Using NDVI percentiles to monitor real-time crop growth

Li, Congcong; Li, Hongjun; Li, Jiazhen; Lei, Yuping; Li, Chunqiang; Manevski, Kiril; Shen, Yanjun

doi:10.1016/j.compag.2019.04.026

Cited by 76 publications

(36 citation statements)

References 16 publications

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“…By extracting the regional median value of different phenological regions as the standard value, the average growth situations of crops in various phenological areas are reflected, which are not affected by changes of crop planting structure. In other studies, the pNDVI has also been used to solve this problem [39], although the monitoring accuracy of different disasters has not been quantified. Compared with the R NDVI_AM(i)(j) , the R NDVI_ZM(i)(j) cannot reflect the change of crop growth relative to the historical average.…”

Section: Discussionmentioning

confidence: 99%

“…Second, the error of the crop growth fluctuation in the selected reference year will affect the assessment results of that year. In order to avoid the crop distribution changes that lead to information errors, C. Li proposed monitoring the growth of winter wheat based on the percentage of crop NDVI (pNDVI) [39]. Few studies, however, can remove this limitation in terms of phenology.…”

Section: Introductionmentioning

confidence: 99%

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Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

Liu

Luo

et al. 2020

Sustainability

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Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Google Earth Engine provides the possibility of quickly extracting the disaster range over a large area. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (RNDVI_TM(i)), the NDVI median phenology standardization model (RNDVI_AM(i)(j)), and the NDVI median spatiotemporal standardization model (RNDVI_ZM(i)(j)). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for RNDVI_TM(i), RNDVI_AM(i), and (j)RNDVI_ZM(i)(j), respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and was applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

Liu

Luo

et al. 2020

Sustainability

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“…The GIMMS NDVI 3g dataset was used in the present study, which can be freely downloaded from NASA (https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1). This dataset has a spatial resolution of 8 km and 15-day intervals from 1982 to 2015 and has been widely utilized in research on monitoring vegetation dynamics responding to climatic changes [15][16][17][18]. To reduce the interference of water vapor in the atmosphere and enhance the accuracy of this NDVI dataset, we obtained the monthly NDVI dataset from 1982 to 2015 using the Maximum Value Composite (MVC) method [19,20].…”

Section: Data Sourcementioning

confidence: 99%

Examining Fractional Vegetation Cover Dynamics in Response to Climate from 1982 to 2015 in the Amur River Basin for SDG 13

Yang

Mao

et al. 2020

Sustainability

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The impacts of climate and the need to improve resilience to current and possible future climate are highlighted in the UN’s Sustainable Development Goal (SDG) 13. Vegetation in the Amur River Basin (ARB), lying in the middle and high latitudes and being one of the 10 largest basins worldwide, plays an important role in the regional carbon cycle but is vulnerable to climate change. Based on GIMMS NDVI3g and CRU TS4.01 climate data, this study investigated the spatiotemporal patterns of fractional vegetation cover (FVC) in the ARB and their relationships with climatic changes from 1982 to 2015 varying over different seasons, vegetation types, geographical gradients, and countries. The results reveal that the FVC presented significant increasing trends (P < 0.05) in growing season (May to September) and autumn (September to October), but insignificant increasing trends in spring (April to May) and summer (June to August), with the largest annual FVC increase occurring in autumn. However, some areas showed significant decreases of FVC in growing season, mainly located on the China side of the ARB, such as the Changbai mountainous area, the Sanjiang plain, and the Lesser Khingan mountainous area. The FVC changes and their relationships varied among different vegetation types in various seasons. Specifically, grassland FVC experienced the largest increase in growing season, spring, and summer, while woodland FVC changed more dramatically in autumn. FVC correlated positively with air temperature in spring, especially for grassland, and correlated negatively with precipitation, especially for woodland. The correlations between FVC and climatic factors in growing season were zonal in latitude and longitude, while 120° E and 50° N were the approximate boundaries at which the values of mean correlation coefficients changed from positive to negative, respectively. These findings are beneficial to a better understanding the responses of vegetation in the middle and high latitudes to climate change and could provide fundamental information for sustainable ecosystem management in the ARB and the northern hemisphere.

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“…However, this method has limitations: it requires a crop that is constant for many years, The average value, that is, the planting structure and distribution of the crop cannot be changed; or the error of the crop growth fluctuation selected as the reference year will affect the assessment results of this year. In order to avoid the change of the crop distribution and lead to information errors, Li C proposed a Monitoring the growth of winter wheat based on the percentage of crop NDVI (pNDVI) [39]; However, few studies can eliminate this limitation from the perspective of phenology. In addition, studies are mostly concentrated in smaller areas.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Extraction Method of Regional Scale Agricultural Disasters Based on Google Earth Engine

Liu¹,

Liu²,

Luo³

et al. 2020

Preprint

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Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of temporal and spatial disasters patterns of large-scale long time series. In order to find out a rapid and effective method to monitor disaster in a wide range, based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products of 250 meter spatial resolution synthesized in 16 days during the year 2005-2019 and three kinds of disaster monitoring and scope extraction models are proposed: normalized vegetation index median time standardization (RNDVI_TM(i)) model, the normalized vegetation index median phenology Standardization(RNDVI_AM(i)(j)) model, normalized vegetation index median spatiotemporal Standardization (RNDVI_ZM(i)(j)) model. The optimal threshold of disaster extraction for each model in different time phases was determined by Otsu method, and the extraction results were verified by Medium resolution image and ground measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang province from 2010 to 2019 was extracted and the temporal and spatial pattern of disasters was analyzed based on the meteorological data. It shows that the three above-mentioned models have high disaster monitoring and range extraction capabilities with the verification accuracy of RNDVI_TM(i) 97.46%, RNDVI_AM(i)(j) 96.90%, and RNDVI_ZM(i)(j) 96.67% respectively. The spatial and temporal distribution of disasters is consistent with the disaster of the insured plots and meteorological data in the whole province. Meanwhile, it turns out that different monitoring and extraction methods are used in different disasters, among which wind hazard and insect disasters often need to be delayed for 16 days to observe. Each model also has various sensitivity and applicability to different disasters. Compared with other methods, this method is fast, and convenient, which allows it to be used for large-scale agricultural disaster monitoring and is easy to be applied into other research areas. The research provides a new idea for large-scale agricultural disaster monitoring.

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Using NDVI percentiles to monitor real-time crop growth

Cited by 76 publications

References 16 publications

Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

Examining Fractional Vegetation Cover Dynamics in Response to Climate from 1982 to 2015 in the Amur River Basin for SDG 13

A Rapid Extraction Method of Regional Scale Agricultural Disasters Based on Google Earth Engine

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