Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland

Feng, Sijia; Qiu, Jianxiu; Crow, Wade T.; Mo, X. H.; Liu, Suxia; Wang, Sheng; Gao, Lun; Wang, Xinghang; Chen, Shuisen

doi:10.1016/j.jhydrol.2022.129015

Cited by 11 publications

(4 citation statements)

References 54 publications

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“…The curve-fitting method, on the other hand, uses a mathematical function to estimate the time-series trajectory of vegetation growth without the need to set empirical thresholds in advance, which is relatively more objective 42 . Among them, the Savizky-Golay filter can maximize the retention of data information while eliminating noise interference, which makes it particularly well-suited for extracting climate-related insights from different vegetation 43 , 44 . Commonly used methods for determining key phenological parameters include the threshold method and the method for detecting changes in vegetation indices.…”

Section: Introductionmentioning

confidence: 99%

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Wang,

Chen,

et al. 2024

Sci Rep

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Previous studies have primarily focused on the influence of temperature and precipitation on phenology. It is unclear if the easily ignored climate factors with drivers of vegetation growth can effect on vegetation phenology. In this research, we conducted an analysis of the start (SOS) and end (EOS) of the growing seasons in the northern region of China above 30°N from 1982 to 2014, focusing on two-season vegetation phenology. We examined the response of vegetation phenology of different vegetation types to preseason climatic factors, including relative humidity (RH), shortwave radiation (SR), maximum temperature (Tmax), and minimum temperature (Tmin). Our findings reveal that the optimal preseason influencing vegetation phenology length fell within the range of 0–60 days in most areas. Specifically, SOS exhibited a significant negative correlation with Tmax and Tmin in 44.15% and 42.25% of the areas, respectively, while EOS displayed a significant negative correlation with SR in 49.03% of the areas. Additionally, we identified that RH emerged as the dominant climatic factor influencing the phenology of savanna (SA), whereas temperature strongly controlled the SOS of deciduous needleleaf forest (DNF) and deciduous broadleaf forest (DBF). Meanwhile, the EOS of DNF was primarily influenced by Tmax. In conclusion, this study provides valuable insights into how various vegetation types adapt to climate change, offering a scientific basis for implementing effective vegetation adaptation measures.

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Section: Introductionmentioning

confidence: 99%

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Wang,

Chen,

et al. 2024

Sci Rep

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“…Hence, the trend towards indirect SMC detection, established by relating crop reflectance to soil moisture, is on the rise. The direct link between reflectance and vegetation water content forms the cornerstone of SMC monitoring techniques [18]. Numerous studies have underscored the reflectance-water content relationship across various crops, wheat included [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index

Li,

Xiang,

Liu

et al. 2024

Agronomy

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Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.

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“…This increased precipitation will directly separate soil particles through Sustainability 2023, 15, 7865 2 of 16 raindrops and indirectly enhance runoff scouring. Additionally, the increased frequency and intensity of droughts caused by climate change will exacerbate the process of dryland land degradation and desertification [8,9], which is difficult to reverse. Anthropogenic land-use change is another major factor contributing to human-induced soil erosion.…”

Section: Introductionmentioning

confidence: 99%

Assessing Land Use and Climate Change Impacts on Soil Erosion Caused by Water in China

et al. 2023

Self Cite

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Soil erosion poses a significant threat to land conservation, freshwater security, and ocean ecology. Climate change, with rainfall as one of its primary drivers, exacerbates this problem. Therefore, reliably predicting future soil erosion rates and taking into account anthropogenic influences are crucial for policymakers and researchers in the earth-system field. To address this challenge, we have developed a novel framework that combines the Bayesian Model Averaging (BMA) method with the Revised Universal Soil Loss Equation (RUSLE) model to estimate erosion rates on a national scale. We used BMA to merge five Regional Climate Models (RCMs), reducing uncertainty in ensemble simulations and improving the plausibility of projected changes in climatic regimes over China under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). The RUSLE model was applied to forecast the effects of climate change and land-use change on water erosion in China, using high-resolution climate simulation and prediction inputs. Our findings revealed that under the RCP4.5 and RCP8.5 scenarios, average annual soil loss will increase by 21.20% and 33.06%, respectively, compared to the baseline period. Our analysis also demonstrated a clear distinction between the effects of climate change and land-use change on water erosion. Climate change leads to an increase in precipitation, which exacerbates water erosion rates, with contributions ranging from 59.99% to 78.21%. Furthermore, an increase in radiative forcing will further amplify the effects of climate change. The transformation of land from one that has not been disturbed by humans to one that has been exposed to some soil and water conservation measures will have a mitigating effect on water erosion, with a contribution of −6.96% to −4.68%. Therefore, implementing effective soil and water conservation measures can somewhat mitigate the severity of ongoing soil loss. Our findings have significant implications for policymakers seeking to develop national strategies for soil conservation and model developers working to reduce uncertainty in erosion predictions.

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Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland

Cited by 11 publications

References 54 publications

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index

Assessing Land Use and Climate Change Impacts on Soil Erosion Caused by Water in China

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