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Advances in Scaling and Modelling of Essential Variables for Environmental...
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Advances in Scaling and Modelling of Essential Variables for Environmental Monitoring with Multiscale Earth Observations

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 March 2023) | Viewed by 6206

Special Issue Editors

Dr. Antonino Maltese

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, University of Palermo, 90128 Palermo, Italy
Interests: remote sensing; GIS; geomatics
Special Issues, Collections and Topics in MDPI journals
Dr. Yijian Zeng

E-Mail Website1 Website2
Guest Editor
Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
Interests: soil-water-plant-energy interactions; land-atmosphere interactions; soil moisture; earth observation; climate data records; data assimilation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Peng

E-Mail Website
Guest Editor
Helmholtz Centre for Environmental Research—UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Interests: quantitative retrieval of land surface parameters from remote sensing data; radiative transfer in soil–vegetation–atmosphere systems; process-based modeling and data-driven methods; hydroclimatic extremes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although Earth Observation (EO) satellite technology has improved our ability to characterize and manage ecosystems both in space and in time, there are many emerging technologies and research areas where the potential of Earth Observation has not been fully exploited.

Essential variables for environmental monitoring include geophysical variables of water, energy, and carbon cycles. Many water–energy–vegetation satellite products are currently available as down streaming services. However, these are at coarse resolutions and not suitable to characterize the environment at field or finer scale. On the other hand, although proximity sensing can provide very high resolution products, they require considerable efforts for cal/val procedures (e.g., geometric and radiometric calibrations in both labs and fields). Furthermore, the intercomparison between high resolution and coarser resolution products require either downscaling or upscaling methodologies.

Key questions are “How are these downscaling/upscaling approaches are carried out currently and what are their accuracy and uncertainty? And How these quality assurance information can be traced back to geometric and radiometric calibrations?” This issue is dedicated to collect the output of recent advances in the scaling and modelling of essential variables for environmental monitoring with multiscale Earth Observations, which include satellite products, in-situ measurements, (process-based) environmental modelling, and proximity sensing imagery (UAS).

Dr. Antonino Maltese
Dr. Yijian Zeng
Prof. Dr. Jian Peng
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

  • Unmanned aerial systems, geostationary/polar-orbit satellites, in-situ networks
  • Scaling of essential variables for environmental monitoring (i.e., linked to water, energy and carbon cycles)
  • Earth Observations and soil–water–plant–energy modelling
  • Accuracy assurance and uncertainty traceability
  • Operational applications
  • Data science methods

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Published Papers (2 papers)

