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Special Issue "Ecophysiological Remote Sensing"

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: 30 September 2017

Special Issue Editors

Guest Editor
Dr. John S. Kimball

Numerical Terradynamic Simulation Group, College of Forestry & Conservation, The University of Montana, Missoula, United States
Website | E-Mail
Interests: ecological remote sensing, water and carbon cycle interactions, vegetation phenology, boreal and Arctic ecosystems, remote sensing retrieval algorithms and modeling
Guest Editor
Dr. Kaiyu Guan

Department of Natural Resources and Environmental Sciences, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign Urbana, Illinois, United States
Website | E-Mail
Interests: ecohydrology, crop modeling and forecasting, agriculture adaptation to climate change, vegetation dynamic modeling, terrestrial remote sensing (optical/microwave)

Special Issue Information

Dear Colleagues,

Recent developments in satellite remote sensing include new sensors and/or complimentary observations from different sensors that are providing new insight and capabilities for understanding vegetation and ecosystem properties, dynamics and functional processes. Next generation missions and sensors are now underway or scheduled for near-term operations that may provide new capabilities for better understanding and monitoring of vegetation ecophysiology, including photosynthesis and respiration, canopy phenology, structure, water use, and environmental stress behaviour. Global observations from continuing or similar overlapping satellite missions now span multiple decades and also multiple spectral ranges (visible, near-infrared, thermal, microwave, etc.), enabling more precise documentation and new understanding of vegetation changes and their environmental controls.

In this Special issue on “Ecophysiological Remote Sensing”, we invite papers involving one or more of the following topical areas, emphasizing satellite remote sensing of properties and processes pertaining to terrestrial ecosystems and vegetation; investigations using multi-scale satellite, airborne and ground based observations are also encouraged:

  • New technological developments, including next generation sensors and missions.
  • Fusion of multi-sensor observations, including satellite, and airborne and terrestrial sources.
  • Development and analysis of long time series satellite environmental data records for analysing climate related trends, and anomalies.
  • New application studies, including vegetation phenology and stress, drought detection and monitoring, agriculture, rangeland, and forestry.

Dr. John S. Kimball
Dr. Kaiyu Guan
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 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 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 monthly 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 1600 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

  • Phenology
  • Vegetation
  • Productivity
  • Water stress
  • Canopy structure
  • Disturbance
  • Fluorescence
  • Freeze-thaw
  • Biomass
  • Plant traits
  • Crop yield

Published Papers (5 papers)

