Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages

Author(s):  
Rui Dong ◽  
Yuxin Miao ◽  
Xinbing Wang ◽  
Fei Yuan ◽  
Krzysztof Kusnierek
2020 ◽  
Vol 12 (8) ◽  
pp. 1290 ◽  
Author(s):  
Xu Ma ◽  
Tiejun Wang ◽  
Lei Lu

In modeling the canopy reflectance of row-planted crops, neglecting horizontal radiative transfer may lead to an inaccurate representation of vegetation energy balance and further cause uncertainty in the simulation of canopy reflectance at larger viewing zenith angles. To reduce this systematic deviation, here we refined the four-stream radiative transfer equations by considering horizontal radiation through the lateral “walls”, considered the radiative transfer between rows, then proposed a modified four-stream (MFS) radiative transfer model using single and multiple scattering. We validated the MFS model using both computer simulations and in situ measurements, and found that the MFS model can be used to simulate crop canopy reflectance at different growth stages with an accuracy comparable to the computer simulations (RMSE < 0.002 in the red band, RMSE < 0.019 in NIR band). Moreover, the MFS model can be successfully used to simulate the reflectance of continuous (RMSE = 0.012) and row crop canopies (RMSE < 0.023), and therefore addressed the large viewing zenith angle problems in the previous row model based on four-stream radiative transfer equations. Our results demonstrate that horizontal radiation is an important factor that needs to be considered in modeling the canopy reflectance of row-planted crops. Hence, the refined four-stream radiative transfer model is applicable to the real world.


2016 ◽  
Vol 179 (4) ◽  
pp. 488-498 ◽  
Author(s):  
Qingnan Chu ◽  
Toshihiro Watanabe ◽  
Takuro Shinano ◽  
Takuji Nakamura ◽  
Norikuni Oka ◽  
...  

2020 ◽  
Vol 12 (21) ◽  
pp. 3600
Author(s):  
Xu Ma ◽  
Yong Liu

The canopy reflectance model is the physical basis of remote sensing inversion. In canopy reflectance modeling, the geometric optical (GO) approach is the most commonly used. However, it ignores the description of a multiple-scattering contribution, which causes an underestimation of the reflectance. Although researchers have tried to add a multiple-scattering contribution to the GO approach for forest modeling, different from forests, row crops have unique geometric characteristics. Therefore, the modeling approach originally applied to forests cannot be directly applied to row crops. In this study, we introduced the adding method and mathematical solution of integral radiative transfer equation into row modeling, and on the basis of improving the overlapping relationship of the gap probabilities involved in the single-scattering contribution, we derived multiple-scattering equations suitable for the GO approach. Based on these modifications, we established a row model that can accurately describe the single-scattering and multiple-scattering contributions in row crops. We validated the row model using computer simulations and in situ measurements and found that it can be used to simulate crop canopy reflectance at different growth stages. Moreover, the row model can be successfully used to simulate the distribution of reflectances (RMSEs < 0.0404). During computer validation, the row model also maintained high accuracy (RMSEs < 0.0062). Our results demonstrate that considering multiple scattering in GO-approach-based modeling can successfully address the underestimation of reflectance in the row crops.


HortScience ◽  
1997 ◽  
Vol 32 (3) ◽  
pp. 519A-519
Author(s):  
C.A. Sanchez ◽  
M. Wilcox ◽  
J.L. Aguiar ◽  
K.S. Mayberry

Twenty field experiments were conducted to evaluate the response of iceberg lettuce (Lactuca sativa L.) to N and evaluate various diagnostic technologies as tools for assessing the N nutritional status of lettuce. Lettuce yields showed a curvilinear response to N in most experiments. Generally, the dry midrib nitrate-N test and the sap nitrate-N test appear to be sensitive indicators of the N nutritional status of lettuce after the folding stage of growth. The chlorophyll meter was not a sensitive indicator of the N nutritional status of lettuce. Preliminary data also show that canopy reflectance, including digital analysis of aerial photographs, is correlated to N nutritional status of lettuce. However, reflectance technologies do not readily distinguish between N deficiencies and other factors (insects, diseases, water stress, etc.) that affect plant biomass and color. Because plant tests do not appear to be sensitive indicators of N nutrition during early growth stages (before folding), a post-thinning (and pre-sidedress) soil nitrate-N test is currently being evaluated.


