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Ecohydrological Remote Sensing
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Ecohydrological 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 (31 May 2021) | Viewed by 30613

Special Issue Editors

Dr. Monica Garcia

E-Mail Website
Guest Editor
Department of Environmental Engineering, Denmark Technical University, 2100 Lyngby, Denmark
Interests: thermal and optical remote sensing; land surface fluxes; dryland ecosystems; Unmanned Aerial Systems
Prof. Pierre Gentine

E-Mail Website
Guest Editor
Department of Earth and Environmental Engineering, Columbia University, 500 W 120th st, New York, NY 10027, USA
Interests: microwave remote sensing and solar-induced fluorescence; droughts; land-atmosphere interactions

Special Issue Information

Dear Colleagues,

The current intensification of the water cycle under climate change with more frequent and more instense extreme hydrological events, e.g., droughts, is putting increased pressure on natural and agricultural ecosystems, water managers, and governments to mitigate and adapt. However, the precise impact on ecosystems remains largely unknown, partly due to knowledge gaps on the joint regulation of water and carbon fluxes as well as potential lags in memory between the different processes at play, which vary with biomes and climate types.

Thus, advancing the use of remote sensing to assess the traits and factors controlling ecosystem responses to hydro-climatic conditions at different spatial and temporal scales is essential. The development of real-time monitoring systems of ecohydrological variables like evapotranspiration, gross primary productivity, net ecosystem excahnge, or crop yields can help to inform policy decisions and conduct national and international action, especially in regions with scarce ground observations.

The aim of this Special Issue is to investigate functional relationships between hydrology and ecology at multiple spatial and temporal scales using data from land and atmosphere remote-sensing missions to advance the ecohydrological monitoring of terrestrial ecosystems.

In particular, but not exclusively, manuscripts are encouraged addressing the following topics using remote sensing from satellite, airborne, or unmanned missions (optical, hyperspectral, thermal, fluorescence, radar, passive microwaves, LiDAR, or sounders, e.g., AIRS, Calipso):

  • The resilience of ecosystems’ fluxes to droughts and heat waves or their combination.
  • Vegetation–atmosphere interactions: responses to soil mositure vs. vapor pressure deficits, atmospheric pollutants and aerosol loadings, radiation or precipitation response and feedback.
  • Carbon and water footprints of dryland and irrigated crops at regional scales.
  • Remote-sensing analysis of plant hydraulic and water traits to better understand and model drought responses.
  • Effects of land use/land cover changes on various components of the hydrological cycle such as surface runoff, recharge, or feedback to climate.
  • Novel approaches to estimate vegetation status and functions based on statistical analysis including machine learning, combinations of data-driven and mechanistic models, plant hydraulics, or surface energy balance approaches.
  • Meso and microscale landscape heterogeneity to advance the transfer of schemes across scales (e.g., aerodynamic and canopy resistances) or to provide effective community level descriptions alternatives to plant functional types (PFT).

Target variables include, but are not limited to, the following: evapotranspiration and its partitioning in transpiration and evaporation, leaf and canopy energy-budgets, photosynthesis, net ecosystem exchange, biomass, root zone soil moisture, water use efficiency, hydraulic traits such stomatal conductance, hydraulic resistance, or canopy water potential proxies

Dr. Monica Garcia
Prof. Pierre Gentine
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

  • Ecosystem resilience
  • Water intensification
  • Droughts
  • Aerial and satellite remote sensing
  • Aridity
  • Soil moisture
  • Heat waves
  • Traits

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Published Papers (6 papers)