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Research

20 pages, 7871 KiB  
Article
Spatio-Temporal Variations in Soil pH and Aluminum Toxicity in Sub-Saharan African Croplands (1980–2050)
by Yves Uwiragiye, Qahtan Abdul Wahid Khalaf, Hayssam M. Ali, Mbezele Junior Yannick Ngaba, Mingxia Yang, Ahmed S. Elrys, Zhujun Chen and Jianbin Zhou
Remote Sens. 2023, 15(5), 1338; https://doi.org/10.3390/rs15051338 - 27 Feb 2023
Cited by 4 | Viewed by 2133
Abstract
Soil acidity threatens food production in the tropics. The effect of increasing ammonium-based fertilizer (INF) on soil pH was assessed in sub-Saharan Africa (SSA). A total of 9043 soil data from Africa soil information services, past INF use, and two future scenarios of [...] Read more.
Soil acidity threatens food production in the tropics. The effect of increasing ammonium-based fertilizer (INF) on soil pH was assessed in sub-Saharan Africa (SSA). A total of 9043 soil data from Africa soil information services, past INF use, and two future scenarios of INF use (business as usual (BAU) and equitable diet (EqD)) were used to determine soil pH variations from 1980 to 2022 and to predict soil PH variations from 2022 to 2050. Random forest and extreme gradient boosting algorithms and soil-forming factor covariates were used for the spatio-temporal soil pH predictions. Topsoil acidification was shown to be significant, with mean annual decrements of 0.014, 0.024, and 0.048 from 1980 to 2022, 2022 to 2050 (BAU), and 2022 to 2050 (EqD), respectively. Over the past 42 years, croplands with soil pH < 6.5 have declined significantly, and soil acidification is predicted to become severe by 2050 in the BAU and EqD scenarios. This was indicated by a predicted 3% increase in croplands at risk of aluminum toxicity (soil pH < 5.5) from 66 × 106 ha in 2022 to 78.5 × 106 ha in 2050. The drivers of the spatial variations in the soil pH between 1980 and 2050 were the MAP, basic cation, clay content, SOC, and nitrogen fertilizers. The evaluation metrics of the 10-fold cross-validation showed that the root mean squared errors (RMSEs) of the soil pH from 1980 to 2022, as well as the predicted soil PH from 2022 to 2050 (BAU) and 2022 to 2050 (EqD), were 0.53 pH units, 0.54 pH units, and 0.56 pH units, respectively, with coefficients of determination (R2) of 0.63, 0.64, and 0.66. The findings of this study can be used for the establishment of management strategies for increasing INF use in acidic soils. Full article
(This article belongs to the Special Issue Advances in Scaling and Modelling of Essential Variables for Environmental Monitoring with Multiscale Earth Observations)
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17 pages, 5806 KiB  
Article
Patterns, Dynamics, and Drivers of Soil Available Nitrogen and Phosphorus in Alpine Grasslands across the QingZang Plateau
by Yuchuan He, Jian Sun, Junnan Xiong, Hua Shang and Xin Wang
Remote Sens. 2022, 14(19), 4929; https://doi.org/10.3390/rs14194929 - 2 Oct 2022
Cited by 5 | Viewed by 2234
Abstract
Soil available nutrient contents are critical for regulating ecosystem structure and function; therefore, exploring patterns, dynamics, and drivers of soil available nutrient contents is helpful for understanding the geochemical cycle at the regional scale. However, learning the patterns and dynamics of soil available [...] Read more.
Soil available nutrient contents are critical for regulating ecosystem structure and function; therefore, exploring patterns, dynamics, and drivers of soil available nutrient contents is helpful for understanding the geochemical cycle at the regional scale. However, learning the patterns and dynamics of soil available nutrients across a regional scale is quite limited, especially the soil available nitrogen (SAN) and soil available phosphorus (SAP) in alpine grasslands. In this study, we used machine learning (Random Forest) to map the SAN and SAP at a soil depth of 0–30 cm in alpine grasslands across the QingZang Plateau (QZP) in 2015. Our results showed that the current (2015) contents of the SAN and SAP in alpine grasslands on the QZP were 139.96 mg kg−1 and 2.63 mg kg−1, respectively. Compared to the 1980s, the SAN significantly increased by 18.12 mg kg−1 (14.83%, p < 0.05) and the SAP decreased by 1.71 mg kg−1 (39.40%, p < 0.05). The SAN and SAP contents of alpine meadows were higher than those of alpine steppes. The increases in SAN were not significantly (p > 0.05) different between those two grassland types, while the decrease in SAP was significantly (p < 0.05) higher in alpine meadows than in alpine grasslands. Combined with redundancy analysis, we quantified the impact of environmental drivers, and 80% of the spatial variation in SAN and SAP could be explained by environmental factors. Our findings also highlighted that in the context of global change, the increase in SAP and decrease in SAP might lead to weakening of nitrogen limitation and intensification of phosphorus limitation, especially in alpine meadows. In general, this study expanded the knowledge about the patterns and dynamics of SAN and SAP, and deepened the understanding of the driving mechanisms, which provided a basis for sustainable management of grasslands and optimization of ecological security barrier functions on the QZP. Full article
(This article belongs to the Special Issue Advances in Scaling and Modelling of Essential Variables for Environmental Monitoring with Multiscale Earth Observations)
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