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Applications of Machine Learning and Remote Sensing in Soil and Water Conservation

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01 October 2024

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01 October 2024

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Abstract
The application of machine learning (ML) and remote sensing (RS) in soil and water conserva-tion has become a powerful tool. As analytical tools continue to advance, the variety of ML al-gorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review on the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applica-tions in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the field of soil and water conservation.
Keywords: 
Subject: Environmental and Earth Sciences  -   Environmental Science

1. Introduction

Soil and water play a pivotal role in various ecological processes, including nutrient cycling, water filtration, and habitat provision, which collectively support biodiversity and ecosystem stability. Soils contribute to the cycling of carbon, nitrogen, and phosphorus critical for plant growth and ecosystem productivity [1,2,3]. Water is essential for maintaining hydrological cycles, regulating temperature, and sustaining terrestrial and aquatic habitats [4,5]. These resources are fundamental to ecosystem resilience and functionality, impacting not only natural processes, but also human activities such as agriculture and urban development [6,7].
In addition to their ecological significance, soil and water resources are crucial for sustainable agricultural practices and food security [8]. However, their integrity is increasingly compromised by anthropogenic factors including climate change, population growth, deforestation, and unsustainable land use practices [9]. Climate change can exacerbate soil erosion, disrupt nutrient cycles, and affect water availability by altering precipitation patterns and increasing frequencies of extreme weather events [10]. Population growth and urban expansion place additional pressures on these resources, leading to overexploitation, pollution, and habitat loss [11]. Deforestation can further undermine soil structure and reduce land’s capacity to retain water, while poor land management practices can accelerate soil degradation and water contamination [12,13].
Addressing these challenges requires a comprehensive approach to soil and water conservation that encompasses a range of strategies aimed at mitigating negative effects of these stressors [14,15]. Effective soil conservation involves practices such as erosion control, moisture retention through irrigation management and organic amendments, and sustainable land use planning. Similarly, water conservation encompasses measures to enhance water quality, improve storage capacity, and promote efficient usage. These conservation practices are important not only for sustaining ecosystem health and agricultural productivity, but also for supporting broader environmental management goals, including wildfire mitigation and recovery. To address this, numerical models have traditionally been important tools in soil and water conservation [16,17]. However, their reliance on a limited set of variables and specific assumptions often results in prediction accuracy being heavily dependent on given input data [18,19,20,21]. Additionally, they may fail to account for uncertainties in the detection of climate change [18]. Thus, advanced tools that can complement or even replace traditional numerical models are needed. Integration of machine learning (ML) and remote sensing (RS) data presents a promising solution to limitations of traditional methods in soil and water conservation [22]. Advances in usage of RS data provide extensive spatial and temporal data, capturing environmental changes with high precision across large areas [23,24]. RS techniques such as multispectral and hyperspectral imaging, LiDAR, and synthetic aperture radar (SAR) enable the collection of data on various environmental parameters, including soil moisture, vegetation cover, land surface temperature, and water quality. When combined with ML techniques such as Random Forest (RF), Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and Gradient Boosting Machines (GBMs), these data can be analyzed to identify patterns, make predictions, and develop more effective conservation strategies. Combined with ML, these data can be analyzed to identify patterns of spatial and temporal data, make predictions, and establish more effective conservation strategies [25,26]. The ability of ML to process vast amounts of RS data enables extraction of valuable insights from complex, unstructured datasets, leading to improved accuracy in predicting soil and water resource changes. Additionally, the fusion of ML and RS allows for real-time monitoring and assessment, offering dynamic and responsive tools for decision-making in conservation practices [27,28]. This integrated approach can enhance resource optimization, increase precision of policy implementations, and facilitate data-driven decision-making in soil and water management.
The objective of this review is to explore the potential of ML and RS technologies in advancing soil and water conservation. This review aims to provide an overview of current research areas within soil and water conservation and to present how a combination of ML and RS can overcome limitations of traditional methods. The review will highlight applications of various ML techniques and RS data in different subfields of soil and water conservation, demonstrating their effectiveness in improving prediction accuracy and resource management. Additionally, this paper will discuss future research directions, focusing on integrating ML with RS and development of innovative solutions for sustainable soil and water conservation. The scope includes a detailed examination of how these advanced tools can be applied across diverse conservation challenges and implications for future policy and practice.

