Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

Thorp, Kelly R.; Wang, G.; West, A.L.; Moran, M. Susan; Bronson, K. F.; White, Jeffrey W.; Mon, Jarai

doi:10.1016/j.rse.2012.05.013

Cited by 85 publications

(45 citation statements)

References 24 publications

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“…The periodic monitoring of the canopy biophysical parameters, such as biomass, leaf area index (LAI) and plant height (PHT), is essential to understanding crop development, variations in canopy reflectance and net ecosystem exchange or nitrogen and pesticide demand during the growth season [7][8][9]. These parameters and derivatives are important for precision agriculture, remote sensing, crop modeling, ecosystem modeling and climate modeling.…”

Section: Introductionmentioning

confidence: 99%

“…Biophysical parameters may further deliver vital information about the specific infection situation with fungal diseases to make field-specific decisions on plant protection [12]. The availability of this information at the field scale would enable a new generation of decision support systems that can optimize fungicide application in winter wheat, e.g., the prototype "proPlant expert" precisely recommends maximum application rates for up to three management zones within a field according to the yield expectation [7].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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Abstract:Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system-three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px −1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m 2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R 2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R 2 values with the best model obtained at flowering (R 2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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“…Therefore, it is important to estimate wheat growth status and predict wheat yield in a timely and accurate way [2]. The integration of crop models and remote sensing data has become a useful method for monitoring crop growth status and crop yield based on data assimilation over extensive regions [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…The third is the updating method, in which the simulated state variables are continuously renewed whenever remote sensing state variables are available. It is more flexible than the forcing method, but the remote sensing data must be of a higher accuracy than those of the simulated state variables, and this method heavily relies on the selection of the remote sensing data [4,33,35].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data

Jin

Kumar

et al. 2016

Remote Sensing

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Knowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The objective of this study was to use spectral-based biomass values generated from spectral indices to calibrate the AquaCrop model using the particle swarm optimization (PSO) algorithm to improve biomass and yield estimations. Spectral reflectance and concurrent biomass and yield were measured at the Xiaotangshan experimental site in Beijing, China, during four winter wheat-growing seasons. The results showed that all of the measured spectral indices were correlated with biomass to varying degrees. The normalized difference matter index (NDMI) was the best spectral index for estimating biomass, with the coefficient of determination (R 2 ), root mean square error (RMSE), and relative RMSE (RRMSE) values of 0.77, 1.80 ton/ha, and 25.75%, respectively. The data assimilation method (R 2 = 0.83, RMSE = 1.65 ton/ha, and RRMSE = 23.60%) achieved the most accurate biomass estimations compared with the spectral index method. The estimated yield was in good agreement with the measured yield (R 2 = 0.82, RMSE = 0.55 ton/ha, and RRMSE = 8.77%). This study offers a new method for agricultural resource management through consistent assessments of winter wheat biomass and yield based on the AquaCrop model and remote sensing data.

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“…Estimating and evaluating chlorophyll concentration can be helpful for managing forest, agricultural, and grassland ecological systems. Over the past few decades, hyperspectral remote sensing has become a valuable tool for estimating a wide variety of biophysical components (Jacquemoud et al 1995;Villalobos et al 1995;Thorp et al 2012;Avtar et al 2013;Delegido et al 2013) and biochemical components (Curran 1989;Delegido et al 2008;Botha et al 2010;Nichol and Grace 2010;Cheng et al 2013;Rotbart et al 2013). Rapid foliar biochemical concentration determination by remote sensing technology has developed since the 1980s.…”

Section: Introductionmentioning

confidence: 99%

Predicting Cherry Leaf Chlorophyll Concentrations Based on Foliar Reflectance Spectra Variables

Xia

Peng

2014

J Indian Soc Remote Sens

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This paper assesses the capability of hyperspectral remote sensing to estimate chlorophyll a (Chl a), chlorophyll b (Chl b), and chlorophyll a + b (Chl a + b) concentration at cherry leaf scale using a variety of spectral variables during the growth season. A field experiment was conducted in cherry orchard. The leaf reflectance spectra of cherry plants were acquired within 350-1050 nm wavelengths. A variety of spectral variables were mathematically computed based on the leaf spectra and transformation of reflectance spectra. The relationships between all of spectral variables and chlorophyll concentration were discussed. Estimating Chl a, Chl b, and Chl a + b concentration by stepwise linear regression method and curve estimation method were carried out. Results demonstrated that the best spectral variable for prediction of chlorophyll concentration was the new spectral variable (the first derivative of log (1/R 741 ) and D 751 /D 511 ), with the root mean square error prediction (RMSEP) of 4.802 mg L −1 for Chl a concentration, 1.659 mg L −1 for Chl b concentration, and 6.419 mg L −1 for Chl a + b concentration. It should be noted that spectral variables such as D 715 /D 705 , EBFR, D 705 /D 722 , and BND showed a good performance with the RMSEP of 5. 768, 7.838, 12.146, and 14.437 mg L −1 for Chl a concentration, 1.795, 1.985, 1.765, and 3.164 mg L −1 for Chl b concentration, and 7. 935, 11.49, 17.99, and 21.79 mg L −1 for Chl a + b concentration respectively. Further investigation is needed to evaluate the effectiveness of such techniques on other orchard varieties or at the canopy level.

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Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

Cited by 85 publications

References 24 publications

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data

Predicting Cherry Leaf Chlorophyll Concentrations Based on Foliar Reflectance Spectra Variables

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