Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm

Nguy-Robertson, Anthony L.; Yi, Peng; Gitelson, Anatoly A.; Arkebauer, Timothy J.; Pimstein, Agustin; Herrmann, Ittai; Karnieli, Arnon; Rundquist, Donald C.; Bonfil, David J.

doi:10.1016/j.agrformet.2014.03.004

Cited by 145 publications

(89 citation statements)

References 51 publications

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“…700 nm and 800 nm were located in the red edge region and the near-infrared range, respectively. Previous studies showed that the red edge wavebands index (680 nm-760 nm) were sensitive to the LAI and chlorophyll [13,[44][45][46][47], and the near-infrared short-wave (780 nm-1100 nm) was sensitive to the structure and water, which can deeply explore the canopy under higher biomass status [48,49]. Therefore, that was why MTVI 2 had strong performance in this paper.…”

Section: Performance Of Diverse Vis On Estimating Wheat Laimentioning

confidence: 94%

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

et al. 2017

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Leaf area index (LAI) is a significant biophysical variable in the models of hydrology, climatology and crop growth. Rapid monitoring of LAI is critical in modern precision agriculture. Remote sensing (RS) on satellite, aerial and unmanned aerial vehicles (UAVs) has become a popular technique in monitoring crop LAI. Among them, UAVs are highly attractive to researchers and agriculturists. However, some of the UAVs vegetation index (VI)-derived LAI models have relatively low accuracy because of the limited number of multispectral bands, especially as they tend to saturate at the middle to high LAI levels, which are the LAI levels of high-yielding wheat crops in China. This study aims to effectively estimate wheat LAI with UAVs narrowband multispectral image (400-800 nm spectral regions, 10 cm resolution) under varying growth conditions during five critical growth stages, and to provide the potential technical support for optimizing the nitrogen fertilization. Results demonstrated that the newly developed LAI model with modified triangular vegetation index (MTVI 2 ) has better accuracy with higher coefficient of determination (R c 2 = 0.79, R v 2 = 0.80) and lower relative root mean squared error (RRMSE = 24%), and higher sensitivity under various LAI values (from 2 to 7), which will broaden the applied range of the new LAI model. Furthermore, this LAI model displayed stable performance under different sub-categories of growth stages, varieties, and eco-sites. In conclusion, this study could provide effective technical support to precisely monitor the crop growth with UAVs in various crop yield levels, which should prove helpful in family farm for the modern agriculture.

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Section: Performance Of Diverse Vis On Estimating Wheat Laimentioning

confidence: 94%

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

et al. 2017

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“…The physical models are complex, thus, only a few previous studies conducted crop biomass estimation using the inversion of physical models [11,12]. In contrast, spectral vegetation indices (VIs) were commonly used to estimate crop biophysical variables due to the simplicity and practicality [13][14][15][16][17]. However, the estimation accuracy of crop biomass is usually affected by background soil reflectance, atmospheric and water absorption using broadband VIs.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Wang

Nie

et al. 2016

Remote Sensing

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Abstract:The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H CV ) of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H CV combined with H mean performed better than one LiDAR-derived metric alone in biomass estimation (R 2 = 0.835, RMSE = 374.655 g/m 2 , RMSE CV = 393.573 g/m 2 ). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R 2 = 0.883, RMSE = 321.092 g/m 2 , RMSE CV = 337.653 g/m 2 ) compared with LiDAR or hyperspectral data alone.

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“…We automatically geo-referenced the remaining images to the manually geo-referenced image using image-to-image registration with an affine transformation in ENVI. Finally, we calculated the green chlorophyll vegetation index (GCVI; Equation (1)) for each image because previous studies have shown that GCVI has a fairly linear relationship with wheat leaf area index (LAI; [32]). …”

Section: Approachmentioning

confidence: 99%

“…We use similar methods to those derived in Lobell et al (2015) to test whether this approach, called SCYM, may accurately map the yields of smallholder farms. With this method, a suite of crop models are run that encompass variation in management across the study region, and then daily LAI outputs from the crop models are converted into daily vegetation indices (VIs) using field-tested and calibrated equations that relate VIs to crop LAI (e.g., [32]). We then create linear models where we regress simulated yield on simulated VIs for the dates that we have satellite imagery.…”

Section: Crop Model Datamentioning

confidence: 99%

Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data

et al. 2016

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Abstract:Remote sensing offers a low-cost method for developing spatially continuous crop production statistics across large areas and through time. Nevertheless, it has been difficult to characterize the production of individual smallholder farms, given that the land-holding size in most areas of South Asia (<2 ha) is smaller than the spatial resolution of most freely available satellite imagery, like Landsat and MODIS. In addition, existing methods to map yield require field-level data to develop and parameterize predictive algorithms that translate satellite vegetation indices to yield, yet these data are costly or difficult to obtain in many smallholder systems. To overcome these challenges, this study explores two issues. First, we employ new high spatial (2 m) and temporal (bi-weekly) resolution micro-satellite SkySat data to map sowing dates and yields of smallholder wheat fields in Bihar, India in the 2014-2015 and 2015-2016 growing seasons. Second, we compare how well we predict sowing date and yield when using ground data, like crop cuts and self-reports, versus using crop models, which require no on-the-ground data, to develop and parameterize prediction models. Overall, sow dates were predicted well (R 2 = 0.41 in 2014-2015 and R 2 = 0.62 in 2015-2016), particularly when using models that were parameterized using self-report sow dates collected close to the time of planting and when using imagery that spanned the entire growing season. We were also able to map yields fairly well (R 2 = 0.27 in 2014-2015 and R 2 = 0.33 in 2015-2016), with crop cut parameterized models resulting in the highest accuracies. While less accurate, we were able to capture the large range in sow dates and yields across farms when using models parameterized with crop model data and these estimates were able to detect known relationships between management factors (e.g., sow date, fertilizer, and irrigation) and yield. While these results are specific to our study site in India, it is likely that the methods employed and the lessons learned are applicable to smallholder systems more generally across the globe. This is of particular interest given that similar high spatio-temporal resolution micro-satellite data will become increasingly available in the coming years.

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Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm

Cited by 145 publications

References 51 publications

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data

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