2. Materials and Methods

2.1 Searching/Classification Methodology
The literature review focused on collecting published journals that applied ML algorithms and RS data to conduct research related to soil and water conservation, as summarized in Figure 1.
To identify relevant publications, we utilized the web-based bibliographic database ‘ScienceDirect’ to identify relevant publications, using specific keywords such as “machine learning,” “remote sensing,” “soil conservation,” and “water conservation”. This search resulted in the identification of 200 studies conducted across 47 countries, as shown in Figure 2. Studies conducted in China, the United States, and Iran, with smaller but notable frequencies in Australia, Canada, Russia, and several European countries showed the highest frequencies. Studies conducted in other locations around the world, including parts of Africa, South America, and South Asia, were also marked, indicating their relative frequency in the data. Figure 3 illustrates trends in reviewed publications across four key research areas from 2006 to 2024. Wildfire management exhibited a dramatic increase in publications, peaking in 2022, likely due to the increasing impact of climate change, advances in ML and RS technologies, and increased global awareness driven by initiatives such as the UN’s SDGs and the Paris Agreement. Research studies on soil properties, hydrology, and water resources also peaked around the same time, although they showed a more gradual rise and fall. Frequency of research on biomass-vegetation remained relatively low in comparison, with minimal fluctuations throughout the period.
Research subjects of 200 studies were then identified and categorized into a total of 37 specific research topics, referred to as “subcategorized subjects,” including above-ground biomass, grassland biomass, ground biomass, soil conductivity, soil salinity, soil organic carbon (SOC), soil aggregate stability, soil chemistry, soil degradation, soil erodibility, soil matric potential, soil mercury, soil moisture, soil nutrients, soil total nitrogen, soil respiration, soil stiffness, soil texture, soil types, soil organic matter, soil water content and evapotranspiration, groundwater level, streamflow, surface water, water storage, sediment concentration, algal blooms, Secchi disk depth, sediment discharge, water quality, turbidity, evapotranspiration, flash flood water depth, inundation status, ocean surface CO2, wildfire prediction, wildfire monitoring, and wildfire recovery as shown in Table 1. These subcategorized subjects were then reclassified into four research fields: 1) biomass-vegetation, 2) soil properties, 3) hydrology and water resources, and 4) wildfire management. While some subcategorized subjects of collected studies were closely related and ambiguous to distinguish, the classification focused on the objective of this study. The research field of soil properties had the highest number of publications, followed by wildfire management, hydrology and water resources, and biomass-vegetation research fields. Publications were distributed as follows: 93 (47%) papers on soil properties, 52 (26%) papers on wildfire management, 50 (25%) papers on hydrology and water resources, and 5 (2%) papers on biomass-vegetation as shown in Figure 4.

3. Results and Discussion

3.1. Types and Frequencies of RS Data Used in Soil and Water Conservation Research

Of 200 studies collected, a total of 41 different types of RS techniques were identified (Table A2). Figure 5 depicts the number of publications that utilized each RS data, highlighting only those RS data used more than twice across the 200 studies. The following RS data were used only once each: AGRS, ALOS-2, AVIRIS-NG, GF-1, Triplesat, PALSAR-2, Terra, ZH-1, ETM+, SVC, SRTM, NLCD, Himawari-8, TDC, AMSR-E, MERIS, MERRA-2, Chinese Environmental 1A satellite, GOES-16, TM, SAR, and SPOT-4. This study analyzed types and frequencies of RS data used across different research fields in soil and water conservation. RS data were classified based on four research fields. Below is an overview of the most frequently used RS data types within each field. Table 3 summarizes the number of publications and the top three most commonly used RS data in different environmental research fields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management. In the field of biomass-vegetation, MODIS and UAVs were the most commonly used algorithms, appearing two times. These tools are crucial for large-scale biomass estimation and vegetation monitoring. For Soil Properties, Landsat 8 was the most frequently used (32 times). Its high-resolution images and multispectral capabilities are particularly effective for evaluating soil characteristics. In hydrology and water resources, Landsat 8 was again predominant. It was used 18 times for tracking changes in water bodies, flood monitoring, and resource management. Lastly, in wildfire management, MODIS was the leading algorithm. It was used 20 times, offering vital support for real-time fire monitoring and management across damaged areas.
Landsat and MODIS are essential satellite sensors, each suitable for specific research applications due to differences in data characteristics and resolution. Landsat 8, Sentinel-2, and MODIS are frequently used in soil and water resource studies due to their technical features. Landsat, with its high spatial resolution of 30 meters, is ideal for detailed studies of soil characteristics and hydrology. It allows for precise analysis of small-scale features such as soil moisture and water flow. In contrast, MODIS with a lower resolution of 250 to 1,000 meters but a higher revisit frequency of twice daily is better suited for monitoring large-scale, rapidly changing events like wildfires. MODIS is particularly effective for wildfire management due to its ability to capture wide areas quickly and detect heat through infrared bands, while Landsat’s detailed imagery is preferred for soil and hydrological research.
These RS data also provide broad spatial and temporal coverage. MODIS collects global data daily, while Landsat 8 and Sentinel-2 offer high-resolution imagery with revisit periods of 16 days and 5 days, respectively. This allows for long-term monitoring of environmental changes and time-series analysis of hydrological events or soil changes. Moreover, these RS data offer long-term historical datasets, which is a significant advantage. For instance, the Landsat program has been accumulating data since the 1970s, making it valuable for analyzing decades of environmental changes or soil erosion patterns. Similarly, Sentinel-2 and MODIS provide consistent datasets over the years, supporting accurate trend analysis and modeling. The preference for these applications reflects their efficiency in capturing crucial information for soil and water resource conservation.