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Research

Open AccessArticle Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production
Remote Sens. 2017, 9(9), 914; doi:10.3390/rs9090914
Received: 30 June 2017 / Revised: 22 August 2017 / Accepted: 29 August 2017 / Published: 1 September 2017
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Abstract
Across Eastern Africa, croplands cover 45 million ha. The regional economy is heavily dependent on small holder traditional rain-fed peasant agriculture (up to 90%), which is vulnerable to extreme weather events such as drought and floods that leads to food insecurity. Agricultural production
[...] Read more.
Across Eastern Africa, croplands cover 45 million ha. The regional economy is heavily dependent on small holder traditional rain-fed peasant agriculture (up to 90%), which is vulnerable to extreme weather events such as drought and floods that leads to food insecurity. Agricultural production in the region is moisture limited. Weather station data are scarce and access is limited, while optical satellite data are obscured by heavy clouds limiting their value to study cropland dynamics. Here, we characterized cropland dynamics in Eastern Africa for 2003–2015 using precipitation data from Tropical Rainfall Measuring Mission (TRMM) and a passive microwave dataset of land surface variables that blends data from the Advanced Microwave Scanning Radiometer (AMSR) on the Earth Observing System (AMSR-E) from 2002 to 2011 with data from AMSR2 from 2012 to 2015 with a Chinese microwave radiometer to fill the gap. These time series were analyzed in terms of either cumulative precipitable water vapor-days (CVDs) or cumulative actual evapotranspiration-days (CETaDs), rather than as days of the year. Time series of the land surface variables displayed unimodal seasonality at study sites in Ethiopia and South Sudan, in contrast to bimodality at sites in Tanzania. Interannual moisture variability was at its highest at the beginning of the growing season affecting planting times of crops, while it was lowest at the time of peak moisture. Actual evapotranspiration (ETa) from the simple surface energy balance (SSEB) model was sensitive to track both unimodal and bimodal rainfall patterns. ETa as a function of CETaD was better fitted by a quadratic model (r2 > 0.8) than precipitable water vapor was by CVDs (r2 > 0.6). Moisture time to peak (MTP) for the land surface variables showed strong, logical correspondence among variables (r2 > 0.73). Land surface parameters responded to El Niño-Southern Oscillation and the Indian Ocean Dipole forcings. Area under the curve of the diel difference in vegetation optical depth showed correspondence to crop production and yield data collected by local offices, but not to the data reported at the national scale. A long-term seasonal Mann–Kendall rainfall trend showed a significant decrease for Ethiopia, while the decrement was not significant for Tanzania. While there is significant potential for passive microwave data to augment cropland status and food security monitoring efforts in the region, more research is needed before these data can be used in an operational environment. Full article
(This article belongs to the Special Issue Ecophysiological Remote Sensing)
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Open AccessArticle Global Analysis of Bioclimatic Controls on Ecosystem Productivity Using Satellite Observations of Solar-Induced Chlorophyll Fluorescence
Remote Sens. 2017, 9(6), 530; doi:10.3390/rs9060530
Received: 18 April 2017 / Revised: 16 May 2017 / Accepted: 21 May 2017 / Published: 26 May 2017
PDF Full-text (7493 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Ecosystem productivity models rely on regional climatic information to estimate temperature and moisture constraints influencing plant growth. However, the productivity response to these environmental factors is uncertain at the global scale and has largely been defined using limited observations from sparse monitoring sites,
[...] Read more.
Ecosystem productivity models rely on regional climatic information to estimate temperature and moisture constraints influencing plant growth. However, the productivity response to these environmental factors is uncertain at the global scale and has largely been defined using limited observations from sparse monitoring sites, including carbon flux towers. Recent studies have shown that satellite observations of Solar-Induced chlorophyll Fluorescence (SIF) are highly correlated with ecosystem Gross Primary Productivity (GPP). Here, we use a relatively long-term global SIF observational record from the Global Ozone Monitoring Experiment-2 (GOME-2) sensors to investigate the relationships between SIF, used as a proxy for GPP, and selected bio-climatic factors constraining plant growth at the global scale. We compared the satellite SIF retrievals with collocated GPP observations from a global network of tower carbon flux monitoring sites and surface meteorological data from model reanalysis, including soil moisture, Vapor Pressure Deficit (VPD), and minimum daily air temperature (Tmin). We found strong correspondence (R2 > 80%) between SIF and GPP monthly climatologies for tower sites characterized by mixed, deciduous broadleaf, evergreen needleleaf forests, and croplands. For other land cover types including savanna, shrubland, and evergreen broadleaf forest, SIF showed significant but higher variability in correlations between sites. In order to analyze temperature and moisture related effects on ecosystem productivity, we divided SIF by photosynthetically active radiation (SIFp) and examined partial correlations between SIFp and the climatic factors across a global range of flux tower sites, and over broader regional and global extents. We found that productivity in arid ecosystems is more strongly controlled by soil water content to an extent that soil moisture explains a higher proportion of the seasonal cycle in productivity than VPD. At the global scale, ecosystem productivity is affected by joint climatic constraint factors so that VPD, Tmin, and soil moisture were significant (p < 0.05) controls over 60, 59, and 35 percent of the global domain, respectively. Our study identifies and confirms dominant climate control factors influencing productivity at the global scale indicated from satellite SIF observations. The results are generally consistent with climate response characteristics indicated from sparse global tower observations, while providing more extensive coverage for verifying and refining global carbon and climate model assumptions and predictions. Full article
