The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Shen, Jianxiu; Evans, F.

doi:10.3390/rs13112202

Cited by 5 publications

(6 citation statements)

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“…The vegetative index has been identified as an efficient and reliable tool for high‐throughput phenotyping of wheat populations against various biological traits like photosynthesis, green leaf biomass, chlorophyll contents including stress tolerance (Cabrera‐Bosquet et al, 2011; Gitelson et al, 2003; Kizilgeci et al, 2021; Li et al, 2020). Recently, the potential of NDVI metrics has been shown to estimate yield variation in field grown wheat in Western Australia (Shen & Evans, 2021). In addition, NDVI has also been found an effective indicator of vegetation response under terminal drought stress (Bhandari et al, 2021; Condorelli et al, 2018; Naser et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

NDVI and grain fill duration are important to be considered in breeding for terminal heat stress tolerance in wheat

Kumar

Mishra

Chand

et al. 2023

J Agronomy Crop Science

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Terminal heat stress is a major constraint for taking a profitable crop of wheat by small and marginal farmers in the Indo‐Gangetic Plains of south Asia. Hence, breeders remain in constant search for heat‐tolerant genotypes. This study was done with the purpose to find out high‐yielding wheat genotypes that perform stably under terminal heat stress as well as to conclude an easy phenotyping trait for this objective. A cross (HUW 234 × HUW 468) was made using two popular cultivars of the eastern Gangetic Plains of India. HUW 234 carries terminal heat tolerance whereas, HUW 468 is high yielding but suffers from terminal heat stress when planted late. So obtained 167 recombinant inbred lines (RILs) were exposed to both timely sown (TS) and late sown (LS) conditions at three locations for two consecutive years 2016–17 and 2017–18 at Varanasi (Uttar Pradesh), Jabalpur (Madhya Pradesh) and Pusa, Samastipur (Bihar). Combined ANOVA revealed that normalized difference vegetation index (NDVI) at anthesis stage (NDVI_AH) and grain filling duration (GFD) was significant for genotype (G), environment (E) and genotype × environment interaction (GEI). Likewise, differences were significant for grain yield (GY) and 1000‐grain weight (TGW). AMMI analysis revealed similar results. However, NDVI_AH showed low variation under heat stressed late sown conditions. Furthermore, the which‐won‐where model and mean versus stability identified the best performing, above‐average yielding and stable lines across the environments. Correlation among measured traits revealed that NDVI_AH had a significant association with GY in most of the environments. Several lines that performed superior to better parent for GY also showed higher values for NDVI_AH and GFD. The study demonstrates that NDVI_AH and GFD appear to be important traits to be considered while breeding for terminal heat stress tolerance in wheat.

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Section: Discussionmentioning

confidence: 99%

NDVI and grain fill duration are important to be considered in breeding for terminal heat stress tolerance in wheat

Kumar

Mishra

Chand

et al. 2023

J Agronomy Crop Science

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“…Climatic factors, evapotranspiration, water quality, and topography all have an impact on crop growth (Jia et al, 2020). NDVI can be used to examine crop growth and its relationship with various factors to reveal the important factors for intervention and tracking climate adaptation (Phan et al, 2021;Shen and Evans, 2021;Yadav and Geli, 2021;Rigden et al, 2022). For instance, in China, precipitation was found to be the leading cause of agricultural failure over other factors (Peng et al, 2008), whereas Lamchin et al (2018) found temperature to be the most influential factor in vegetation growth in the Asia region.…”

Section: Remote Sensing Technology For Climate Change Adaptation Trac...mentioning

confidence: 99%

“…The impact of climate change on farming systems in Sub-Saharan Africa has also been tracked using an 8 km resolution AVHRR-NDVI (Vrieling et al, 2011). A sequence of Landsat 30-meter resolution NDVI has been used by Shen and Evans (2021) for estimating wheat yields in fields.…”