3.2. Types and Frequencies of ML Algorithm Used in Soil and Water Conservation Research

A total of 50 distinct ML algorithms were identified and their usage frequencies are illustrated in Figure 6. Algorithms that appeared more than twice are shown, while those not depicted to prevent excessive clutter, each used only once, include RTM, ANFIS, ARD, BAGGING, BDT, SA, SCA-Elman, SoLIM, SOM, SR, PSO-SVR, B-CART, CBR, DR, DELM, GAN, BAYE, LGBM, GSC, GRNN, PCR, PKR, RPART, MR-CNN, LMM, EBP, ETR, ELR, EM, EFS, EPR, DBN, DRF, DMP, LDA, MDN, MLPR, MT, Nue-SICR, SICR, FR, FCN, FNN, AdaBag, BST, M5P, YOLO, and IF. The number of publications across research fields related to RS data and the top three most commonly used algorithms along with their frequency of usage is shown in Table 4. In the biomass-vegetation field, RF and ANN were used most frequently. The Soil properties field had the highest number of publications, with RF being the most prevalent algorithm. Similarly, hydrology and water resources and wildfire management also showed a preference for RF as the leading algorithm. In cases where algorithms were used with the same frequency, they were ranked equally. The total number of algorithm usages does not necessarily match the total number of publications due to the use of multiple algorithms in some studies.
RF, ANN, and SVM were the most commonly used ML algorithms across different research fields. RF is an ensemble learning method that constructs multiple decision trees during training and aggregates their outputs for prediction. This approach enhances accuracy and mitigates overfitting by averaging results of numerous trees, which can help manage noisy and high-dimensional datasets common in environmental research. RF is frequently used in environmental research due to its effectiveness in identifying feature importance, which helps researchers determine the most influential variables affecting environmental processes such as water quality prediction [143,156] and land cover classification[80] . Its robustness and capacity to manage large datasets enhance its utility across various ecological and hydrological applications. ANNs excel at modeling complex, non-linear relationships capturing intricate data patterns. Comprised of interconnected neurons that can adjust weights through iterative training, ANNs can optimize predictions for complex environmental variables. This adaptability makes them highly effective for tasks such as flood prediction and groundwater level forecasting, where capturing non-linear interactions is essential. Their ability to generalize from extensive datasets ensures precise modeling of dynamic environmental systems. This adaptability makes ANNs particularly useful for tasks such as flood prediction or groundwater level forecasting, where capturing non-linear interactions is crucial. Their capability to generalize from extensive training datasets enables accurate modeling of dynamic environmental systems and phenomena.

3.3 Field-Specific Observations

3.3.1. Biomass-Vegetation

In the research field of biomass and vegetation, researchers frequently combine MODIS and UAV data with ML algorithms such as RF and ANN to assess vegetation conditions and estimate biomass. MODIS is effective for large-scale vegetation monitoring and tracking seasonal changes and long-term trends due to its global coverage and frequent revisit times. UAV, on the other hand, provides high spatial resolution and flexibility, capturing detailed images at plot or landscape levels. Equipped with sensors like RGB cameras, multispectral, hyperspectral, and LiDAR, UAVs can be utilized to detect specific wavelengths that indicate plant health, structure, and biomass. RF is particularly useful for managing large datasets and modeling complex relationships between spectral data and vegetation attributes such as Leaf Area Index (LAI), chlorophyll content, and biomass density.

3.3.2. Soil Properties

In the research field of soil properties, numerous studies have combined RS data from Landsat 8 and Sentinel-2 with RF. Landsat 8 and Sentinel-2 are widely utilized for mapping and monitoring soil properties at regional and global scales due to their high-resolution multispectral imagery. These satellites provide data that can be used to derive indicators related to soil properties such as organic carbon content, soil moisture, and soil texture. ML algorithms such as RF and SVM are particularly effective in this field because they can handle large and complex datasets and model nonlinear relationships between RS-derived variables and soil attributes. RF is especially useful for processing large amounts of data and analyzing complex patterns that link spectral information with soil properties, while SVM is often employed to classify and predict soil properties by maximizing the margin between different types of soil data. The integration of RS data and ML algorithms enables more accurate and efficient prediction and mapping of soil characteristics, which is essential for sustainable land management, agriculture, and environmental conservation.

3.3.3. Hydrology and Water Resources

In the research field of hydrology and water resources, studies have mainly focused on predicting river flow, groundwater levels, and water quality parameters. RS data from Landsat 8 and Sentinel-2 have been widely used due to their ability to capture detailed spatial and temporal information related to water bodies and terrain. These satellites can provide high-resolution multispectral imagery that is crucial for monitoring and assessing various hydrological variables such as surface water extent, vegetation cover, and soil moisture, which can directly influence hydrological processes. In this field, RF and SVR are preferred for modeling complex and nonlinear hydrological processes due to their robustness and accuracy. RF has been utilized to process large datasets and identify patterns in river flow and groundwater levels, while SVR has been employed to predict continuous variables such as water quality parameters, leveraging its ability to model relationships in data with limited observations. The combination of RS data from Landsat 8 and Sentinel-2 with ML algorithms such as RF and SVR can enhance the ability to accurately predict and manage water resources, which is essential for sustainable water resource management and planning.

