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Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
School of Architecture, Chang'an University, Xi’an 710061, China
Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou, China
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Environmental Footprints Forecasts Using Remote Sensing, Information Technology and Artificial Intelligence Methods

Abstract submission deadline
31 October 2024
Manuscript submission deadline
31 December 2024
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394

Topic Information

Dear Colleagues,

Environmental footprints have emerged and been widely applied in environmental impact assessments to show the appropriation of natural resources by humans. Footprints are divided into environmental, economic, and social footprints, and combined environmental, social, and/or economic footprints. The concept of a “footprint” originates from the idea of the ecological footprint, EF. Each footprint indicates particular classes of pressures associated with process, product, or activity from the life cycle perspective.

Several footprints are identified as key footprints because they are essential for sustainability and sustainable development. The recognized footprints are carbon (CF), water (WF), nitrogen (NF), phosphorus (PF), biodiversity (BF), and land (LF).

The goal of this Special Issue is to offer a platform for the quick, open publication of peer-reviewed research on environmental footprints estimations using RS, GIS, and artificial modes in semiarid and arid regions. The topics that will be covered in this issue include, but are not limited to, the following:

  • Environmental resource management;
  • Environmental risk assessment;
  • Water/groundwater footprint;
  • Gas emissions
  • Water scarcity;
  • Advanced artificial intelligence;
  • Remote sensing applications;
  • Rainfall harvesting;
  • Information technology;
  • Spatial planning and governance for climate adaptation.

Dr. Ahmed Elbeltagi
Prof. Dr. Quanhua Hou
Prof. Dr. Bin He
Topic Editors

Keywords

  • footprint assessment
  • water/groundwater footprint
  • carbon footprint
  • ecological processes
  • water resources management
  • remote sensing/information technology tools
  • novel artificial intelligence
  • environmental risk assessment
  • climate change
  • global sustainability

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Agriculture
agriculture 		3.6 3.6 2011 17.7 Days CHF 2600 Submit
Climate
climate 		3.7 5.2 2013 19.7 Days CHF 1800 Submit
Forests
forests 		2.9 4.5 2010 16.9 Days CHF 2600 Submit
Remote Sensing
remotesensing 		5.0 7.9 2009 23 Days CHF 2700 Submit
Water
water 		3.4 5.5 2009 16.5 Days CHF 2600 Submit
Earth
earth 		- 1.6 2020 17.6 Days CHF 1200 Submit

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Published Papers (1 paper)

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21 pages, 4697 KiB  
Article
Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data
by Xuchao Jiao, Hui Liu, Weimu Wang, Jiaojiao Zhu and Hao Wang
Agriculture 2024, 14(6), 873; https://doi.org/10.3390/agriculture14060873 (registering DOI) - 30 May 2024
Abstract
Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, [...] Read more.
Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, and require a large amount of manpower and resources. In contrast, the use of hyperspectral technology enables the precise and rapid monitoring of soil physicochemical properties, playing an important role in advancing precision agriculture. Yuxi City, Yunnan Province, was selected as the study area; soil samples were collected and analyzed for soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) contents. Additionally, soil spectral reflectance was obtained using a portable spectroradiometer. Hyperspectral characteristic bands for soil nutrients were selected from different spectral preprocessing methods, and different models were used to predict soil nutrient content, identifying the optimal modeling approach. For SOM prediction, the second-order differentiation-multiple stepwise regression (SD-MLSR) model performed exceptionally well, with an R2 value of 0.87 and RMSE of 6.61 g·kg−1. For TN prediction, the logarithm of the reciprocal first derivative-partial least squares regression (LRD-PLSR) model had an R2 of 0.77 and RMSE of 0.37 g·kg−1. For TP prediction, the logarithmic second-order differentiation-multiple stepwise regression (LTSD-MLSR) model had an R2 of 0.69 and RMSE of 0.04 g·kg−1. For AN prediction, the logarithm of the reciprocal second derivative-partial least squares regression (LRSD-PLSR) model had an R2 of 0.83 and RMSE of 24.12 mg·kg−1. The results demonstrate the high accuracy of these models in predicting soil nutrient content. Full article
(This article belongs to the Topic Environmental Footprints Forecasts Using Remote Sensing, Information Technology and Artificial Intelligence Methods)
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