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

Jin, Xiuliang; Kumar, Lalit; Li, Zhenhai; Xu, Xingang; Yang, Guijun; Wang, Jihua

doi:10.3390/rs8120972

Cited by 88 publications

(43 citation statements)

References 53 publications

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“…These dry matter indices expectedly performed better than the chlorophyll indices for the estimation of total biomass. A recent study by Jin et al [18] showed the better performance of the NDMI for biomass estimation than a few chlorophyll indices, but did not consider the ND LMA . Gnyp et al [27] determined (R 1301 − R 1706 )/(R 1301 + R 1706 ), of which the two bands were approximately 20 nm offset from their counterparts in the ND LMA , as their best wavelength combination for the estimation of the total biomass in rice.…”

Section: Why Did Dry Matter Indices Work Better Than Chlorophyll Indimentioning

confidence: 99%

Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices

Cheng

Song

et al. 2017

Remote Sensing

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Abstract:Crop biomass is a critical variable for characterizing crop growth development, understanding dry matter partitioning, and predicting grain yield. Previous studies on the spectroscopic estimation of crop biomass focused on the use of various spectral indices based on chlorophyll absorption features and found that they often became saturated at high biomass levels. Given that crop biomass is commonly expressed as the dry weight of canopy components per unit ground area, it may be better estimated using the spectral indices that directly characterize dry matter absorption. This study aims to evaluate a group of four dry matter indices (DMIs) by comparison with a group of four chlorophyll indices (CIs) for estimating the biomass of individual components (e.g., leaves, stems) and their combinations with the field data collected from a two-year rice cultivation experiment. The Red-edge Chlorophyll Index (CI Red-edge ) of the CI group exhibited the best relationship with leaf biomass (R 2 = 0.82) for the whole growing season and with total biomass (R 2 = 0.81), but only for the growth stages before heading. However, the Normalized Difference Index for Leaf Mass per Area (ND LMA ) of the DMI group showed the best relationships with both stem biomass (R 2 = 0.81) and total biomass (R 2 = 0.81) for the whole season. This research demonstrated the suitability of dry matter indices and provided physical explanations for the superior performance of dry matter indices over chlorophyll indices for the estimation of whole-season total biomass.

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Section: Why Did Dry Matter Indices Work Better Than Chlorophyll Indimentioning

confidence: 99%

Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices

Cheng

Song

et al. 2017

Remote Sensing

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“…These models to estimate the AGB use the original VIs and are validated by using VIs with noise added. In these tests, the signal-to-noise ratio (SNR) is set to 5,10,15,20,30, and 50 (the noise increases with decreasing SNR). We also compare the results to those obtained with noiseless VIs.…”

Section: Noise Simulationmentioning

confidence: 99%

“…One such variable, above-ground biomass (AGB), plays an important role in plant functioning because it reflects the status of crop growth and is related to solar-energy consumption, yield, and grain quality [3,4]. Therefore, AGB is considered as one of the most important crop biophysical parameters, and its accurate estimation can help improve crop monitoring and yield prediction [5]. Traditional AGB estimates are based on destructive measurements, which are not only time and labor consuming, but more importantly, are difficult to apply over large areas [6].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R 2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models.

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“…In their work, Jin et al [112] used FAOs' AquaCrop model and calibrated using spectral-based biomass values generated from fifteen spectral indices and particle swarm optimization (PSO). The primary goal of their work was to improve yield and biomass prediction of winter wheat yield in China, by assimilating field spectroscopic data into AquaCrop model with PSO algorithm.…”

Section: Limitations and Advantages Of Using Remote Sensing Data Withmentioning

confidence: 99%

Contribution of Remote Sensing on Crop Models: A Review

et al. 2018

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Abstract:Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.

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

Cited by 88 publications

References 53 publications

Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices

Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Contribution of Remote Sensing on Crop Models: A Review

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