3.3.4. Wildfire Management

In the research field of wildfire management, ML techniques are extensively used to predict and monitor wildfire occurrences. MODIS, known for its daily global coverage, is one of the most frequently utilized RS data sources in this domain. MODIS provides critical information for real-time monitoring and historical analysis of wildfires, enabling the detection of active fires, mapping of burn scars, and assessment of the extent of fire-affected areas. Its frequent revisit times are particularly useful for tracking wildfire progression and immediate impacts. To predict fire-prone areas and assess post-fire effects on soil and vegetation, MODIS data are commonly used with RF and SVM. RF is highly effective in identifying complex patterns among environmental variables, such as vegetation type, moisture content, and weather conditions, which can influence wildfire risk. SVM is also employed to classify regions based on fire vulnerability and to assess the severity of fires within ecosystems. The integration of MODIS data with ML algorithms such as RF and SVM can enhance the ability to predict wildfires, mitigate risks, and manage post-fire recovery efforts, contributing to more effective wildfire management strategies.

4. Challenges and Limitations

4.1. Data-Related Challenges

One of the fundamental challenges in the application of ML to soil and water conservation lies in the availability, quality, and consistency of RS data. RS data are often characterized by varying spatial, spectral, and temporal resolutions, which can introduce significant variability into datasets used for model training and validation. For instance, while Landsat can provide data with moderate (15 ~ 120 m) spatial resolution and a long temporal record, Sentinel-2 offers higher spatial resolution (10 ~ 60 m) but with a shorter historical dataset. The integration of these diverse data sources can be problematic, as differences in resolution, sensor characteristics, and data acquisition periods can lead to discrepancies that need to be harmonized. In addition, inconsistent or incomplete datasets are a common issue, particularly in regions with limited historical monitoring or where cloud cover frequently obstructs satellite observations. These data gaps can introduce biases into ML models, leading to inaccurate results for prediction. For example, if training data are not representative of the full range of environmental conditions, the model may fail to generalize effectively, resulting in poor performance when applied to new or unseen conditions. Moreover, preprocessing of RS data, including tasks such as georeferencing, atmospheric correction, and resampling to a common spatial and temporal grid, can be technically demanding and resource-intensive. Harmonization of data from multiple sensors requires advanced techniques, such as data fusion and cross-calibration, to ensure consistency of inputs for ML models.

4.2. Technological Limitations

The implementation of ML in the context of soil and water conservation is often constrained by availability of computational resources and inherent complexity of algorithms employed. High-dimensional datasets—characterized by a large number of variables and extensive temporal records—are common in environmental studies. Processing these datasets requires significant computational power, including high-performance computing (HPC) clusters or cloud-based solutions, which may not be readily available in all research settings. The storage of such large volumes of data also poses challenges, as traditional data storage solutions might be insufficient to handle the scale and complexity of RS data. Furthermore, the complexity of ML algorithms—particularly those involving deep learning (e.g., convolutional neural networks, recurrent neural networks)—requires not only computational resources, but also specialized expertise. Deep learning models, for instance, often involve a large number of hyperparameters and require extensive tuning to achieve optimal performances. This complexity can be a significant barrier to the adoption of ML in resource-limited settings, where access to both infrastructure and skilled personnel might be limited. Model interpretability is another a significant concern in environmental applications. Many ML models, especially those classified as “black-box” models, offer limited insights into the underlying decision-making processes, which can hinder their acceptance and use in policy-making or by stakeholders. Decision-makers often require not only accurate predictions, but also an understanding of the rationale, which can be challenging to provide with complex ML models. Finally, the scalability limits the broader applicability of ML models across diverse geographic regions with availability constraints.

4.3. Implementation Issues

The practical application of ML in conservation efforts is also limited by challenges in model interpretation and transparency. Many ML techniques, particularly deep learning models, operate as “black boxes,” making it difficult for stakeholders to understand the decision-making process. This lack of interpretability can hinder the integration of ML outcomes into policy and management strategies.

5. Future Directions and Research Opportunities

Future research on ML for soil and water conservation will benefit from advancements in big data analytics and cloud computing, which can address current limitations related to data processing and storage. The development of more interpretable ML models, such as explainable AI (XAI), is also expected to enhance the integration of ML into decision-making, making outputs more accessible to non-experts. There is also a growing need for research that focuses on the development of hybrid models that can strength various ML algorithms and RS data to enhance prediction accuracy and provide a comprehensive understanding of soil and water processes. Such approaches can improve prediction accuracy and provide a more holistic understanding of environmental processes. Additionally, research should explore the potential of integrating ground-based sensor networks with RS data, enhancing real-time monitoring and predictive capabilities. The adoption of ML in soil and water conservation has significant policy implications, particularly in the context of climate change adaptation and sustainable land management. Future research should emphasize the importance of interdisciplinary collaboration, bringing together experts in ML, environmental science, and policy to ensure that technological advancements can translate into practical conservation outcomes. Collaborative efforts should also focus on capacity-building initiatives to equip stakeholders with necessary skills and knowledge to implement ML-driven solutions effectively.
The selection of algorithms in each field is influenced by characteristics of the data, complexity of the problem, and objectives of the prediction. RF is popular in many environmental and resource management fields due to its ability to handle complex interactions among variables and manage nonlinearity effectively. SVM is useful for high-dimensional data or problems with nonlinear boundaries. Meanwhile, ANN and MLP excel at learning complex nonlinear patterns. The choice of these algorithms is made to optimize outcomes based on characteristics of the data and goals of problem-solving. The selection of ML algorithms varies depending on factors such as data characteristics, problem complexity, and interpretability. Future research should focus on optimizing algorithm selection and improvement by considering these factors. Additionally, comparing performances of various algorithms and conducting comprehensive evaluations will be crucial for proposing the most suitable methodologies for each field, offering significant insights into best practices.

