various applications of agricultural water planning and management, ecohydrology, and energy balance studies. The constant reference evaporative fraction (EFr) temporal upscaling method has been proven to be efficient in extrapolating instantaneous ET to a daily timescale. [...] Read more.
Daily evapotranspiration (ET) integration is essential to various applications of agricultural water planning and management, ecohydrology, and energy balance studies. The constant reference evaporative fraction (EFr) temporal upscaling method has been proven to be efficient in extrapolating instantaneous ET to a daily timescale. Unlike upscaling methods, the direct calculation (DC) method developed in our previous study directly estimates daily ET without calculating instantaneous ET. The present study aimed to compare daily estimations of ET using the EFr and DC methods based on field and MODIS data at a site from the ChinaFLUX network. The estimation results were validated by eddy covariance (EC) ET both with and without the correction of energy imbalance. Based on field data, the results show that (i) the DC method performed with higher accuracy when compared to uncorrected EC measurements, while daily ET from both methods was overestimated; (ii) the DC method still performed better after the EC ET was corrected by the Residual Energy scheme, and the overestimations were significantly decreased; (iii) both methods performed best when compared with corrected ET by the Bowen Ratio scheme. The results from satellite data reveal that (i) the constant EFr method overestimated daily ET by a mean-bias-error (MBE) of 5.6 W/m2, and a root-mean-square error (RMSE) of 18.6 W/m2; and (ii) the DC method underestimated daily ET by a smaller MBE of −4.8 W/m2 and an RMSE of 22.5 W/m2. Therefore, the DC method has similar or better performance than the widely used constant EFr upscaling method and can estimate daily ET directly and effectively. Full article
(This article belongs to the Special Issue Understanding Biosphere-Atmosphere Interactions with Remote Sensing)
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19 pages, 41051 KiB  
Article
Effects of Climate Change on Vegetation Growth in the Yellow River Basin from 2000 to 2019
by Yanqun Ren, Jinping Liu, Suxia Liu, Zhonggen Wang, Tie Liu and Masoud Jafari Shalamzari
Remote Sens. 2022, 14(3), 687; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030687 - 31 Jan 2022
Cited by 27 | Viewed by 3405
Abstract
A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors [...] Read more.
A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000–2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB. Full article
(This article belongs to the Special Issue Understanding Biosphere-Atmosphere Interactions with Remote Sensing)
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25 pages, 5292 KiB  
Article
Assessment and Inter-Comparison of Multi-Source High Spatial Resolution Evapotranspiration Products over Lancang–Mekong River Basin, Southeast Asia
by Houbing Chen, Palingamoorthy Gnanamoorthy, Yaoliang Chen, Lamin R. Mansaray, Qinghai Song, Kuo Liao, Aoni Shi, Ganlin Feng and Chenna Sun
Remote Sens. 2022, 14(3), 479; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030479 - 20 Jan 2022
Cited by 6 | Viewed by 3130
Abstract
Evapotranspiration (ET) plays a crucial role in water balance within the global hydrological cycle. Timely assessment of ET products can provide the scientific basis for quantitative analysis of hydrological cycle processes and water resources assessment. In this paper, four high spatial resolution remote [...] Read more.
