Improved mapping and change detection of the start of the crop growing season in the US Corn Belt from long-term AVHRR NDVI

Gim, Hyeon Ju; Ho, Chin Siong; Jeong, Su‐Jong; Kim, Jinwon; Feng, Song; Hayes, Michael J.

doi:10.1016/j.agrformet.2020.108143

Cited by 28 publications

(10 citation statements)

References 46 publications

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“…Especially, seasonal vegetation information (e.g., the start, the end, and the length of the growing season, and vegetation biomass) derived from these indices provides key parameters for the understanding of how vegetation has responded to climate change and human activities over time [11,12]. Current remotely sensed datasets such as the global inventory modeling and mapping studies (GIMMS) NDVI3g data set from Advanced Very High-Resolution Radiometer (AVHRR), which have existed for over 30 years and provide an unprecedented opportunity to examine long-term vegetation growth dynamics [13]. However, the use of linear trends to characterize vegetation greening or browning over the period of three decades (since the 1980s) may mask the potential breakpoint and step changes due to continuous global warming, CO 2 enrichment, and intensive human activity, thus hiding the identification of underlying causes and ecological indication [14,15].…”

Section: Introductionmentioning

confidence: 99%

Continuously Vegetation Greening over Inner Mongolia for the Past Three Decades

et al. 2021

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The warming climate has rapidly altered vegetation growth in drylands, and consequently, has put great pressure on sustainable livelihoods. Various datasets have been applied from local to global scale to study vegetation dynamics and there is a lack of solid comparison among multiple datasets. Note that vegetation growth might shift over time and the greening and browning components over a long-time span might be masked by a linear trend. Here, we aim to monitor the long-term and nonlinear dynamics in vegetation greenness for Inner Mongolia (an important part of dryland Asia). As a useful tool that indicates vegetation greenness, NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) integrals derived from the GIMMS (Global Inventory Modelling and Mapping Studies) NDVI3g and the GIMMS LAI3g products are applied. During the period of 1982-2016, NDVI/LAI integrals have an overall acceptable consistency in characterizing the trends of vegetation greenness, with NDVI large/small integrals and LAI large/small integrals increase at a rate of 0.96, 1.72, 2.23, and 3.13 per decade, respectively. Inner Mongolia experienced a noticeable greening process (71% and 82% greening area in NDVI large/small integrals, 67% and 73% greening area in LAI large/small integrals), despite the fragmentally distributed browning trends in eastern and partial northern Inner Mongolia. As inferred from nonlinear trend analysis, we found the greening process is still prevalent. The browning of eastern Inner Mongolia under the linear analysis was actually transferring from browning to greening, while the greening trend in northern Inner Mongolia was changing to browning. Increased occurrences in the frequency of breakpoints after 1999 suggest that previously stable vegetation ecology is more sensitive to external disturbances such as altered climatic impact and anthropogenic intervention.

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Section: Introductionmentioning

confidence: 99%

Continuously Vegetation Greening over Inner Mongolia for the Past Three Decades

et al. 2021

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“…In general, the behavior of the PVI is similar to Tasseled Cap greenness and the 2nd Principal Component in its approach. The PVI demonstrates to be less sensitive to the soil emissivity than other vegetation indices, like NDVI [13,15] or SAVI [23].…”

Section: Discussionmentioning

confidence: 85%

Distance-based vegetation indices computed by SAGA GIS: A comparison of the perpendicular and transformed soil adjusted approaches for the LANDSAT TM image

Lemenkova¹

2021

Poljoprivredna tehnika

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Landsat-TM of 2001 covering Iceland (15.5°W-21°W, 64.5°N-67°N) was processed using SAGA GIS for testing distance-based Vegetation Indices (VIs): four approaches of Perpendicular Vegetation Index (PVI) and two approaches of Transformed Soil Adjusted Vegetation Index TSAVI. The PVI of vegetation from the soil background line indicated healthiness as a leaf area index (LAI). The results showed that the reflectance for vegetation has a linear relation with soil background line. Four PVI models and two TSAVI shown coefficients of determination with LAI. The dataset demonstrate variations in the calculated coefficients. The mode in the histograms of the PVI based on four different algorithms show the difference:-7.1,-8.36, 2.78 and 7.0. The dataset for the two approaches of TSAVI: first case ranges in 4.4.-80.6 with a bell-shape mode of a histogram (8.09 to 23.29) for the first algorithm and an irregular shape for the second algorithm with several modes starting from 0.11 to 0.2 and decreasing to 0.26. SAGA GIS permits the calculation of PVI and TSAVI by computed NDVI based on the intersection of vegetation and soil background. Masking the NIR and R, a linear regression of grids was performed using an equation embedded in SAGA GIS. The advantages of the distance-based PVI and TSAVI consists in the adjusted position of pixels on the soil brightness line which refines it comparing to the slope-based VIs. The paper demonstrates SAGA GIS application in agricultural studies.

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“…The NDVI has not been successfully used to detect sowing dates accurately. 34,35 Therefore, the present study used local multiyear average sowing dates. 33 In the present study, sowing to greenup date was defined as phenological stage 1 (P1), green-up to heading date was defined as phenological stage 2 (P2) and heading to maturity date was defined as phenological 3 (P3).…”

Section: Data Preprocessingmentioning

confidence: 99%

Impacts of climate change on winter wheat net primary production: the regulatory role of crop management

Wu,

Gu,

Sun

et al. 2023

J Sci Food Agric

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BackgroundThe Huang–Huai–Hai Plain (3HP) is the main agricultural area in China. Although climate change (CC) and crop management (CM) are considered factors affecting the winter wheat net primary production (NPP) in this region, their effects remain unclear. In this study, we evaluated the relative contributions of CC and CM to winter wheat aboveground NPP (ANPP) in the 3HP and the relationships between climatic factors therein and ANPP by using the first‐order difference method from 2000 to 2020.ResultsCM had a greater influence on the ANPP of winter wheat than did CC. However, the relative contribution of CM to ANPP gradually decreased in humid and dry sub‐humid regions with the development of winter wheat. Furthermore, in areas characterized by low temperatures and limited precipitation, CC became the dominant factor contributing to ANPP, indicating that varieties resilient to drought and cold should be selected in these regions. Minimum and average temperatures were the dominant factors driving spatiotemporal variations in ANPP during the early stage of winter wheat growth, whereas maximum temperature constrained growth throughout the winter wheat growth cycle. When winter wheat entered the vigorous growth stage, precipitation and solar radiation replaced temperature as the driving factors influencing winter wheat growth.ConclusionOur results provide guidance for optimizing winter wheat crop management in the 3HP.This article is protected by copyright. All rights reserved.

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Improved mapping and change detection of the start of the crop growing season in the US Corn Belt from long-term AVHRR NDVI

Cited by 28 publications

References 46 publications

Continuously Vegetation Greening over Inner Mongolia for the Past Three Decades

Continuously Vegetation Greening over Inner Mongolia for the Past Three Decades

Distance-based vegetation indices computed by SAGA GIS: A comparison of the perpendicular and transformed soil adjusted approaches for the LANDSAT TM image

Impacts of climate change on winter wheat net primary production: the regulatory role of crop management

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