Author Contributions

“Conceptualization, Jang, W.S., Yun, Y.J.; methodology, Jang, W.S., Kim,Y.I., and Park, W.H.; validation, Jang, W.S., Park, W.H., Shin, Y.; formal analysis, Yun, Y.J.; investigation, Park, W.H.; resources, Shin, Y.; writing—original draft preparation, Kim, Y.I.; writing—review and editing, Kim, Y.I., Park, W.H., Engel, B.; visualization, Kim, Y.I.; supervision, Jang, W.S., Yun, Y.J.; project administration, Jang. W.S.; funding acquisition, Jang, W.S. All authors have read and agreed to the published version of the manuscript.”

Funding

“This study was supported by the research project, Developing of S-P-C experts for field-adaptive forest fire management (S: Smart, P: Professional, C: Confluence) (RS-2024-00402624), funded by the Korea Forest Service.”

Data Availability Statement

The data used in this study are contained within the article. Additional data are available upon request from the corresponding author.

Conflicts of Interest

“The authors declare no conflicts of interest.”

Appendix A

Table A1. A list of all the abbreviated ML algorithms used in the paper.
Table A1. A list of all the abbreviated ML algorithms used in the paper.
ML Full of name
ABR Adaptive Boosting Regression
AdaBag Boosting and Bagging
AdaBoost Boosted Classifier
ANFIS Adaptive Neuro Fuzzy Inference System
ANN Artificial Neural Network
ARD Automatic Relevance Determination
BAGGING Bootstrap Aggregating Regression
BAYE Bayesian
B-CART Bagged Classification and Regression Trees
BDT Bagging Decision Tree
BPNN Back Propagation Neural Network
BRTs Boosted Regression Trees
BST Extreme Gradient Boosting Tree
CART Classification and Regression Trees
CB Cubist
CBR Catboost Regression
CNN Convolutional Neural Network
DBN Deep Belief Network
DELM Deep Extreme Learning Machine
DL Deep Learning
DMP Dense Multilayer Perceptron
DNN Deep Neural Networks
DR Dmine Regression
DRF Distributed Random Forest
DTr Decision Tree
EBP Error Back Propagation
EFS Exhaustive Feature Selection
ELM Extreme Learning Machine
ELR Extreme Learning Machine Regression
EM Evaluation metrics
EN Elastic Net
EPR Evolutionary Polynomial Regression
ERT Extremely Randomized Tree
ETR Extreme Tree Regression
FCN Fully Connected Network
FNN Feed forward Neural Networks
FR Frequency Ratio
GAN Generative Adversarial Networks
GB Gradient Boosting
GBDT Gradient Boosted Decision Tree
GBM Gradient Boosting Machine
GBR Gradient Boosting Regression
GBRT Gradient Boosting Regression Tree
GEP Genetic Expression Programming
GLM Generalized Linear Model
GPR Gaussian Process Regression
GRNN General Regression Neural Network
GSC Generalized Synthetic Control
Isolation Forest Isolation Forest
KNN K-nearest Neighbors
La-R Lasso Regression
LARS Least Angle Regression
LDA Linear Discriminant Analysis
LGBM Light Gradient Boosting Machine
Li-R Linear Regression
LMM Linear Mixed-Effects Model
Lo-R Logistic Regression
LSTM Long Short-Term Memory
M5P M5-pruned
MARS Multivariate Adaptive Regression Spline
MaxEnt Maximum Entropy Model
MDN Mixture Density Network
MLP Multilayer Perceptron
MLPR Multi-Layer Perceptron Regression
MLR Multiple Linear Regression
MR-CNN Mask Region-Based Convolutional Neural Network
MT M5 Model Tree
NB Naïve Bayes
Neu-SICR Neural Network-Satellite and In situ sensor Collaborated Reconstruction
NN Neural Networks
NNET Feed-Forward Neural Network
OLS Ordinary Least Squares
PCR Principal Component Regression
PKR Polynomial Kernel Regression
PLS Partial Least Squares
PLSR Partial Least Squares Regression
PSO-SVR Particle Swarm Optimization and Support Vector Machine
QR Quantile Regression Forest
RBFN Radial Basin Function Neural Network
RF Random Forest
RNN Recurrent Neural Network
RPART Recursive Partitioning and Regression Trees
RR Ridge Regression
RT Regression Tree
RTM Radiative Transfer Models
RVR Relevance Vector Regression
SA Sensitivity Analysis
SCA-Elman Sine Cosine Algorithm-Elman
SGB Stochastic Gradient Boosting
SICR Sensor Collaborated Reconstruction
SLR Stepwise Linear Regression
SoLIM Soil–Landscape Inference Model (Fuzzy logic)
SOM Self-Organizing Maps
SR Simple Regression
SVM Support Vector Machine
SVR Support Vector Regression
XGB EXtreme Gradient Boosting
XGBR Extreme Gradient Boosting Regression
YOLO You Only Look Once
Table A2. Descriptions for RS techniques implemented in reviewed publications. In the description of RS techniques related to satellites, the resolution, launching entity, and key features were included. Note: Each asterisk level corresponds to a specific category (*Satellite name, ** Resolution of satellite, *** Launching entity, and **** Key features).
