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Remote Sensing of Soil Moisture and the Dynamics of Soil–Vegetation...
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Remote Sensing of Soil Moisture and the Dynamics of Soil–Vegetation Systems

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 April 2024 | Viewed by 7183

Special Issue Editors

Dr. Ankur Srivastava

E-Mail Website
Guest Editor
UTS · Faculty of Science, University of Technology Sydney, Sydney, Australia
Interests: hydrology; ecohydrology; water resource management; geomorphology; remote sensing
Dr. Mauro Holzman

E-Mail Website
Guest Editor
1. Instituto de Hidrología de Llanuras “Dr. Eduardo J. Usunoff”, Buenos Aires, Argentina
2. Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: remote sensing applied to hydrology; evapotranspiration; soil moisture
Dr. Facundo Carmona

E-Mail Website
Guest Editor
Instituto de Hidrología de Llanuras Dr. Eduardo J. Usunoff, Universidad Nacional del Centro de la Provincia de Buenos Aires, Pinto 399, Tandil 7000, Argentina
Interests: evapotranspiration; crop; precision agriculture; environment surface hydrology; soil and water conservation; remote sensing; landsat; climatology; hydrology

Special Issue Information

Dear Colleagues,

Soil moisture is a crucial factor influencing the water cycle and vegetation dynamics, especially in arid and semiarid ecosystems and rainfed crops, where hydric conditions determine much of the vegetation growth. The monitoring of this variable is key to understanding vegetation productivity and phenology, the impacts of climatic variability on vegetation and carbon uptake, among others. During the last several decades, significant progress has been made in estimating water availability for vegetation. Microwave bands can retrieve soil water content, while other methods that use thermal and/or reflectance data are more associated to evapotranspiration or vegetation condition. Despite these significant advances, it is still necessary to understand processes at different spatial and temporal scales that determine the vegetation water condition and dynamics. In this sense, although geostationary satellites have mostly been used in the past for meteorological studies, they have the capability to make significant contributions to soil–vegetation system monitoring.

This Special Issue aims to publish studies covering different uses of remote sensing data, not only for the estimation of soil moisture but also exploring how this variable determines the dynamics of vegetation. Topics may include anything from the estimation of vegetation productivity in agricultural lands to more comprehensive aims and scales. Thus, multisource data integration (e.g., multispectral, thermal and microwave), multiscale approaches, among other issues, are welcome. We also welcome the submission of manuscripts that investigate the developments and applications of data products from geostationary satellites and their potential combined use with polar orbiting ones and other types of sensors for advanced monitoring processes of the soil–vegetation interface.

Articles may address, but are not limited to, the following topics:

  • Spatial monitoring of soil moisture in soil profile;
  • Multispectral water vegetation indices;
  • Polar and geostationary satellites combination for assessing vegetation dynamics and phenology;
  • Impact of soil moisture in vegetation productivity;
  • Multisensor data fusion techniques for vegetation water deficit assessment;
  • Tracking long- and short-term trends in soil moisture and vegetation response;
  • Exploring the interactions between vegetation and climatic-hydrological forcings. 

Dr. Ankur Srivastava
Dr. Mauro Ezequiel Holzman
Dr. Facundo Carmona
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

  • vegetation water stress
  • surface and sub-surface soil moisture
  • solar-thermal spectra
  • evapotranspiration
  • arid and semi-arid ecosystems
  • vegetation productivity
  • water deficit in agricultural lands

Published Papers (3 papers)

