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Remote Sensing for Agriculture, Hydrology, and Ecosystems Response to Climatic Variability

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

Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 10351

Special Issue Editors

Prof. Dr. Won-Ho Nam

E-Mail Website
Guest Editor
School of Social Safety and Systems Engineering, Hankyong National University, Anseong, Gyeonggi 17579, Korea
Interests: irrigation and drainage engineering; agricultural drought and water resources management; drought monitoring, mitigation, planning, and policy; risk and vulnerability management; remote sensing for drought monitoring and management; soil moisture and hydrologic/watershed modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiang Zhang

E-Mail Website
Guest Editor
National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Lumo Road 388, Hongshan District, Wuhan, China
Interests: drought disaster monitoring and analysis; geospatial sensor web theory, methods and applications; smart city technology; sustainable development goals (SDGs)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climatic variability and trends in agricultural processes affect global crop yields, agricultural productivity, and hydrology circulation. Meanwhile, ecosystems are rapidly changing in response to climate change and other global change drivers, not only in response to temperature changes but also associated changes in precipitation, atmospheric carbon dioxide concentration, water balance, ocean chemistry, and the frequency and magnitude of extreme events. Climate change affects individual species and the way they interact with other organisms and their habitats, which alters the structure and function of ecosystems and the goods and services that natural systems provide to society. Understanding the direction and magnitude of ecological responses allows human communities to better anticipate these changes and adapt as necessary. Moving forward, evaluations of effectiveness and demonstrative case studies of adaptation success stories are needed to promote agricultural, hydrological, and ecosystems management.

In recent years, remote sensing (RS)-based data and models have provided efficient tools for agriculture, hydrology, and water resources monitoring and modeling. However, estimating ecosystems’ responses to climatic variability and impacts on agricultural processes is still challenging because of the complex interactions among hydrological processes, agricultural water management, biodiversity, and ecosystems. We believe that remote sensing data, models, and methods will play critical roles in addressing these challenges.

This Special Issue will focus on “Remote Sensing for Agriculture, Hydrology, and Ecosystems Response to Climatic Variability”. We welcome novel research, review, and opinion pieces covering all related topics, including climatic variability, climate change, extreme events, water resources, soil moisture, hydrology, ecosystems, agriculture, evapotranspiration, crop yield, drought, irrigation water management, case studies from the field, and policy positions.

This Special Issue is inviting manuscripts on the following topics:

  • RS-based ecosystem modeling;
  • RS-based crop evapotranspiration modeling;
  • RS-based agro-hydrological modeling;
  • RS-based assessments of crop water productivity and crop yield;
  • RS-based drought assessment modeling;
  • RS-based optimization of irrigation water and crop planting pattern;
  • RS-based impact assessment of irrigation practices on ecosystems and environment;
  • Other topics related to RS-based hydrology and water resources modeling.

Prof. Dr. Won-Ho Nam
Prof. Dr. Xiang Zhang
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

  • remote sensing
  • climatic variability
  • climate change
  • hydrology
  • ecosystems
  • agriculture
  • evapotranspiration
  • crop yield
  • drought
  • water resources

Published Papers (5 papers)