2019 ◽  
Vol 11 (11) ◽  
pp. 1331 ◽  
Author(s):  
Fenling Li ◽  
Li Wang ◽  
Jing Liu ◽  
Yuna Wang ◽  
Qingrui Chang

Leaf nitrogen concentration (LNC) is an important indicator for accurate diagnosis and quantitative evaluation of plant growth status. The objective was to apply a discrete wavelet transform (DWT) analysis in winter wheat for the estimation of LNC based on visible and near-infrared (400–1350 nm) canopy reflectance spectra. In this paper, in situ LNC data and ground-based hyperspectral canopy reflectance was measured over three years at different sites during the tillering, jointing, booting and filling stages of winter wheat. The DWT analysis was conducted on canopy original spectrum, log-transformed spectrum, first derivative spectrum and continuum removal spectrum, respectively, to obtain approximation coefficients, detail coefficients and energy values to characterize canopy spectra. The quantitative relationships between LNC and characteristic parameters were investigated and compared with models established by sensitive band reflectance and typical spectral indices. The results showed combining log-transformed spectrum and a sym8 wavelet function with partial least squares regression (PLS) based on the approximation coefficients at decomposition level 4 most accurately predicted LNC. This approach could explain 11% more variability in LNC than the best spectral index mSR705 alone, and was more stable in estimating LNC than models based on random forest regression (RF). The results indicated that narrowband reflectance spectroscopy (450–1350 nm) combined with DWT analysis and PLS regression was a promising method for rapid and nondestructive estimation of LNC for winter wheat across a range in growth stages.


Agronomy ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 201 ◽  
Author(s):  
Qiang Cao ◽  
Yuxin Miao ◽  
Jianning Shen ◽  
Fei Yuan ◽  
Shanshan Cheng ◽  
...  

Active crop canopy sensors can be used for non-destructive real-time diagnosis of crop nitrogen (N) status and guiding in-season N management. However, limited studies have compared the performances of two commercially available sensors with three different wavebands: Crop Circle ACS-470 (CC-470) and Crop Circle ACS-430 (CC-430). The objective of this study was to evaluate the performances of CC-470 and CC-430 sensors for estimating winter wheat (Triticum aestivum L.) N status at different measurement heights (40 cm, 70 cm and 100 cm) and growth stages. Results indicated that the canopy reflectance values of CC-470 were more affected by height compared to the CC-430 sensor. The normalized difference red edge (NDRE) and red edge chlorophyll index (CIRE) of CC-430 were stable at the three different measuring heights. The relationships between these indices and the N status indicators were stronger at the Feekes 9–10 stages than the Feekes 6–7 stages for both sensors; however, the CC-430 sensor-based vegetation indices had higher coefficient of determination (R2) values for both stages. It is concluded that the CC-430 sensor is more reliable than CC-470 for winter wheat N status estimation due to its capability of making height-independent measurements. These results demonstrated the importance of considering the influences of height when using active canopy sensors in field measurements.


2006 ◽  
Vol 30 (4) ◽  
pp. 675-681 ◽  
Author(s):  
XUE Li_Hong ◽  
◽  
LU Ping ◽  
YANG Lin_Zhang ◽  
SHAN Yu_Hua ◽  
...  

Crop Science ◽  
1995 ◽  
Vol 35 (5) ◽  
pp. 1400-1405 ◽  
Author(s):  
I. Filella ◽  
L. Serrano ◽  
J. Serra ◽  
J. Peñuelas

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