Evapotranspiration (ET) plays a crucial role in water balance within the global hydrological cycle. Timely assessment of ET products can provide the scientific basis for quantitative analysis of hydrological cycle processes and water resources assessment. In this paper, four high spatial resolution remote sensing ET products—the Moderate-resolution Imaging Spectroradiometer global terrestrial evapotranspiration product (MOD16), the ET product based on Penman–Monteith–Leuning equation version 2 (PML-V2), the ET product based on the Breathing Earth System Simulator (BESS) and the ET product of the Global LAnd Surface Satellite (GLASS)—were firstly assessed using the eddy covariance (EC) of different vegetation types in the Lancang–Mekong River Basin (LMRB). To fully assess the performances of these four products, spatiotemporal inter-comparisons and literature comparisons were also conducted across different climatic zones. The results are summarized as follows: (1) MOD16 does not perform well as compared to the other three products, with its Root Mean Square Error (RMSE) being higher than GLASS, PML-V2 and BESS, which are approximately 0.47 mm/8-day, 0.66 mm/8-day, and 0.90 mm/8-day, respectively; (2) the performance of each product varies across different vegetation types, and even within the same climate zone. PML-V2 performs best in evergreen broadleaf forests, BESS performs best in deciduous broadleaf forests and croplands, and GLASS performs best in shrubs, grasslands and mixed vegetation; (3) each product can well reflect the spatial difference brought by topography, climate and vegetation over the entire basin but all four ET products do not show either a consistent temporal trend or a uniform spatial distribution; (4) ET ranges of these four products over LMRB are consistent with previous literature in evergreen broadleaf forests, deciduous broadleaf forests, needleleaf forests and mixed forests in other regions with the same climate zones, but they show great differences in croplands, grasslands and shrubs. This study will contribute to improving our understanding of these four ET products in the different climatic zones and vegetation types over LMRB. Full article
(This article belongs to the Special Issue Understanding Biosphere-Atmosphere Interactions with Remote Sensing)
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14 pages, 1831 KiB  
Article
Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
by Olivia Azevedo, Thomas C. Parker, Matthias B. Siewert and Jens-Arne Subke
Remote Sens. 2021, 13(13), 2571; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132571 - 30 Jun 2021
Cited by 6 | Viewed by 3306
Abstract
Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence [...] Read more.
Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e3.508 x, p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis. Full article
(This article belongs to the Special Issue Understanding Biosphere-Atmosphere Interactions with Remote Sensing)
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Other

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20 pages, 2330 KiB  
Technical Note
Remote Sensing of Ecosystem Water Use Efficiency: A Review of Direct and Indirect Estimation Methods
by Wanyuan Cai, Sana Ullah, Lei Yan and Yi Lin
Remote Sens. 2021, 13(12), 2393; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122393 - 18 Jun 2021
Cited by 21 | Viewed by 4445
Abstract
Water use efficiency (WUE) is a key index for understanding the ecosystem of carbon–water coupling. The undistinguishable carbon–water coupling mechanism and uncertainties of indirect methods by remote sensing products and process models render challenges for WUE remote sensing. In this paper, current progress [...] Read more.
Water use efficiency (WUE) is a key index for understanding the ecosystem of carbon–water coupling. The undistinguishable carbon–water coupling mechanism and uncertainties of indirect methods by remote sensing products and process models render challenges for WUE remote sensing. In this paper, current progress in direct and indirect methods of WUE estimation by remote sensing is reviewed. Indirect methods based on gross primary production (GPP)/evapotranspiration (ET) from ground observation, processed models and remote sensing are the main ways to estimate WUE in which carbon and water cycles are independent processes. Various empirical models based on meteorological variables and remote sensed vegetation indices to estimate WUE proved the ability of remotely sensed data for WUE estimating. The analytical model provides a mechanistic opportunity for WUE estimation on an ecosystem scale, while the hypothesis has yet to be validated and applied for the shorter time scales. An optimized response of canopy conductance to atmospheric vapor pressure deficit (VPD) in an analytical model inverted from the conductance model has been also challenged. Partitioning transpiration (T) and evaporation (E) is a more complex phenomenon than that stated in the analytic model and needs a more precise remote sensing retrieval algorithm as well as ground validation, which is an opportunity for remote sensing to extrapolate WUE estimation from sites to a regional scale. Although studies on controlling the mechanism of environmental factors have provided an opportunity to improve WUE remote sensing, the mismatch in the spatial and temporal resolution of meteorological products and remote sensing data, as well as the uncertainty of meteorological reanalysis data, add further challenges. Therefore, improving the remote sensing-based methods of GPP and ET, developing high-quality meteorological forcing datasets and building mechanistic remote sensing models directly acting on carbon–water cycle coupling are possible ways to improve WUE remote sensing. Improvement in direct WUE remote sensing methods or remote sensing-driven ecosystem analysis methods can promote a better understanding of the global ecosystem carbon–water coupling mechanisms and vegetation functions–climate feedbacks to serve for the future global carbon neutrality. Full article
(This article belongs to the Special Issue Understanding Biosphere-Atmosphere Interactions with Remote Sensing)
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