Effect of dust deposition on spectrum-based estimation of leaf water content in urban plant

Lin, Wenpeng; Li, Yuan; Du, Shiqiang; Zheng, Yuanfan; Gao, Jun; Sun, Tengteng

doi:10.1016/j.ecolind.2019.04.074

Cited by 24 publications

(16 citation statements)

References 23 publications

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“…From the perspective of plant parameters, the retrieval accuracy variation with the parameter should also be studied because the parameter usually varies widely in reality. However, this factor was not considered in previous studies on the dust effect [35,36]. In this study, MTCI provided a higher retrieval accuracy for chlorophyll contents of 25-60 µg/cm 2 due to its low sensitivity in this range for the whole dustfall amount.…”

Section: Leaf Chlorophyll Content Retrieval Accuracymentioning

confidence: 76%

“…The effect of dust deposition of leaf water content on estimation was studied in urban areas [36]. The results showed that the effect of dust cannot be ignored for remote sensing estimation of leaf water content.…”

Section: Leaf Chlorophyll Content Retrieval Accuracymentioning

confidence: 99%

“…From the VI perspective, the differences in dust amount would also lead to differences in retrieval accuracy. For water content, the shortwave infrared water stress index (SIWSI) and water index (WI) have different retrieval accuracies [36], similar to MTCI and DD performing differently for chlorophyll content. Thus, we needed to test the performance of different VIs.…”

Section: Leaf Chlorophyll Content Retrieval Accuracymentioning

confidence: 99%

“…The authors found that foliar dust increases the nitrogen prediction error. The effect of dust on spectra-based estimation of leaf water content was also studied in Shanghai, China [36], where the accuracy of water content retrieval varied with dust amount. These studies were instructive for research on the retrieval of chlorophyll content in dusty environments.…”

Section: Introductionmentioning

confidence: 99%

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Experimental and Numerical Investigation of Dustfall Effect on Remote Sensing Retrieval Accuracy of Chlorophyll Content

Liang

et al. 2019

Sensors

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Chlorophyll is the dominant pigment in the photosynthetic light-harvesting complexes that is related to the physiological function of leaves and is responsible for light absorption and energy transfer. Dust pollution has become an environmental problem in many areas in China, indicating that accurately estimating chlorophyll content of vegetation using remote sensing for assessing the vegetation growth status in dusty areas is vital. However, dust deposited on the leaf may affect the chlorophyll content retrieval accuracy. Thus, quantitatively studying the dustfall effect is essential. Using selected vegetation indices (VIs), the medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI), and the double difference index (DD), we studied the retrieval accuracy of chlorophyll content at the leaf scale under dusty environments based on a laboratory experiment and spectra simulation. First, the retrieval accuracy under different dustfall amounts was studied based on a laboratory experiment. Then, the relationship between dustfall amount and fractional dustfall cover (FDC) was experimentally analyzed for spectra simulation of dusty leaves. Based on spectral data simulated using a PROSPECT-based mixture model, the sensitivity of VIs to dust under different chlorophyll contents was analyzed comprehensively, and the MTCI was modified to reduce its sensitivity to dust. The results showed that (1) according to experimental investigation, the DD model provides low retrieval accuracy, the MTCI model is highly accurate when the dustfall amount is less than 80 g/m2, and the retrieval accuracy decreases significantly when the dustfall amount is more than 80 g/m2; (2) a logarithmic relationship exists between FDC and dustfall amount, and the PROSPECT-based mixture model can simulate the leaf spectra under different dustfall amounts and different chlorophyll contents with a root mean square error of 0.015; and (3) according to numerical investigation, MTCI’s sensitivity to dust in the chlorophyll content range of 25 to 60 μg/cm2 is lower than in other chlorophyll content ranges; DD’s sensitivity to dust was generally high throughout the whole chlorophyll content range. These findings may contribute to quantitatively understanding the dustfall effect on the retrieval of chlorophyll content and would help to accurately retrieve chlorophyll content in dusty areas using remote sensing.