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Research

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16 pages, 11157 KiB  
Article
Distance to a River Modifies Climate Legacy on Vegetation Growth in a Boreal Riparian Forest
by Yingyu Li, Qiaoqi Sun, Hongfei Zou and Petra Marschner
Remote Sens. 2023, 15(23), 5582; https://doi.org/10.3390/rs15235582 - 30 Nov 2023
Viewed by 675
Abstract
Inter-annual variability in growing season temperature and precipitation, together with snow coverage duration, determine vegetation growth in boreal ecosystems. However, little is known about the impact of concurrent and antecedent climate, particularly snow cover duration, on vegetation growth in a boreal riparian forest. [...] Read more.
Inter-annual variability in growing season temperature and precipitation, together with snow coverage duration, determine vegetation growth in boreal ecosystems. However, little is known about the impact of concurrent and antecedent climate, particularly snow cover duration, on vegetation growth in a boreal riparian forest. Additionally, significant uncertainty exists regarding whether the distance to a river (as a proxy of groundwater availability) further modifies these climatic legacy effects on vegetation growth. To fill this knowledge gap, we quantified the responses of different vegetation types (shrub, deciduous coniferous and broadleaf forests) to concurrent and antecedent climate variables in a boreal riparian forest, and further determined the magnitude and duration of climate legacies in relation to distance to a river, using MODIS-derived NDVI time series with gridded climate data from 2001 to 2020. Results showed that higher temperature and precipitation and longer snow cover duration increased vegetation growth. For deciduous coniferous forests and broadleaf forests, the duration of temperature legacy was about one year, precipitation legacy about two years and snow cover duration legacy was 3 to 4 years. Further, distance to a river modified the concurrent and antecedent temperature and snow cover duration legacy effects on vegetation growth, but not that of precipitation. Specifically, temperature and snow cover duration legacies were shorter at the sites near a river compared to sites at greater distance to a river. Our research highlights the importance of snow cover duration on vegetation growth and that closeness to a river can buffer adverse climate impacts by shortening the strength and duration of climate legacies in a boreal riparian forest. Full article
(This article belongs to the Special Issue Remote Sensing for Agriculture, Hydrology, and Ecosystems Response to Climatic Variability)
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19 pages, 3708 KiB  
Article
Multiscale Variability of Hydrological Responses in Urbanizing Watershed
by Urmila R. Panikkar, Roshan Srivastav and Ankur Srivastava
Remote Sens. 2023, 15(3), 796; https://doi.org/10.3390/rs15030796 - 31 Jan 2023
Cited by 1 | Viewed by 1610
Abstract
Anthropically-induced land-use/land cover (LULC) changes create an imbalance between water and energy fluxes by affecting rainfall-runoff partitioning. This alters the catchment’s flow regime, generating increased highs and reduced low flows, triggering socio-economic and environmental damages. The focus of this study is two-fold (i) [...] Read more.
Anthropically-induced land-use/land cover (LULC) changes create an imbalance between water and energy fluxes by affecting rainfall-runoff partitioning. This alters the catchment’s flow regime, generating increased highs and reduced low flows, triggering socio-economic and environmental damages. The focus of this study is two-fold (i) to quantify the hydrological changes induced in the urbanizing watershed and (ii) to analyze changes in streamflow variability and generation of extremes (high- and low-flow), using the soil and water assessment tool (SWAT) for Peachtree Creek, USA. The results indicate that the change in LULC significantly influences the availability of soil moisture, ET, and contribution to groundwater flow. It is observed that the variations in these processes regulate the water availability from the surface and sub-surface sources, thus affecting the generation of extreme flows. The spatio-temporal analysis, in response to LULC changes, indicates that (i) urbanization significantly affects baseflow, and its variability depends on the degree of urbanization and the predominant land-use class of the subwatersheds, and (ii) the seasonal variations in the baseflow contribution to the streams depend on ET and the timing and magnitude of groundwater outflow to streams. These variations in ET and groundwater lead to water excess/deficit regions, thus increasing the susceptibility to floods during heavy precipitation events and reducing the reliability of streams during dry periods. Thus, in an urbanizing watershed, the hydrological regime of the watershed may not always be a function of changes in the surface runoff, but will be modified by ET and groundwater dynamics. Further, the study shows that the changes in model parameters can provide insight into the implications of LULC changes on hydrological processes and flow regimes. Evaluating the implications on the basin water balance is paramount for deriving any management operations and restoration activities. The study also outlines the significance of analyzing the spatial and temporal scale streamflow variations for managing water resources to reduce damage to lives and properties. Full article
(This article belongs to the Special Issue Remote Sensing for Agriculture, Hydrology, and Ecosystems Response to Climatic Variability)
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20 pages, 11689 KiB  
Article
Detecting Spatially Non-Stationary between Vegetation and Related Factors in the Yellow River Basin from 1986 to 2021 Using Multiscale Geographically Weighted Regression Based on Landsat
by Xiaolei Wang, Shouhai Shi, Xue Zhao, Zirong Hu, Mei Hou and Lei Xu
Remote Sens. 2022, 14(24), 6276; https://doi.org/10.3390/rs14246276 - 11 Dec 2022
Cited by 7 | Viewed by 1323
Abstract
As an important ecological barrier in northern China, the ecological environment of the Yellow River Basin (YRB) has been greatly improved in recent decades. However, due to spatially non-stationarity, the contribution of human activities and natural factors to vegetation restoration may exhibit different [...] Read more.
As an important ecological barrier in northern China, the ecological environment of the Yellow River Basin (YRB) has been greatly improved in recent decades. However, due to spatially non-stationarity, the contribution of human activities and natural factors to vegetation restoration may exhibit different coupling effects in various areas. In this paper, the Normalized Difference Vegetation Index (NDVI) of the YRB from 1986 to 2021 was used as the dependent variable, and terrain, meteorological, and socioeconomic factors were used as independent variables. With the help of Multiscale Geographically Weighted Regression (MGWR), which could handle the scale difference well, combined with Ordinary Least Squares (OLS) and traditional Geographically Weighted Regression (GWR), the spatial non-stationary relationship between vegetation and related factors was discussed. The results showed that: (1) The vegetation was subject to fluctuating changes from 1986 to 2021, mainly improving, with a growth rate of 0.0018/year; the spatial distribution pattern of vegetation in the basin was high in the southeast and low in the northwest. (2) Compared with the OLS and GWR, the MGWR could better explain the relationship between vegetation and various factors. (3) The response scale of vegetation and related factors was significantly variant, and this scale changed with time. The effect scale of terrain factor is lower than climate and social factors. (4) There was obvious spatial heterogeneity in the effects of various influencing factors on vegetation. The vegetation of the upstream was mainly positively affected by mean annual temperature (coefficients ∈ [1.507, 1.784]); while potential evapotranspiration was the dominant factor of vegetation in the middle and lower reaches of the basin (coefficients ∈ [−1.724, −1.704]); it was worth noting that the influence of social factors on vegetation was relatively small. This study deeply explores the spatial non-stationarity of vegetation and various related factors, thereby revealing the evolution law of vegetation pattern and providing scientific support for monitoring and improving the ecological environment quality of the YRB. Full article
(This article belongs to the Special Issue Remote Sensing for Agriculture, Hydrology, and Ecosystems Response to Climatic Variability)
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Review

