Monitoring Soil Properties Based on Remote Sensing

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Soil-Sediment-Water Systems".

Deadline for manuscript submissions: closed (28 April 2023) | Viewed by 3912

Special Issue Editors


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Guest Editor
National Authority for Remote Sensing and Space Sciences, Cairo 1564, Egypt
Interests: VIS-NIR reflectance spectroscopy; remote sensing; soil characteristics; soil erosion; soil degradation; soil nutrient; soil quality; soil evaluation; soil salinity

E-Mail Website
Guest Editor
1. Department of Crop Science, Faculty of Agricultural and Forestry Sciences, Universiti Putra Malaysia Bintulu Sarawak Campus, Bintulu 97008, Sarawak, Malaysia
2. Ethnic Borneo Laboratory, Institute of Ecosystem Science Borneo, Universiti Putra Malaysia Bintulu Sarawak Campus, Bintulu 97008, Sarawak, Malaysia
Interests: soil science; soil microbiology; agricultural technology

Special Issue Information

Dear Colleagues,

Increasing food production has become an urgent necessity to address population growth, which necessitates the intensification of food production. Hence, the careful management of soil requires an understanding of its characteristics as well as the factors that limit its production. There is no doubt that the soil is a complex system, and is affected by surrounding climatic factors such as temperature, rain, wind, radiation, and others. Recently, remote sensing data have become more available and have high resolution and capabilities; these data are generated by Landsat, Sentinel 1 or 2, and other satellites; drones; VIS-NIR spectroscopy, etc. Apart from ground sensors, these remote sensing data provide a valuable basis for updating and monitoring soil properties. In addition, remote sensing data can be used in integration with other environmental data to monitor changes in soil properties and their impact on crop production. We welcome contributions on the following topics:

  • UAV-based soil mapping;
  • Soil properties using visible–near-infrared (VIS-NIR) reflectance spectroscopy;
  • Mapping soil organic carbon;
  • Mapping soil characteristics;
  • Mapping soil salinity;
  • Mapping land degradation;
  • Mapping soil water retention;
  • Soil erosion;
  • Satellite time series;
  • Assessing and monitoring soil quality;
  • Soil conservation evaluation;
  • Mapping soil nutrients using remote sensing;
  • Soil quality and crop performance.

Prof. Dr. Elsayed Said Mohamed
Dr. Zakry Fitri bin Ab Aziz
Guest Editors

Manuscript Submission Information

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Keywords

  • UAV-based soil mapping
  • soil properties using visible–near-infrared (VIS-NIR) reflectance spectroscopy
  • mapping soil organic carbon
  • mapping soil characteristics
  • mapping soil salinity
  • mapping land degradation
  • mapping soil water retention
  • soil erosion
  • satellite time series
  • assessing and monitoring soil quality
  • soil conservation evaluation
  • mapping soil nutrients using remote sensing
  • soil quality and crop performance

Published Papers (2 papers)

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Research

16 pages, 4044 KiB  
Article
Assessment of Soil Potentially Toxic Metal Pollution in Kolchugino Town, Russia: Characteristics and Pollution
by Inna Z. Kamanina, Wael M. Badawy, Svetlana P. Kaplina, Oleg A. Makarov and Sergey V. Mamikhin
Land 2023, 12(2), 439; https://doi.org/10.3390/land12020439 - 8 Feb 2023
Cited by 4 | Viewed by 1619
Abstract
The present study was carried out to describe the characteristic features of potentially toxic metals (PTMs) in the soil of industrial city, Kolchugino–Vladimir Region–Russia. The mass fractions in mg/kg of Cu, Pb, Zn, and Cd were measured by atomic absorption spectrometer (AAS). Multivariate [...] Read more.
The present study was carried out to describe the characteristic features of potentially toxic metals (PTMs) in the soil of industrial city, Kolchugino–Vladimir Region–Russia. The mass fractions in mg/kg of Cu, Pb, Zn, and Cd were measured by atomic absorption spectrometer (AAS). Multivariate statistical analysis and hierarchical cluster analysis (HCA), were performed. The obtained results of the potentially toxic elements were mapped using ArcGIS. A total of four pollution indices were calculated to identify the areas with significant pollution associations. The data analysis showed that locations 14 (urban area) and 16 (non-ferrous metallurgy plant) contribute significantly to pollution. Maximum likelihood method was used to classify the land-use and land-cover (LULC). The analysis shows that locations 14 and 16 are belonging to the industrial area on LULC. Great attention should be paid to the control and regulation of waste disposal into the environment, which in turn, has an adversely impact on human health. By using the data, it could help to identify areas where waste control measures need to be implemented, such as increasing recycling or introducing more waste control legislation. It could also help to identify areas where waste control efforts have been successful. Data can be used by government, policymakers, and stakeholders for future planning and R&D activities. Full article
(This article belongs to the Special Issue Monitoring Soil Properties Based on Remote Sensing)
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15 pages, 3257 KiB  
Article
Past and Future Changes of Land Use/Land Cover and the Potential Impact on Ecosystem Services Value of Damietta Governorate, Egypt
by Hazem T. Abd El-Hamid, Hoda Nour-Eldin, Nazih Y. Rebouh and Ahmed M. El-Zeiny
Land 2022, 11(12), 2169; https://doi.org/10.3390/land11122169 - 30 Nov 2022
Cited by 10 | Viewed by 1393
Abstract
This investigation aims to assess the changes of Land Use/Land Cover (LULC) and its impact on ecosystem services value in Damietta Governorate, Egypt. To fulfill this aim, Landsat data of TM5 in 2001, ETM in 2011 and OLI in 2021 were used. The [...] Read more.
This investigation aims to assess the changes of Land Use/Land Cover (LULC) and its impact on ecosystem services value in Damietta Governorate, Egypt. To fulfill this aim, Landsat data of TM5 in 2001, ETM in 2011 and OLI in 2021 were used. The Maximum Likelihood Classifier was employed to track the changes in LULC of the study area. Cellular automata (CA) and Markov model adopted from IDRISI software were used for accurate prediction of the LULC in 2031. The VALIDATE model in TerrSet was used to compare the predicted 2031 LULC with actual 2021 LULC to assess the accuracy of the model. The Millennium Ecosystem Assessment was utilized to assess the value per unit area of land types. The results indicated that there was a noticeable change in different land cover classes during the duration 2001–2021. Results showed that there are decreases in the cultivated area and the bare area, meanwhile the urban area was increased. The cultivated area was remarkably decreased recording 548.2 km2 (62.15%) at 2001, 548.2 km2 (55.79%) at 2011 and 468.96 km2 (53.16%) at 2021 of the total study area. However, the percentage of urban area increased; reporting 65.69 km2 (7.45%), 124.57 km2 (14.12%), and 176.67 km2 (20.03%) at 2001, 2011, and 2021, respectively. LULC analyses in 2031 showed an increase in the urban area by 2.8% and a decrease in the cultivated area by 7.2%. The kappa index values are greater than 0.80, which shows a strong agreement between simulated and predicted LULC maps. The comprehensive index of Damietta Governorate ranges from 100 to 400. The ES that experienced positive ESV changes during the study period gives strength indicator for achieving the sustainable development of Damietta Governorate. To prevent further ecosystem degradation and to ensure the best possible delivery of ES, it is necessary to reduce the current drivers of LULC changes within the buildup in agricultural land. The study helps the local authorities to better understand the land use system and to develop an improved land use management strategies that manage the urban expansion and guarantee the ecological conservation. Full article
(This article belongs to the Special Issue Monitoring Soil Properties Based on Remote Sensing)
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