Section: Options and Challenges For Developing A Biomass Climate Adap...mentioning

confidence: 99%

“…Landsat (Wulder et al, 2019) and Sentinel-2 (Drusch et al, 2012) would be appropriate satellites for this purpose, with spatial resolutions of 30 meters (Wulder et al, 2019) and 10-60 meters (Drusch et al, 2012), respectively. Time-series biomass maps and current crop yield estimates generated using SRS and NDVI have the potential to help various level stakeholders by providing information on how yield varies over time and space and optimizing crop management (Yao et al, 2017;Shen and Evans, 2021). Furthermore, it contributes to advanced crop and environmental analytical tools that assist farmers in implementing the appropriate management practices at the appropriate rates, times, and locations, hence, meeting both economic and environmental targets (Khanal et al, 2017).…”

Section: Potential Barriers Potential Approaches To Overcome Barriersmentioning

confidence: 99%

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Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems?

et al. 2022

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Systematic tools and approaches for measuring climate change adaptation at multiple scales of spatial resolution are lacking, limiting measurement of progress toward the adaptation goals of the Paris Agreement. In particular, there is a lack of adaptation measurement or tracking systems that are coherent (measuring adaptation itself), comparable (allowing comparisons across geographies and systems), and comprehensive (are supported by the necessary data). In addition, most adaptation measurement efforts lack an appropriate counterfactual baseline to assess the effectiveness of adaptation-related interventions. To address this, we are developing a “Biomass Climate Adaptation Index” (Biomass CAI) for agricultural systems, where climate adaptation progress across multiple scales can be measured by satellite remote sensing. The Biomass CAI can be used at global, national, landscape and farm-level to remotely monitor agri-biomass productivity associated with adaptation interventions, and to facilitate more tailored “precision adaptation”. The Biomass CAI places focus on decision-support for end-users to ensure that the most effective climate change adaptation investments and interventions can be made in agricultural and food systems.

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“…It combines spatial smoothing by use of spatial weights with temporal smoothing by growth curve estimation to improve estimation of land surface phenology from Landsat NDVI. Because it is not dependent on individual cells having sufficient cloud-free images within the growing season, SWGC enables phenology detection at more cells than non-spatial approaches which typically exclude cells with insufficient observations (e.g., [44,49]).…”

Section: Introductionmentioning

confidence: 99%

Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning

Evans

Shen

2021

Remote Sensing

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Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to identify the timing of phenological events from sequences of Landsat NDVI and derive phenological and seasonal climate metrics. Using data from a 17,000 ha study area, we investigated the relationships between the metrics and yield over 17 years from 2003 to 2019. We compared six statistical and machine learning models for estimating yield: multiple linear regression, mixed effects models, generalised additive models, random forests, support vector regression using radial basis functions and deep learning neural networks. We used a 50-50 train-test split on paddock-years where 50% of paddock-year combinations were randomly selected and used to train each model and the remaining 50% of paddock-years were used to assess the model accuracy. Using only phenological metrics, accuracy was highest using a linear mixed model with a random effect that allowed the relationship between integrated NDVI and yield to vary by year (R2 = 0.67, MAE = 0.25 t ha−1, RMSE = 0.33 t ha−1, NRMSE = 0.25). We quantified the improvements in accuracy when seasonal climate metrics were also used as predictors. We identified two optimal models using the combined phenological and seasonal climate metrics: support vector regression and deep learning models (R2 = 0.68, MAE = 0.25 t ha−1, RMSE = 0.32 t ha−1, NRMSE = 0.25). While the linear mixed model using only phenological metrics performed similarly to the nonlinear models that are also seasonal climate metrics, the nonlinear models can be more easily generalised to estimate yield in years for which training data are unavailable. We conclude that long-term hindcasts of wheat yield in fields, at 30 m spatial resolution, can be produced using remotely sensed phenology from Landsat NDVI, climate data and machine learning.

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The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Cited by 5 publications

References 46 publications

NDVI and grain fill duration are important to be considered in breeding for terminal heat stress tolerance in wheat

NDVI and grain fill duration are important to be considered in breeding for terminal heat stress tolerance in wheat

Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems?

Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning

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