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25 pages, 5959 KiB  
Review
Application of Remote Sensing in Detecting and Monitoring Water Stress in Forests
by Thai Son Le, Richard Harper and Bernard Dell
Remote Sens. 2023, 15(13), 3360; https://doi.org/10.3390/rs15133360 - 30 Jun 2023
Cited by 7 | Viewed by 4117
Abstract
In the context of climate change, the occurrence of water stress in forest ecosystems, which are solely dependent on precipitation, has exhibited a rising trend, even among species that are typically regarded as drought-tolerant. Remote sensing techniques offer an efficient, comprehensive, and timely [...] Read more.
In the context of climate change, the occurrence of water stress in forest ecosystems, which are solely dependent on precipitation, has exhibited a rising trend, even among species that are typically regarded as drought-tolerant. Remote sensing techniques offer an efficient, comprehensive, and timely approach for monitoring forests at local and regional scales. These techniques also enable the development of diverse indicators of plant water status, which can play a critical role in evaluating forest water stress. This review aims to provide an overview of remote sensing applications for monitoring water stress in forests and reveal the potential of remote sensing and geographic information system applications in monitoring water stress for effective forest resource management. It examines the principles and significance of utilizing remote sensing technologies to detect forest stress caused by water deficit. In addition, by a quantitative assessment of remote sensing applications of studies in refereed publications, the review highlights the overall trends and the value of the widely used approach of utilizing visible and near-infrared reflectance data from satellite imagery, in conjunction with classical vegetation indices. Promising areas for future research include the utilization of more adaptable platforms and higher-resolution spectral data, the development of novel remote sensing indices with enhanced sensitivity to forest water stress, and the implementation of modelling techniques for early detection and prediction of stress. Full article
(This article belongs to the Special Issue Remote Sensing for Agriculture, Hydrology, and Ecosystems Response to Climatic Variability)
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Other

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15 pages, 3040 KiB  
Technical Note
Going Back to Grassland? Assessing the Impact of Groundwater Decline on Irrigated Agriculture Using Remote Sensing Data
by Haoying Wang
Remote Sens. 2023, 15(6), 1698; https://doi.org/10.3390/rs15061698 - 21 Mar 2023
Cited by 1 | Viewed by 1240
Abstract
Climate change has increased agricultural drought risk in arid/semi-arid regions globally. One of the common adaptation strategies is shifting to more drought-tolerant crops or switching back to grassland permanently. In many drought-prone areas, groundwater dynamics play a critical role in agricultural production and [...] Read more.
Climate change has increased agricultural drought risk in arid/semi-arid regions globally. One of the common adaptation strategies is shifting to more drought-tolerant crops or switching back to grassland permanently. In many drought-prone areas, groundwater dynamics play a critical role in agricultural production and drought management. This study aims to help understand how groundwater level decline affects the propensity of cropland switching back to grassland. Taking Union County of New Mexico (US) as a case study, field-scale groundwater level projections and high-resolution remote sensing data on crop choices are integrated to explore the impact of groundwater level decline in a regression analysis framework. The results show that cropland has been slowly but permanently switching back to grassland as the groundwater level in the Ogallala Aquifer continues to decline in the area. Specifically, for a one-standard-deviation decline in groundwater level (36.95 feet or 11.26 m), the average likelihood of switching back to grassland increases by 1.85% (the 95% confidence interval is [0.07%, 3.58%]). The findings account for the fact that farmers usually explore other options (such as more drought-tolerant crops, land idling, and rotation) before switching back to grassland permanently. The paper concludes by exploring relevant policy implications for land (soil) and water conservation in the long run. Full article
(This article belongs to the Special Issue Remote Sensing for Agriculture, Hydrology, and Ecosystems Response to Climatic Variability)
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