Table A2. Descriptions for RS techniques implemented in reviewed publications. In the description of RS techniques related to satellites, the resolution, launching entity, and key features were included. Note: Each asterisk level corresponds to a specific category (*Satellite name, ** Resolution of satellite, *** Launching entity, and **** Key features).
RS Techniques Descriptions
Satellite ALOS-2*
  • 3m, 6m, 10m**
  • JAXA (Japan Aerospace Exploration Agency) ***
  • ALOS-2 is a Japanese Earth observation satellite equipped with a SAR sensor for monitoring land surface changes and natural hazards regardless of weather conditions such as clouds or rain****
Chinese Environmental 1A
  • 30m ~ 60m
  • CRESDA (China Centre for Resources Satellite Data and Application)
  • China’s Earth observation satellite for monitoring environmental elements such as air, water, soil, and vegetation
GF-1
  • 2m, 8m
  • China National Space Administration (CNSA)
  • A high-resolution ground observation satellite from China’s GaoFen series, known for its advanced imaging capabilities to monitor urban areas, natural resources, and environmental changes
GOES-16
  • 0.5km ~ 2km
  • NOAA (National Oceanic and Atmospheric Administration)
  • U.S. geostationary weather satellite that monitors weather conditions over North America in real time
Himawari-8
  • 0.5km, 1km, 2km
  • JMA (Japan Meteorological Agency)
  • A geostationary satellite for weather monitoring in the Asia-Pacific region, Himawari-8 provides continuous and detailed weather observations
Landsat 4, 5
  • 30m, 80m, 120m
  • NASA (The National Aeronautics and Space Administration)/USGS (The United States Geological Survey)
  • A satellite sensor designed to observe the Earth’s land surface and monitor changes in land use and natural resources, both of which have officially ended their missions
Landsat 7
  • 15m, 30m, 60m
  • NASA (The National Aeronautics and Space Administration)/USGS (The United States Geological Survey)
  • A satellite sensor used for Earth observation, capable of capturing detailed imagery to monitor environmental changes, land use, and natural resources, and continues to provide valuable data since its launch in 1999
Landsat 8, 9
  • 15m, 30m, 100m
  • NASA (The National Aeronautics and Space Administration)/USGS (The United States Geological Survey)
  • The latest satellite sensors in the Landsat series, launched in 2013 and 2021, providing high-quality Earth surface data for monitoring environmental changes, land use, and natural resources
RADARSAT
  • 1m ~ 100m
  • CSA (Canadian Space Agency)
  • A Canadian-operated SAR (Synthetic Aperture Radar) satellite known for its capability to provide detailed, all-weather, day-and-night imagery for global environmental monitoring, disaster response, and resource management
RapidEye
  • 5m, 6.5m
  • BlackBridge Networks
  • A satellite constellation that provides multi-spectral imagery, designed for applications in agriculture, forest management, and other areas
Sentitel-1
  • 5 ~ 40m
  • ESA (European Space Agency)
  • A satellite sensor using Synthetic Aperture Radar (SAR) to observe the Earth in all weather conditions, both day and night, providing continuous and detailed surface monitoring
Sentitel-2
  • 10m, 20m, 60m
  • ESA (European Space Agency)
  • A satellite sensor providing high-resolution multispectral images, useful for agriculture, forestry, and land cover monitoring
Sentitel-3
  • 300m, 500m, 1km
  • ESA (European Space Agency)
  • A satellite sensor used to monitor sea and land surface temperature, color, and ocean conditions, equipped with multiple instruments for comprehensive Earth observation and environmental monitoring
SMAP
  • 3km, 10km, 40km
  • NASA (The National Aeronautics and Space Administration)
  • A satellite primarily used for observing soil moisture and freeze/thaw conditions, crucial for climate research and agricultural monitoring, providing detailed data to support environmental and climate studies
SPOT-4
  • 10m, 20m , 60m
  • CNES (Centre national d’études spatiales)
  • SPOT-4 is an Earth observation satellite that carries HRVIR, HRG, and VEGETATION sensors with an additional shortwave infrared band for agriculture, forestry, and environmental monitoring
SPOT-7
  • 1.5m, 6m
  • Airbus Defence and Space
  • SPOT-7 is the latest satellite in the SPOT series, providing high-resolution Earth observation imagery with a NAOMI sensor that significantly improves spatial resolution
