# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN

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## Abstract

**:**

^{2}= 0.38 and RMSE = 87.97) and SVR (R

^{2}= 0.64 and RMSE = 64.38), the GA-BPNN model (R

^{2}= 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Areas

^{2}with an annual crop yield of 440,900 tons (referencing the Statistical Communiqué of Guangzhou on the 2015 National Economic and Social Development), of which the paddy field was 11,885.16 hm

^{2}with the average paddy stand size of about 0.25 hm

^{2}, accounting for 87.91% of the total cultivated land area. The cultivated land is mainly concentrated in Conghua and Zengcheng Districts. Rice is the principal crop in the study area, with an annual double rotation system (early rice: March–June and late rice: August–November).

#### 2.2. Data

#### 2.3. Methods

#### 2.3.1. Downscaling of MODIS GPP Products Based on the EBK Interpolation

_{i}, ${\lambda}_{i}$, (i = 1,2,3…,n), represents the kriging weight generated using the parameters of cross-variograms, and ${s}_{i}$, (i = 1,2,3…,n) is the kriging weight estimated on the basis of a cross-variogram between ${\mathrm{Z}}_{\left({x}_{i}\right)}$ and ${U}_{\left({x}_{i}\right)}$. The $n$ denotes the total number of observations. The variable ${U}_{\left(x\right)}$ was a standardized rank that was calculated as [30]:

#### 2.3.2. Selecting the Phases of GPP

#### 2.3.3. Partial Least Squares Regression

#### 2.3.4. Support Vector Regression

#### 2.3.5. Genetic Algorithm-Back Propagation Neural Network

## 3. Results

#### 3.1. Downscaling of MODIS GPPs by EBK Interpolation

^{2}for the 500 m, 472.73 g C/m

^{2}for 30 m, and the average field observation was 465.49 g/m

^{2}. Compared with the field observations, the 30 m spatial resolution MODIS GPP has a root mean square error (RMSE) of 7.43 and a normalized RMSE (NRMSE) of 1.59%, while the corresponding values for the 500 m MODIS GPP were 33.43 and 7.18%. The results imply that the downscaled MODIS-GPPs by EBK interpolation can reflect the productivity of cultivated land more accurately than the unscaled MODIS-GPPs.

#### 3.2. Model Comparison for CLQ Evaluation

^{2}) between the estimated and observed CLQ values [45] according to the training and validation samples. The obtained 2011–2015 PLSR evaluation models are:

^{−8}, 2

^{8}) [47]. Moreover, the obtained GA-BPNN models had a three-layer network and a hidden layer with 13 neuron nodes. A total of 1000 iterations was used with 10 maximum runs. Both learning rate and learning objective were 0.01. The mutation probability, crossover probability and population size were respectively 0.1, 0.3, and 10 [36]. The obtained models based on the 294 training samples are compared in Figure 4.

^{2}values and greater RMSE and NRMSE values, then the SVR models and the GA-BPNN models. This indicates that the GA-BPNN models performed best, implying that CLQ was nonlinearly correlated with GPPs.

#### 3.3. Mapping CLQ at the Regional Scale

^{2}, RMSE, and NRMSE values of the predictions from the GA-BPNN model were calculated based on 60 sample data in Aotou and Zhongxin town, respectively (Figure 7). The prediction accuracies with RMSE of 73.32 and 104.35 and NRMSE of 10.47% and 17.75% show that the GA-BPNN model is appropriate to map CLQ at both towns.

## 4. Discussion

^{2}= 0.64 and NRMSE = 9.78%) and GA-BPNN (average R

^{2}= 0.69 and NRMSE = 8.59%) models performed better than the PLSR model (average R

^{2}= 0.38 and NRMSE = 11.55%), implying that there is obvious non-linear correlation of CLQ with GPP spectral indicator. This conclusion is consistent with the findings of previous studies [16], indicating that the non-linear models are appropriate. It was also found that the GA-BPNN models provided more accurate predictions of CLQ than the SVR and PLSR models, which was mainly attributed to the integration of BPNN with GA which has the ability of optimizing the BPNN weights and thresholds. For the SVR models, however, the kernel function and penalty factor used only referenced expert experiences and they were limited in the accuracy of CLQ evaluation [50,51,52].