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Section: Leaf Chlorophyll Content Retrieval Accuracymentioning

confidence: 76%

Section: Leaf Chlorophyll Content Retrieval Accuracymentioning

confidence: 99%

Section: Leaf Chlorophyll Content Retrieval Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experimental and Numerical Investigation of Dustfall Effect on Remote Sensing Retrieval Accuracy of Chlorophyll Content

Liang

et al. 2019

Sensors

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“…Considering the background of severe rainstorm waterlogging, how to better understand the natural and human causes of rainstorm waterlogging in China is not only the focus of academic circles [32][33][34][35][36][37], but also the basic work of the government (i.e., to carry out rainstorm waterlogging control). It is also one of the important aspects of this paper.…”

Section: Introductionmentioning

confidence: 99%

Understanding China’s Urban Rainstorm Waterlogging and Its Potential Governance

Kong

Sun

Lei

2021

Water

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Urban rainstorm waterlogging is one of the most important problems in urban development and a comprehensive embodiment of urban diseases. China is facing a severe risk of rainstorm waterlogging disasters, which is affecting sustainable development. Urban rainstorm waterlogging in China is caused by many factors, including natural factors and human factors, such as climate warming, unreasonable urban construction, inadequate upgrading of urban fortification standards, etc. Based on the analysis of the current strategies to deal with urban waterlogging around the world, including an increase in surface infiltration, and a reduction in runoff (and its various impacts), this paper holds that the connotation and goal of these measures are highly consistent with the construction of a sponge city in China. Based on the analysis of the problems, including construction of an urban rainwater recovery system, construction of urban rainwater storage facilities, and construction of data platforms faced by China’s sponge city, this paper puts forward the guiding principles of promoting the construction of a sponge city. The guiding principles are to cooperate to deal with climate change and ecological civilization construction, to study the foreign experience, and to unite multiple subjects, integrate multiple elements, design multiple processes, form a joint force, and create an all-round response system to deal with urban rainstorm waterlogging. Then, this paper gives policy recommendations on how to deal with the urban rainstorm waterlogging disasters, which include improving the defense standards, encouraging social participation, popularizing the construction of sponge cities, perfecting the monitoring and early warning system, strengthening the scientific planning of cities, strengthening the ability of dealing with catastrophes in metropolitan areas, the overall planning of cross-regional responses, and enhancing the awareness of decision makers. Finally, this paper expounds the reference significance of urban rainstorm waterlogging control in China to the global audience. This paper explores the significance of comprehensively and scientifically understanding urban rainstorm waterlogging disasters, and provides support for long-term planning and high-quality construction of future safe cities.

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Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi‐task regression and transfer component analysis

Zhai,

Wang,

Zhou

et al. 2024

J Sci Food Agric

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BACKGROUNDWater content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotype, resulting in the difficulties of establishing a universal model. In this study, hyperspectral imaging was used to detect the Soil and Plant Analyzer Development (SPAD) values and water content of fresh rice leaves of three rice varieties (Jiahua 1, Xiushui 121 and Xiushui 134).RESULTSBoth partial least squares regression and convolutional neural networks were used to establish single‐task and multi‐task models. Transfer component analysis (TCA) was used as transfer learning to learn the common features to achieve an approximate identical distribution between any two varieties. Single‐task and multi‐task models were also built using the features of the source domain, and these models were applied to the target domain. These results indicated that for models of each rice variety the prediction accuracy of most multi‐task models was close to that of single‐task models. As for TCA, the results showed that the single‐task model achieved good performance for all transfer learning tasks.CONCLUSIONCompared with the original model, good and differentiated results were obtained for the models using features learned by TCA for both the source domain and target domain. The multi‐task models could be constructed to predict SPAD values and water content simultaneously and then transferred to another rice variety, which could improve the efficiency of model construction and realize rapid detection of rice growth indicators. © 2024 Society of Chemical Industry.

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Effect of dust deposition on spectrum-based estimation of leaf water content in urban plant

Cited by 24 publications

References 23 publications

Experimental and Numerical Investigation of Dustfall Effect on Remote Sensing Retrieval Accuracy of Chlorophyll Content

Experimental and Numerical Investigation of Dustfall Effect on Remote Sensing Retrieval Accuracy of Chlorophyll Content

Understanding China’s Urban Rainstorm Waterlogging and Its Potential Governance

Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi‐task regression and transfer component analysis

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