SRTM
  • 30m, 90m
  • NASA (The National Aeronautics and Space Administration)
  • A global satellite mission that collects elevation data to create 3D terrain models of Earth’s surface
Terra
  • 250m, 500m, 1km
  • NASA (The National Aeronautics and Space Administration)
  • A satellite that provides comprehensive observations of the Earth’s environment, collecting data on the atmosphere, land, oceans, and energy systems to support environmental monitoring and research
Triplesat
  • 0.8m, 3.2m
  • 21AT (The Twenty First Century Aerospace Technology)
  • A high-resolution Earth observation satellite widely used for commercial purposes, offering detailed imagery for applications in agriculture, urban planning, and resource management
WorldView-3
  • 0.31m, 1.24m
  • DigitalGlobe
  • WorldView-3 is a commercial high-resolution Earth observation satellite that provides high-quality imagery data for use in a variety of industries
ZH-1
  • 10m
  • CNSA (the China National Space Administration / ASI (the Italian Space Agency)
  • A high-resolution Earth observation satellite from China, designed for detailed imaging to monitor urban areas, natural resources, and environmental changes
AGRS
  • A technology that utilizes aircraft and drones to gather detailed information about the Earth’s surface and geology, AGRS provides valuable data for various applications
AMSR-E
  • A microwave radiometer that monitors various aspects of the Earth’s water cycle, including precipitation, cloud water, water vapor, sea surface winds, sea surface temperature, ice, snow, and soil moisture
AVIRIS-NG
  • An airborne hyperspectral imaging sensor that captures detailed information across a wide range of spectral bands, including visible and infrared wavelengths
ETM+
  • ETM+ is a sensor on board the Landsat 7 satellite that is an enhanced version of ETM with a total of eight spectral bands
Thermal infrared
  • A remote sensing technology that measures surface temperatures by observing thermal infrared emissions, providing data for climate studies, weather monitoring, and environmental analysis
Leica ADS80
  • A high-resolution digital camera used for aerial photogrammetry, the Leica ADS80 captures detailed images from the air
LiDAR
  • A remote sensing technology that uses lasers to precisely measure the 3D structure of terrain, providing detailed topographic data for applications in mapping, forestry, and environmental monitoring
MERIS
  • A sensor with high spectral and radiometric resolution and dual spatial resolution that studies the Earth’s water cycle, including precipitation, cloud water, water vapor, sea surface winds, sea surface temperature, ice, snow, and soil moisture
MODIS
  • A satellite sensor that captures comprehensive global data, viewing the entire Earth’s surface every one to two days with high temporal resolution
PALSAR-2
  • An L-band SAR mounted on the ALOS-2 satellite, providing detailed surface information globally with high precision for applications in terrain mapping, disaster monitoring, and environmental assessment
SAR
  • A technology that uses electromagnetic waves to observe the Earth’s surface, enabling data collection in all weather conditions and at any time, providing reliable information for environmental monitoring and disaster management
SVC
  • A spectroradiometer that measures the reflectance spectra of the Earth’s surface, enabling detailed analysis of material composition and characterization for applications in geology, agriculture, and environmental monitoring
TDC
  • A thermal infrared sensor, which measures infrared radiation to detect temperature variations on the Earth’s surface
Hyperspectral Imager
  • An instrument that measures hundreds of narrow wavelength bands, a hyperspectral imager precisely analyzes the material composition of the Earth’s surface
TM
  • A sensor on the Landsat 4 and 5 satellites with seven spectral bands, TM (Thematic Mapper) is optimized to collect detailed information on land surface characteristics
UAS / UAV
  • Remote sensing platforms using drones, including UAS (Unmanned Aerial Systems) and UAVs (Unmanned Aerial Vehicles), employed to collect high spatial resolution data for various applications such as mapping, agriculture, and environmental monitoring
VIIRS
  • A satellite sensor that observes the Earth’s atmosphere, oceans, and land, providing valuable data for climate research, disaster monitoring, and various environmental applications with comprehensive multispectral imaging

