Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion

Mouazen, Abdul Mounem; Shi, Zhou

doi:10.3390/rs13050978

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…The PSO model's prediction of the missing soil moisture data that were obtained from the 2014 image was satisfactorily accurate (RMSE = 0.45). This level of accuracy compares well with observations by other investigators in similar studies elsewhere [23][24][25], who also observed significant accuracies using the BME framework to predict soil moisture. The PSO model fit quality that we obtained (AIC value = −10,606.…”

Section: Soil Moisturesupporting

confidence: 90%

Linking Satellite, Land Capability, and Socio-Economic Data for Local-Level Climate-Change-Adaptive Capacity Assessments and Decision Support

Chari,

Zhou,

Hamandawana

2023

Sustainability

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Climate change is now one of the most formidable threats to the livelihoods of resource-poor communities in low-income developing countries world-wide. Addressing this challenge continues to be undermined by the conspicuous absence of actionable adaptation strategies that are potentially capable of enhancing our capacities to realize the Millennial Sustainable Development Goals that seek to securitize access to adequate food supplies for everybody. This paper attempts to address this limitation by providing an improvised geostatistical methodology that integrates multi-source data to map the adaptive capacities of vulnerable communities in a selected South African local municipality, whose livelihoods are largely dependent on rain-fed agriculture. The development of this methodology was based on the use scripts that were compiled in Python and used to test-try its usefulness through a case-study-based assessment of the climate-change-adaptive capacities of local communities in Raymond Mhlaba Local Municipality (RMLM), Eastern Cape Province, South Africa. A Bayesian maximum entropy framework-based technique was used to overcome the lack of missing soil moisture data, which we included because of its reliable usefulness as a surrogate indicator of climate-change-driven variations in this variable on the sustainability of rain-fed agriculture. Analysis of the results from a sampling universe of 124 communities revealed that 65 and 56 of them had high and medium adaptive capacities, respectively, with the remaining 3 having low adaptive capacities. This finding indicates that more than half of the communities in the municipality’s communities have limited capabilities to cope with climate change’s impacts on their livelihoods. Although our proposed methodology is premised on findings from a case-study-based investigation, it is still extremely useful because it demonstratively shows that there is tremendous scope for the scientific community to provide objectively informed insights that can be used to enhance the adaptive capacities of those in need of the badly needed but difficult-to-access information. Added to this is the fact that our proposed methodology is not only applicable for use under different environmental settings but also capable of allowing us to cost-effectively tap into the rich, wide-ranging, freely accessible datasets at our disposal. The aim of this submission is to show that although we have the information, we need to address these persevering challenges by exploring innovative approaches to translate the knowledge we have into actionable climate-change-adaptation strategies.

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Section: Soil Moisturesupporting

confidence: 90%

Linking Satellite, Land Capability, and Socio-Economic Data for Local-Level Climate-Change-Adaptive Capacity Assessments and Decision Support

Chari,

Zhou,

Hamandawana

2023

Sustainability

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“…The common ones are singlelayer feedforward, multilayer feedforward, and Hopfield feedback networks. The simplest network structure can construct a nonlinear structure with multiple inputs but only one output [14].…”

Section: Multidata Fusionmentioning

confidence: 99%

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

et al. 2023

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In order to control the grassland ecological environment, an application method of multisource data fusion technology in the construction of land ecological index is proposed. Due to the high requirements for grassland environmental monitoring, the use of traditional technologies to monitor grassland environmental conditions lacks certain effectiveness, has high investment costs, and consumes a lot of manpower and material resources. The use of sensors to dynamically monitor the grassland environment is conducive to monitoring the environment from a scientific and technological level. By understanding the fusion principle and process of three fusion methods, adaptive weighted average, BP neural network, and D-S evidence theory, the construction of Bashang grassland ecological energy big data platform based on multisource data fusion is proposed. A two-level data fusion model based on grassland environmental monitoring is proposed. Several environmental parameters in the experimental environment were monitored, and the validity of the two-level fusion model was verified by two evaluation indicators, the mean absolute percentage error and the corrosion error. This suggests that a combination of BP neural network and D-S proof theory improves system performance. It provides the possibility for more comprehensive monitoring of grassland ecological environment in the future.

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“…The index product with coarser spatial resolution can characterize the overall trend of the area and generally has temporal continuity. However, due to the existence of mixed pixels, the value of a certain pixel in the product represents the comprehensive contribution of various surrounding ground objects, which obscures the spatial details, and navigation has poor interpretation of the monitoring results or even cannot explain it [71][72][73][74][75]. This effect is often more obvious on images with coarser spatial resolution.…”

Section: Discussionmentioning

confidence: 99%

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

Yang

Song

et al. 2023

Remote Sensing

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As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments.

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Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion

Abstract: Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision application of farming input resources [...]

Cited by 12 publications

References 20 publications

Linking Satellite, Land Capability, and Socio-Economic Data for Local-Level Climate-Change-Adaptive Capacity Assessments and Decision Support

Linking Satellite, Land Capability, and Socio-Economic Data for Local-Level Climate-Change-Adaptive Capacity Assessments and Decision Support

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

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