(This article belongs to the Special Issue Ecophysiological Remote Sensing)
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Open AccessArticle Exploring Relationships among Tree-Ring Growth, Climate Variability, and Seasonal Leaf Activity on Varying Timescales and Spatial Resolutions
Remote Sens. 2017, 9(6), 526; doi:10.3390/rs9060526
Received: 22 February 2017 / Revised: 17 May 2017 / Accepted: 22 May 2017 / Published: 25 May 2017
PDF Full-text (2412 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In the first section of this study, we explored the relationship between ring width index (RWI) and normalized difference vegetation index (NDVI) time series on varying timescales and spatial resolutions, hypothesizing positive associations between RWI and current and previous- year NDVI at 69
[...] Read more.
In the first section of this study, we explored the relationship between ring width index (RWI) and normalized difference vegetation index (NDVI) time series on varying timescales and spatial resolutions, hypothesizing positive associations between RWI and current and previous- year NDVI at 69 forest sites scattered in the Northern Hemisphere. We noted that the relationship between RWI and NDVI varies over space and between tree types (deciduous versus coniferous), bioclimatic zones, cumulative NDVI periods, and spatial resolutions. The high-spatial-resolution NDVI (MODIS) reflected stronger growth patterns than those with coarse-spatial-resolution NDVI (GIMMS3g). In the second section, we explore the link between RWI, climate and NDVI phenological metrics (in place of NDVI) for the same forest sites using random forest models to assess the complicated and nonlinear relationships among them. The results are as following (a) The model using high-spatial-resolution NDVI time series explained a higher proportion of the variance in RWI than that of the model using coarse-spatial-resolution NDVI time series. (b) Amongst all NDVI phenological metrics, summer NDVI sum could best explain RWI followed by the previous year’s summer NDVI sum and the previous year’s spring NDVI sum. (c) We demonstrated the potential of NDVI metrics derived from phenology to improve the existing RWI-climate relationships. However, further research is required to investigate the robustness of the relationship between NDVI and RWI, particularly when more tree-ring data and longer records of the high-spatial-resolution NDVI become available. Full article
(This article belongs to the Special Issue Ecophysiological Remote Sensing)
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Open AccessArticle Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress
Remote Sens. 2017, 9(5), 424; doi:10.3390/rs9050424
Received: 10 March 2017 / Revised: 21 April 2017 / Accepted: 27 April 2017 / Published: 29 April 2017
Cited by 1 | PDF Full-text (2924 KB) | HTML Full-text | XML Full-text
Abstract
Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the
[...] Read more.
Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the spatial-temporal analysis of the fraction of absorbed photosynthetically active radiation (FAPAR). The calculation of daily FAPAR is conducted based on a coupled model consisting of the leaf-canopy radiative transfer model and World Food Study Model (WOFOST). These two models are connected by leaf area index (LAI) and a fraction of diffused incoming solar radiation (SKYL) in the rice growth period. The input parameters of the coupled model are obtained from measured data and GF-1 images. Meanwhile, in order to improve accuracy of FAPAR, the crop growth model is optimized by data assimilation. The validation result shows that the correlation between the simulated FAPAR and the measured data is strong in the rice growth period, with the correlation coefficients being above 7.5 for two areas. The discrepancy of FAPAR between two areas of different stress levels is visualized by spatial-temporal analysis. FAPAR discrepancy starts to appear in the jointing-booting period and experiences a gradual rise, reaching its maximum in the heading-flowering stage. This study suggests that the coupled model, consisting of the leaf-canopy radiative transfer model and the WOFOST model, is able to accurately simulate daily FAPAR during crop growth period and FAPAR can be used as a potential indicator to reflect the impact of heavy metal stress on crop growth. Full article
(This article belongs to the Special Issue Ecophysiological Remote Sensing)
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Open AccessArticle Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data
Remote Sens. 2017, 9(3), 294; doi:10.3390/rs9030294
Received: 31 January 2017 / Revised: 13 March 2017 / Accepted: 14 March 2017 / Published: 21 March 2017
PDF Full-text (5818 KB) | HTML Full-text | XML Full-text
Abstract
Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO2 uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water
[...] Read more.
Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO2 uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water stress. Here, we identify the main biophysical limitations that induce canopy water stress in Sahelian vegetation and evaluate the relationships between field data and Earth observation-derived spectral products for up-scaling GPP. We find that plant-available water and vapor pressure deficit together control the GPP of Sahelian vegetation through their impact on the greening and browning phases. Our results show that a multiple linear regression (MLR) GPP model that combines the enhanced vegetation index, land surface temperature, and the short-wave infrared reflectance (Band 7, 2105–2155 nm) of the moderate-resolution imaging spectroradiometer satellite sensor was able to explain between 88% and 96% of the variability of eddy covariance flux tower GPP at three Sahelian sites (overall = 89%). The MLR GPP model presented here is potentially scalable at a relatively high spatial and temporal resolution. Given the scarcity of field data on CO2 fluxes in the Sahel, this scalability is important due to the low number of flux towers in the region. Full article
(This article belongs to the Special Issue Ecophysiological Remote Sensing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