^{2}of 0.59 and NRMSE of 11.19% [16], the GA-BPNN model proposed in this study shows stronger ability for CLQ evaluation with R

^{2}= 0.69 and NRMSE = 8.59%, implying that the GPP spectral indicator provides a direct and effective means for estimating CLQ. The further application of the GA-BPNN model to mapping CLQ for Aotou Town and Zhongxin Town resulted in NRMSE values of 10.47% and 17.75% based on 120 validation samples. This indicated that the GA-BPNN model proposed in this study had great potential to map CLQ at a large scale.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The study area location in Guangzhou City (

**a**), and Conghua and Zengcheng District (

**b**), respectively, with a total of 420 sample plots for cultivated land quality (CLQ) (training sample plots are in yellow and validation sample plots for the model in purple) and another set of 240 sample plots in red for validating multi-scale Moderate-resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) products; (

**c**,

**d**) the validation areas for mapping at Aotou Town of Conghua District and Zhongxin Town of Zengcheng District.

**Figure 3.**The spatial distribution maps of the cumulative GPP from the 233rd to 289th days in the study area in 2013: (

**a**) 500 m original standard MODIS-GPPs and (

**b**) 30 m MODIS-GPPs interpolated by the Empirical Bayes Kriging (EBK) method.

**Figure 4.**Scatterplots of measured versus estimated CLQ using the training dataset from 2011 to 2015: (

**a**–

**e**) partial least squares regression (PLSR) model; (

**f**–

**j**) support vector regression (SVR) model; (

**k**–

**o**) GA-BPNN model.

**Figure 5.**Scatterplots of measured versus estimated values of CLQ using the validation data set of 126 samples from 2011 to 2015: (

**a**–

**e**) PLSR model; (

**f**–

**j**) SVR model; (

**k**–

**o**) GA-BPNN model.

**Figure 6.**Spatial distributions of the predicted CLQ in 2013 using the GA-BPNN model for the study area: (

**a**) Aotou Town and (

**b**) Zhongxin Town.

**Figure 7.**Measured and estimated CLQ in 2013 using the GA-BPNN model with the 120 validation sample plots for mapping: (

**a**) Aotou Town and (

**b**) Zhongxin Town.

Growth Stage | Tillering Stage | Jointing Stage | Heading Stage | Maturity Stage |
---|---|---|---|---|

Acquisition date (m/d/y) | 8/20/2011–8/27/2011 | 9/13/2011–9/20/2011 | 10/15/2011–10/22/2011 | 11/8/2011–11/15/2011 |

8/19/2012–8/26/2012 | 9/12/2012–9/19/2012 | 10/14/2012–10/21/2012 | 11/7/2012–11/14/2012 | |

8/20/2013–8/27/2013 | 9/13/2013–9/20/2013 | 10/15/2013–10/22/2013 | 11/8/2013–11/15/2013 | |

8/20/2014–8/27/2014 | 9/13/2014–9/20/2014 | 10/15/2014–10/22/2014 | 11/8/2014–11/15/2014 | |

8/20/2015–8/27/2015 | 9/13/2015–9/20/2015 | 10/15/2015–10/22/2015 | 11/8/2015–11/15/2015 |

**Table 2.**Comparison of the original 500 m spatial resolution MODIS-GPPs from the 289th day in 2013 with their downscaled 30 m products by the Empirical Bayes Kriging (EBK) method based on the dry biomass field observations of 30 sample plots.