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Research

24 pages, 8797 KiB  
Article
Response of Vegetation to Drought in the Source Region of the Yangtze and Yellow Rivers Based on Causal Analysis
by Jie Lu, Tianling Qin, Denghua Yan, Xizhi Lv, Zhe Yuan, Jie Wen, Shu Xu, Yuhui Yang, Jianming Feng and Wei Li
Remote Sens. 2024, 16(4), 630; https://doi.org/10.3390/rs16040630 - 08 Feb 2024
Viewed by 693
Abstract
The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought–vegetation [...] Read more.
The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought–vegetation interaction in this area under the influence of climate–permafrost coupling. In this study, based on the saturated vapor pressure deficit (VPD) and soil moisture (SM) that characterize atmospheric and soil drought, as well as the Normalized Differential Vegetation Index (NDVI) and solar-induced fluorescence (SIF) that characterize vegetation greenness and function, the evolution of regional vegetation productivity and drought were systematically identified. On this basis, the technical advantages of the causal discovery algorithm Peter–Clark Momentary Conditional Independence (PCMCI) were applied to distinguish the response of vegetation to VPD and SM. Furthermore, this study delves into the response mechanisms of NDVI and SIF to atmospheric and soil drought, considering different vegetation types and permafrost degradation areas. The findings indicated that low SM and high VPD were the limiting factors for vegetation growth. The positive and negative causal effects of VPD on NDVI accounted for 47.88% and 52.12% of the total area, respectively. Shrubs were the most sensitive to SM, and the response speed of grassland to SM was faster than that of forest land. The impact of SM on vegetation in the SRYY was stronger than that of VPD, and the effect in the frozen soil degradation area was more obvious. The average causal effects of NDVI and SIF on SM in the frozen soil degradation area were 0.21 and 0.41, respectively, which were twice as high as those in the whole area, and SM dominated NDVI (SIF) changes in 62.87% (76.60%) of the frozen soil degradation area. The research results can provide important scientific basis and theoretical support for the scientific assessment and adaptation of permafrost, vegetation, and climate change in the source area and provide reference for ecological protection in permafrost regions. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Moisture and the Dynamics of Soil–Vegetation Systems)
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19 pages, 5392 KiB  
Article
Assessment of Irrigation Demands Based on Soil Moisture Deficits Using a Satellite-Based Hydrological Model
by Kallem Sushanth, Abhijit Behera, Ashok Mishra and Rajendra Singh
Remote Sens. 2023, 15(4), 1119; https://doi.org/10.3390/rs15041119 - 18 Feb 2023
Cited by 3 | Viewed by 1949
Abstract
Soil moisture deficit is an essential element in the estimation of irrigation demands, both spatially and temporarily. The determination of temporal and spatial variations of soil moisture in a river basin is challenging in many aspects; however, distributed hydrological modelling with remote sensing [...] Read more.
Soil moisture deficit is an essential element in the estimation of irrigation demands, both spatially and temporarily. The determination of temporal and spatial variations of soil moisture in a river basin is challenging in many aspects; however, distributed hydrological modelling with remote sensing inputs is an effective way to determine soil moisture. In this research, a water demand module was developed for a satellite-based National Hydrological Model—India (NHM-I) to estimate distributed irrigation demands based on soil moisture deficits. The NHM-I is a conceptual distributed model that was explicitly developed to utilize the products from remote sensing satellites. MOD13Q1.5 data were used in this study to classify paddy and irrigated dry crops. Along with the above data, the DEM, Leaf Area Index, FAO soil map, and crop characteristics data were also used as inputs. The NHM-I with water demand module was evaluated in the Damodar river basin, India, from 2009 to 2018. The integrated NHM-I model simulated the irrigation demands effectively with remote sensing data. The temporal analysis reveals that soil moisture deficits in the Kharif season varied annually from 2009 to 2018; however, soil moisture deficits in the Rabi season were almost constant. The 50% Allowable Moisture Depletion (AMD-50) scenario can reduce the irrigation demand of 1966 MCM compared to the Zero Allowable Moisture Depletion (AMD-0) scenario. The highest annual irrigation demand (8923 MCM) under the AMD-50 scenario occurred in the 2015–2016 season, while the lowest (6344 MCM) happened in 2013–2014 season. With a new water demand module and remote sensing inputs, the NHM-I will provide a platform to assess spatial and temporal irrigation demands and soil moisture. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Moisture and the Dynamics of Soil–Vegetation Systems)
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30 pages, 14330 KiB  
Article
Spatial Downscaling and Gap-Filling of SMAP Soil Moisture to High Resolution Using MODIS Surface Variables and Machine Learning Approaches over ShanDian River Basin, China
by Adeel Ahmad Nadeem, Yuanyuan Zha, Liangsheng Shi, Shoaib Ali, Xi Wang, Zeeshan Zafar, Zeeshan Afzal and Muhammad Atiq Ur Rehman Tariq
Remote Sens. 2023, 15(3), 812; https://doi.org/10.3390/rs15030812 - 31 Jan 2023
Cited by 9 | Viewed by 3127
Abstract
High-resolution soil moisture (SM) information is essential for regional to global hydrological and agricultural applications. The Soil Moisture Active Passive (SMAP) offers daily global composites of SM at coarse-resolution 9 and 36 km, with data gaps limiting its local application to depict SM [...] Read more.
High-resolution soil moisture (SM) information is essential for regional to global hydrological and agricultural applications. The Soil Moisture Active Passive (SMAP) offers daily global composites of SM at coarse-resolution 9 and 36 km, with data gaps limiting its local application to depict SM distribution in detail. To overcome the aforementioned problem, a downscaling and gap-filling novel approach was adopted, using random forest (RF) and artificial neural network (ANN) algorithms to downscale SMAP SM data, using land-surface variables from moderate-resolution imaging spectroradiometer (MODIS) onboard Aqua and Terra satellites from the years 2018 to 2019. Firstly, four combinations (RF+Aqua, RF+Terra, ANN+Aqua, and ANN+Terra) were developed. Each combination downscaled SMAP SM at a high resolution (1 km). These combinations were evaluated by using error matrices and in situ SM at different scales in the ShanDian River (SDR) Basin. The combination RF+Terra showed a better performance, with a low averaged unbiased root mean square error (ubRMSE) of 0.034 m3/m3 and high averaged correlation (R) of 0.54 against the small-, medium-, and large-scale in situ SM. Secondly, the impact of various land covers was examined by using downscaled SMAP and in situ SM. Vegetation attenuation makes woodland more error-prone and less correlated than grassland and farmland. Finally, the RF+Terra and ANN+Terra combinations were selected for their higher accuracy in gap filling of downscaled SMAP SM. The gap-filled downscaled SMAP SM results were compared spatially with China Land Data Assimilation System (CLDAS) SM and in situ SM. The RF+Terra combination outcomes were more humid than ANN+Terra combination results in the SDR basin. Overall, the RF+Terra combination gap-filled data showed high R (0.40) and less ubRMSE (0.064 m3/m3) against in situ SM, which was close to CLDAS SM. This study showed that the proposed RF- and ANN-based downscaling methods have a potential to improve the spatial resolution and gap-filling of SMAP SM at a high resolution (1 km). Full article
(This article belongs to the Special Issue Remote Sensing of Soil Moisture and the Dynamics of Soil–Vegetation Systems)
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