In-situ and remote sensing platforms for monitoring prescribed fire effects on fine fuel and vegetation cover in Sonoran semidesert grasslands

Steven E. Sesnie1,3, Holly Eagleston1, Lacrecia Johnson1, and Emily Yurcich2

1US Fish and Wildlife Service, Division of Biological Sciences Albuquerque, NM

2Lab of Landscape Ecology and Conservation Biology, Northern Arizona University, Flagstaff AZ

3Corresponding author email: steven_sesnie@fws.gov

Abstract

Prior to Euro-American settlement, fire played a vital role in shaping semidesert plant composition and structure in the southwestern United States. Currently, most semidesert grasslands have undergone significant soil, hydrology, and vegetation alterations from intensive livestock grazing, non-native plant invasion, drought, and near fire exclusion. US Fish and Wildlife Service land management approaches aim to recover historical disturbance regimes and native plant assemblages necessary for maintaining wildlife habitat and other ecosystem values. These activities can benefit from up-to-date fuels and vegetation data for monitoring disturbance impacts and natural resource decisions. The variety of continuously updated satellite remote sensing systems and sensors provide new opportunities for characterizing management outcomes over large landscapes. For this study, we compared photosynthetically active radiation (PAR) ceptometer and leaf area index (LAI) measurements to conventional means for estimating fine-fuel biomass on 20, 50m x 20m plots and 239, 0.5m x 0.5m quadrats on the Buenos Aires National Wildlife Refuge (BANWR) in southern Arizona. Ceptometer LAI explained 75% of the variance in fine fuel biomass using simple linear regression. An additional 8% to 10% of variance was explained from Random Forest regression tree models that included plant height and cover as predictors. Field biomass and vegetation measurements were used to map fine-fuel load and vegetation cover from plots (n = 446) on BANWR comparing outcomes from multi-date Worldview-3 (WV3) and Operational Land Imager (OLI) imagery. Fine-fuel biomass predicted from multi-date WV3 or OLI imagery explained similar variance using regression tree models (54%). Land cover classification from for 11 categories with high spatial resolution WV3 imagery showed 80% overall accuracy and highlighted areas dominated by non-native grasses with 89% class accuracy. Mixed native and non-native grass and shrublands showed 62% accuracy and rare areas dominated by native grasses that were poorly represented on plots showed low class accuracy (20%). A 30-year record of prescribed fire frequency revealed that greater fire frequency since refuge establishment significantly increased non-native grass cover and fuel loads. We recommend that more consistent fire monitoring be used to assess near- and long-term outcomes, and fire be used in conjunction with activities that can help promote desired native plant composition and structure.

Keywords: biomass, disturbance, vegetation

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