References

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  221. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
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Figure 1. An overview of the study’s process, highlighting the search methodology, identification of research topics, employed ML algorithms and RS data, classification and sub-classification criteria, as well as key discussion points
Figure 1. An overview of the study’s process, highlighting the search methodology, identification of research topics, employed ML algorithms and RS data, classification and sub-classification criteria, as well as key discussion points
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Figure 2. A world map depicting 47 countries where research has been conducted, marked by ‘x’. Colored circles represent the number of publications in each country, with the corresponding numbers shown in the legend.
Figure 2. A world map depicting 47 countries where research has been conducted, marked by ‘x’. Colored circles represent the number of publications in each country, with the corresponding numbers shown in the legend.
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Figure 3. Number of publications from 2006 to 2024 in four research areas: wildfire management, soil properties, hydrology and water resources, and biomass-vegetation. A notable peak in publications on wildfire management occurred around 2022.
Figure 3. Number of publications from 2006 to 2024 in four research areas: wildfire management, soil properties, hydrology and water resources, and biomass-vegetation. A notable peak in publications on wildfire management occurred around 2022.
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Figure 4. Distribution of 200 research studies across four research fields. This pie chart illustrates the percentage of publications dedicated to each field: soil properties (47%), hydrology and water resources (25%), wildfire management (26%), and biomass-vegetation (2%).
Figure 4. Distribution of 200 research studies across four research fields. This pie chart illustrates the percentage of publications dedicated to each field: soil properties (47%), hydrology and water resources (25%), wildfire management (26%), and biomass-vegetation (2%).
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Figure 5. Frequency of usage for RS data in soil and water conservation research. A total of 41 different RS techniques were identified. Only those used more than twice are shown in the figure. RS data used only once are not shown in the figure, including AGRS, ALOS-2, AVIRIS-NG, GF-1, Triplesat, PALSAR-2, Terra, ZH-1, ETM+, SVC, SRTM, Himawari-8, and TDC.
Figure 5. Frequency of usage for RS data in soil and water conservation research. A total of 41 different RS techniques were identified. Only those used more than twice are shown in the figure. RS data used only once are not shown in the figure, including AGRS, ALOS-2, AVIRIS-NG, GF-1, Triplesat, PALSAR-2, Terra, ZH-1, ETM+, SVC, SRTM, Himawari-8, and TDC.
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Figure 6. Frequency of usage for ML algorithm in soil and water conservation research. RF shows the highest frequency of usage. Only algorithms used more than twice are shown in the figure. RS data used only once are not shown in the figure.
Figure 6. Frequency of usage for ML algorithm in soil and water conservation research. RF shows the highest frequency of usage. Only algorithms used more than twice are shown in the figure. RS data used only once are not shown in the figure.
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Table 1. Classification of 200 research studies into four research fields based on their subcategorized subjects (this table lists the number of publications associated with each research field and specific subcategorized subjects within them. The four fields include biomass-vegetation, soil properties, hydrology and water resources, and wildfire management, encompassing a total of 37 subcategorized subjects)
Table 1. Classification of 200 research studies into four research fields based on their subcategorized subjects (this table lists the number of publications associated with each research field and specific subcategorized subjects within them. The four fields include biomass-vegetation, soil properties, hydrology and water resources, and wildfire management, encompassing a total of 37 subcategorized subjects)
Research fields Subcategorized subjects Number of publications
Biomass-vegetation Above-ground biomass[29,30,31], grassland biomass[32], ground biomass[33,34] 5
Soil properties Soil conductivity[35,36,37], soil salinity[28,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57], SOC [58,59,60,61,62,63,64,65,66,67,68,69,70,71,72], soil aggregate stability[73,74], soil chemistry[75,76], soil degradation[77], soil erodibility[78,79,80,81], soil matric potential[82], soil mercury[83], soil moisture[84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110], soil nutrients[111,112,113], soil total nitrogen[114], soil respiration[115], soil stiffness[116], soil texture[117,118,119], soil types[120], soil organic matter[121,122,123,124], soil water content and evapotranspiration[125] 93
Hydrology and water resources Groundwater level[126], streamflow[127], surface water[128,129], water storage[130], sediment concentration[131], algal blooms[132], Secchi disk depth[133], sediment discharge[134], waters quality[135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159], turbidity[160,161,162,163,164,165], evapotranspiration[166,167], flash flood water depth[168], inundation status[169], ocean surface CO2[170] 50
Wildfire management Wildfire prediction[171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192], wildfire monitoring[25,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218], wildfire recovery[219,220] 52
Table 3. Overview of the number of publications and the top three most commonly used RS data in different research fields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management
Table 3. Overview of the number of publications and the top three most commonly used RS data in different research fields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management
Research fields Number of publications Top three most commonly used RS data
Algorithms Frequency of usages
Biomass-vegetation 5 (1) MODIS, UAV 2
(2) Landsat 8, Sentinel-2, ALOS-2, STRM 1
N/A N/A
Soil properties 93 (1) Landsat 8 32
(2) Sentinel-2 28
(3) MODIS 22
Hydrology and water resources 50 (1) Landsat 8 18
(2) Sentinel-2 16
(3) Rapid Eye 7
Wildfire management 52 (1) MODIS 20
(2) Sentinel-2 15
(3) Landsat 8 10
Table 4. Overview of the number of publications and the top three most commonly used ML algorithms in different research fields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management
Table 4. Overview of the number of publications and the top three most commonly used ML algorithms in different research fields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management
Research fields Number of publications Top three most commonly used RS data
Algorithms Frequency of usages
Biomass-vegetation 5 (1) RF, ANN 3
(2) SVM, MLR 2
(3) ANFIS, PLS, KNN, MARS 1
Soil properties 93 (1) RF 67
(2) ANN 23
(3) SVM 21
Hydrology and water resources 50 (1) RF 32
(2) SVM, SVR 14
(3) XGB 9
Wildfire management 52 (1) RF 30
(2) SVM 16
(3) MLP 7
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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