Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

Sun, Ling; Gao, Feng; Anderson, Martha C.; Kustas, William P.; Alsina, María Mar; Sánchez, Luis A.; Sams, B.; McKee, L.; Dulaney, W.; White, William A.; Alfieri, Joseph G.; Prueger, John H.; Melton, F. S.; Post, Kirk

doi:10.3390/rs9040317

Cited by 149 publications

(133 citation statements)

References 45 publications

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“…A quantitative analysis of partitioning accuracy will require direct measurements of T and E, obtained e.g., using sap flow, micro-lysimeter and micro-Bowen ratio techniques Water Resources Research 10.1002/2017WR020700 [Agam et al, 2012;Holland et al, 2013] or eddy covariance flux-variance similarity theory [Scanlon and Sahu, 2008;Scanlon and Kustas, 2012]. Incorporating metrics of crop progress generated using a fusion of Landsat/MODIS surface reflectance time series [Gao et al, 2017;Sun et al, 2017a], will enable an assessment of crop water use efficiency and water availability deficits during critical phases of phenological development.…”

Section: E and T Partitioning Capabilitiesmentioning

confidence: 99%

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Sun

Anderson

Gao

et al. 2017

Water Resources Research

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The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step ‐ an improvement from 27% errors using standard Landsat‐only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed‐scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay.

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Section: E and T Partitioning Capabilitiesmentioning

confidence: 99%

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Sun

Anderson

Gao

et al. 2017

Water Resources Research

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“…Due to the link between net photosynthesis and steady-state fluorescence at airborne spectral level, field-spectral imaging is a very useful technology for crop growth monitoring and real-time management at field scale [19][20][21]. Vegetation indices (VIs) derived from active or passive sensors have been used to distinguish temporal patterns in crop development [22][23][24][25][26]. For example, VIs have been used for detecting N stress in maize at early development stages (V4-V7 stage, 4-7 leaves with visible leaf collar), though with relatively low accuracy [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

2018

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Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green-and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12-VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0-30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYON r ) (216.8 kg N ha −1 ), but underestimated the grain yield optimum N available (GYON a ) (248.6 kg N ha −1 ). Red-based VIs slightly overestimated the GYON r and GYON a , while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EON r ) and economic optimum nitrogen available (EON a ) were determined below the GYON r and the GYON a , demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments.

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“…Basically, significant differences in plant reflectances and energy emission in the optical wavelengths, particularly the red (R) and near-infrared (NIR) region, defined as the range between 700 and 1300 nm [7] due to biochemical plant constitutes such as chlorophyll, have resulted in numerous VI formulas [8]. While the performance of VI-based models has been promising, these indices have generally been developed for uniformly distributed canopies, and are thus not as reliable in estimating plant biomass/Leaf Area Index (LAI) for strongly clumped and uniquely structured canopies such as vineyards [9].A saturation issue occurs with well-developed canopies, wherein, despite significant increases in biomass parameters (and as a result LAI), VI values become saturated, meaning they plateau at a maximum value and are no longer sensitive to increases in LAI [10,11]. Thus, VIs are recommended to be used only in early growing stages in denser canopies [12].…”

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Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

et al. 2019

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In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration Remote Sens. 2020, 12, 50 2 of 34 eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.Evapotranspiration (ET) is one of the key components in water and energy cycles, and its quantification is essential to increasing crop water use efficiency [1]. However, estimation of ET using physically-based models is not a straightforward process due to input requirements and model complexity [2]. The degree of complexity increases with non-homogeneous landscapes where both soil and ...

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Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

Cited by 149 publications

References 45 publications

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

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