Precipitation events determine the spatiotemporal distribution of playa surface salinity in arid regions: evidence from satellite data fused via the enhanced spatial and temporal adaptive reflectance fusion model

Han, Lijing; Ding, Jianli; Zhang, Junyong; Chen, Panpan; Wang, Jingzhe; Wang, Yinghui; Jinjie, Wang; Ge, Xiangyu; Zhang, Zipeng

doi:10.1016/j.catena.2021.105546

Cited by 16 publications

(8 citation statements)

References 78 publications

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“…According to previous field investigation experience and an existing digital soil map (containing information on soil texture, type, etc. ), 129 soil samples were collected for this study, based on the plum sampling method (0–5 cm) (Han et al, 2021; State Environmental Protection Administration of China, 2004). The geographical coordinates and elevation of each central sampling point are recorded by a global positioning system (GPS).…”

Section: Methodsmentioning

confidence: 99%

Soil salinity estimation: Effects of microwave dielectric spectroscopy and important frequencies

Zhao

Ding

et al. 2023

Land Degrad Dev

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The microwave dielectric constant is a key bridge in establishing the relationship between microwave remote sensing and soil salinity (electrical conductivity, EC). However, the response between microwave dielectric spectrum type, frequency, and soil salinity is still unclear. The purpose of this study is to reveal the dielectric spectrum and frequency range closely related to soil salinity. In this study, 129 surface soil samples were collected, and soil EC and microwave dielectric spectrum (frequency range of 0.3–20 GHz) were measured. Four variable selection methods were used to select important spectral variables, and the relationship between these spectral variables and soil salt was constructed using a random forest model. The results showed that the combination of boldε′ (real part) and boldε′′ (imaginary part) revealed the most implicit information related to soil salinity. Considering the important spectral variables, the model accuracy was generally improved to the full‐dielectric spectrum model (the ratios of the performance to the interquartile distance (RPIQ) improved by 0.11–0.40); furthermore, the variable iterative space shrinkage (VISSA) method could most effectively select important spectral variables (RPIQ = 2.32). The selected spectral‐RF models could provide important frequencies for estimating soil salinity; moreover, the low‐frequency range (0.3–4 GHz) and new important frequency ranges (e.g., 7 and 15 GHz) able to respond to soil salinity were identified. This study provides a new strategy for monitoring the soil salinization risk using multiple frequencies microwave remote sensing techniques.

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Section: Methodsmentioning

confidence: 99%

Soil salinity estimation: Effects of microwave dielectric spectroscopy and important frequencies

Zhao

Ding

et al. 2023

Land Degrad Dev

Self Cite

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“…In terms of climatic factors, the concept of warming and humidification has captured the attention of government agencies, academic researchers, and the general public. Both precipitation and temperature in the TRB showed a positive trend, and various studies have demonstrated climate change's impact on the EEQ of the TRB, with different spatial precipitation and temperature variability levels, leading to impacts on different ecosystems [52][53][54]. Therefore, the specific mechanisms behind climate effects on the EEQ of the TRB will be under scrutiny in future studies [42,55].…”

Section: Driving Forces Of Eeq Changes In the Tarim River Basinmentioning

confidence: 97%

Detecting Long-Term Series Eco-Environmental Quality Changes and Driving Factors Using the Remote Sensing Ecological Index with Salinity Adaptability (RSEISI): A Case Study in the Tarim River Basin, China

Chen

Wang

Ding

et al. 2023

Land

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Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone.

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“…This gap is addressed by the fusion of high-spatial-and temporal-multisource remote sensing data, which generates high-spatiotemporal-resolution imagery. They are classified into two categories: enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) [218] and unmixing method. ESTARFM was tested by Bei et al [219], Tao et al [220] and Zeng et al [221], who all reported the enhancement of the fusion method for complex heterogeneous regions.…”

Section: Laimentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

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Precipitation events determine the spatiotemporal distribution of playa surface salinity in arid regions: evidence from satellite data fused via the enhanced spatial and temporal adaptive reflectance fusion model

Cited by 16 publications

References 78 publications

Soil salinity estimation: Effects of microwave dielectric spectroscopy and important frequencies

Soil salinity estimation: Effects of microwave dielectric spectroscopy and important frequencies

Detecting Long-Term Series Eco-Environmental Quality Changes and Driving Factors Using the Remote Sensing Ecological Index with Salinity Adaptability (RSEISI): A Case Study in the Tarim River Basin, China

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

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