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Research

22 pages, 4997 KiB  
Article
Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application
by Hongxiao Jin, Christian Josef Köppl, Benjamin M. C. Fischer, Johanna Rojas-Conejo, Mark S. Johnson, Laura Morillas, Steve W. Lyon, Ana M. Durán-Quesada, Andrea Suárez-Serrano, Stefano Manzoni and Monica Garcia
Remote Sens. 2021, 13(10), 1866; https://doi.org/10.3390/rs13101866 - 11 May 2021
Cited by 12 | Viewed by 5976
Abstract
Miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and [...] Read more.
Miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and water use efficiency (WUE) after biochar application in Costa Rica. The field flights were conducted over two experimental groups with bamboo biochar (BC1) and sugarcane biochar (BC2) amendments and one control (C) group without biochar application. Rice canopy biophysical variables were estimated by inverting a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary productivity (GPP) and WUE across treatments were estimated using light-use efficiency and WUE models respectively from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration rate. We found that GPP was increased by 41.9 ± 3.4% in BC1 and 17.5 ± 3.4% in BC2 versus C, which may be explained by higher soil moisture after biochar application, and consequently significantly higher WUEs by 40.8 ± 3.5% in BC1 and 13.4 ± 3.5% in BC2 compared to C. This study demonstrated the use of hyperspectral and thermal imagery from a drone to quantify biochar effects on dry cropland by integrating ground measurements and physical models. Full article
(This article belongs to the Special Issue Ecohydrological Remote Sensing)
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20 pages, 4222 KiB  
Article
UAV-Based Estimate of Snow Cover Dynamics: Optimizing Semi-Arid Forest Structure for Snow Persistence
by Adam Belmonte, Temuulen Sankey, Joel Biederman, John Bradford, Scott Goetz and Thomas Kolb
Remote Sens. 2021, 13(5), 1036; https://doi.org/10.3390/rs13051036 - 9 Mar 2021
Cited by 11 | Viewed by 4061
Abstract
Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely [...] Read more.
Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health. Full article
(This article belongs to the Special Issue Ecohydrological Remote Sensing)
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32 pages, 5195 KiB  
Article
Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought
by Verónica Sobejano-Paz, Teis Nørgaard Mikkelsen, Andreas Baum, Xingguo Mo, Suxia Liu, Christian Josef Köppl, Mark S. Johnson, Lorant Gulyas and Mónica García
Remote Sens. 2020, 12(19), 3182; https://doi.org/10.3390/rs12193182 - 29 Sep 2020
Cited by 51 | Viewed by 7505
Abstract
During water stress, crops undertake adjustments in functional, structural, and biochemical traits. Hyperspectral data and machine learning techniques (PLS-R) can be used to assess water stress responses in plant physiology. In this study, we investigated the potential of hyperspectral optical (VNIR) measurements supplemented [...] Read more.
During water stress, crops undertake adjustments in functional, structural, and biochemical traits. Hyperspectral data and machine learning techniques (PLS-R) can be used to assess water stress responses in plant physiology. In this study, we investigated the potential of hyperspectral optical (VNIR) measurements supplemented with thermal remote sensing and canopy height (hc) to detect changes in leaf physiology of soybean (C3) and maize (C4) plants under three levels of soil moisture in controlled environmental conditions. We measured canopy evapotranspiration (ET), leaf transpiration (Tr), leaf stomatal conductance (gs), leaf photosynthesis (A), leaf chlorophyll content and morphological properties (hc and LAI), as well as vegetation cover reflectance and radiometric temperature (TL,Rad). Our results showed that water stress caused significant ET decreases in both crops. This reduction was linked to tighter stomatal control for soybean plants, whereas LAI changes were the primary control on maize ET. Spectral vegetation indices (VIs) and TL,Rad were able to track these different responses to drought, but only after controlling for confounding changes in phenology. PLS-R modeling of gs, Tr, and A using hyperspectral data was more accurate when pooling data from both crops together rather than individually. Nonetheless, separated PLS-R crop models are useful to identify the most relevant variables in each crop such as TL,Rad for soybean and hc for maize under our experimental conditions. Interestingly, the most important spectral bands sensitive to drought, derived from PLS-R analysis, were not exactly centered at the same wavelengths of the studied VIs sensitive to drought, highlighting the benefit of having contiguous narrow spectral bands to predict leaf physiology and suggesting different wavelength combinations based on crop type. Our results are only a first but a promising step towards larger scale remote sensing applications (e.g., airborne and satellite). PLS-R estimates of leaf physiology could help to parameterize canopy level GPP or ET models and to identify different photosynthetic paths or the degree of stomatal closure in response to drought. Full article
(This article belongs to the Special Issue Ecohydrological Remote Sensing)
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21 pages, 7580 KiB  
Article
Discrepancies in the Simulated Global Terrestrial Latent Heat Flux from GLASS and MERRA-2 Surface Net Radiation Products
by Xiaozheng Guo, Yunjun Yao, Yuhu Zhang, Yi Lin, Bo Jiang, Kun Jia, Xiaotong Zhang, Xianhong Xie, Lilin Zhang, Ke Shang, Junming Yang and Xiangyi Bei
Remote Sens. 2020, 12(17), 2763; https://doi.org/10.3390/rs12172763 - 26 Aug 2020
Cited by 10 | Viewed by 3149
Abstract
Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two [...] Read more.
Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE. Full article
(This article belongs to the Special Issue Ecohydrological Remote Sensing)
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21 pages, 23853 KiB  
Article
Remote Sensing-Informed Zonation for Understanding Snow, Plant and Soil Moisture Dynamics within a Mountain Ecosystem
by Jashvina Devadoss, Nicola Falco, Baptiste Dafflon, Yuxin Wu, Maya Franklin, Anna Hermes, Eve-Lyn S. Hinckley and Haruko Wainwright
Remote Sens. 2020, 12(17), 2733; https://doi.org/10.3390/rs12172733 - 24 Aug 2020
Cited by 19 | Viewed by 4079
Abstract
In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, [...] Read more.
In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future. Full article
(This article belongs to the Special Issue Ecohydrological Remote Sensing)
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18 pages, 5790 KiB  
Article
Droughts Amplify Differences Between the Energy Balance Components of Amazon Forests and Croplands
by Charles Caioni, Divino Vicente Silvério, Marcia N. Macedo, Michael T. Coe and Paulo M. Brando
Remote Sens. 2020, 12(3), 525; https://doi.org/10.3390/rs12030525 - 6 Feb 2020
Cited by 20 | Viewed by 4420
Abstract
Droughts can exert a strong influence on the regional energy balance of the Amazon and Cerrado, as can the replacement of native vegetation by croplands. What remains unclear is how these two forcing factors interact and whether land cover changes fundamentally alter the [...] Read more.
Droughts can exert a strong influence on the regional energy balance of the Amazon and Cerrado, as can the replacement of native vegetation by croplands. What remains unclear is how these two forcing factors interact and whether land cover changes fundamentally alter the sensitivity of the energy balance components to drought events. To fill this gap, we used remote sensing data to evaluate the impacts of drought on evapotranspiration (ET), land surface temperature (LST), and albedo on cultivated areas, savannas, and forests. Our results (for seasonal drought) indicate that increases in monthly dryness across Mato Grosso state (southern Amazonia and northern Cerrado) drive greater increases in LST and albedo in croplands than in forests. Furthermore, during the 2007 and 2010 droughts, croplands became hotter (0.1–0.8 °C) than savannas (0.3–0.6 °C) and forests (0.2–0.3 °C). However, forest ET was consistently higher than ET in all other land uses. This finding likely indicates that forests can access deeper soil water during droughts. Overall, our findings suggest that forest remnants can play a fundamental role in the mitigation of the negative impacts of extreme drought events, contributing to a higher ET and lower LST. Full article
(This article belongs to the Special Issue Ecohydrological Remote Sensing)
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