Plot# | Field Observations | 30 m MODIS-GPPs | 500 m MODIS-GPPs | ||
---|---|---|---|---|---|

Estimates | Absolute Error (%) | Estimates | Absolute Error (%) | ||

1 | 514.23 | 521.95 | 1.50 | 530.31 | 3.13 |

2 | 485.68 | 496.85 | 2.30 | 485.82 | 0.03 |

3 | 519.91 | 529.79 | 1.90 | 525.33 | 1.04 |

4 | 685.27 | 688.70 | 0.50 | 731.61 | 6.76 |

5 | 538.52 | 546.06 | 1.40 | 555.16 | 3.09 |

6 | 592.24 | 599.94 | 1.30 | 609.87 | 2.98 |

7 | 639.33 | 645.08 | 0.90 | 571.20 | 10.66 |

8 | 402.12 | 411.77 | 2.40 | 407.59 | 1.36 |

9 | 437.78 | 445.66 | 1.80 | 447.50 | 2.22 |

10 | 451.55 | 460.13 | 1.90 | 490.44 | 8.61 |

11 | 555.35 | 560.35 | 0.90 | 598.75 | 7.81 |

12 | 299.14 | 307.52 | 2.80 | 352.92 | 17.98 |

13 | 317.47 | 325.09 | 2.40 | 330.34 | 4.05 |

14 | 506.90 | 515.01 | 1.60 | 520.97 | 2.78 |

15 | 408.60 | 416.77 | 2.00 | 447.72 | 9.57 |

16 | 438.98 | 446.44 | 1.70 | 415.71 | 5.30 |

17 | 457.91 | 464.78 | 1.50 | 446.98 | 2.39 |

18 | 448.84 | 456.02 | 1.60 | 450.13 | 0.29 |

19 | 393.27 | 399.95 | 1.70 | 409.76 | 4.19 |

20 | 494.46 | 501.87 | 1.50 | 459.74 | 7.02 |

21 | 394.18 | 401.67 | 1.90 | 395.16 | 0.25 |

22 | 379.54 | 386.38 | 1.80 | 389.23 | 2.55 |

23 | 425.62 | 431.58 | 1.40 | 431.13 | 1.29 |

24 | 380.76 | 387.62 | 1.80 | 383.95 | 0.84 |

25 | 567.61 | 572.15 | 0.80 | 682.55 | 20.25 |

26 | 401.86 | 410.30 | 2.10 | 370.57 | 7.79 |

27 | 539.04 | 543.35 | 0.80 | 541.45 | 0.45 |

28 | 541.05 | 546.46 | 1.00 | 509.82 | 5.77 |

29 | 364.34 | 372.00 | 2.10 | 368.16 | 1.05 |

30 | 383.00 | 390.66 | 2.00 | 392.90 | 2.59 |

Mean | 465.49 | 472.73 | 1.64 | 475.09 | 4.80 |

Stdev | 91.77 | 91.00 | 97.57 | ||

RMSE | 7.43 | 33.43 | |||

NRMSE (%) | 1.59 | 7.18 |

Growth Stages | Tillering | Jointing | Heading | Maturity | |
---|---|---|---|---|---|

Years | |||||

2011 | 1.971 | 1.981 | 4.611 | 2.874 | |

2012 | 1.407 | 2.687 | 4.130 | 3.451 | |

2013 | 1.274 | 4.092 | 7.679 | 4.468 | |

2014 | 1.421 | 1.667 | 3.257 | 3.448 | |

2015 | 2.073 | 1.699 | 2.655 | 1.026 |

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**MDPI and ACS Style**

Zhu, M.; Liu, S.; Xia, Z.; Wang, G.; Hu, Y.; Liu, Z.
Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN. *Agriculture* **2020**, *10*, 318.
https://doi.org/10.3390/agriculture10080318

**AMA Style**

Zhu M, Liu S, Xia Z, Wang G, Hu Y, Liu Z.
Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN. *Agriculture*. 2020; 10(8):318.
https://doi.org/10.3390/agriculture10080318

**Chicago/Turabian Style**

Zhu, Mingbang, Shanshan Liu, Ziqing Xia, Guangxing Wang, Yueming Hu, and Zhenhua Liu.
2020. "Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN" *Agriculture* 10, no. 8: 318.
https://doi.org/10.3390/agriculture10080318