# Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{2}) value of 0.847 [25]. Compared with a single time image, more information on time series images can be mined, which is helpful to obtain phenological information and reduce classification errors [2,9]. In southern China, many crops exist during the growing stage of rice, resulting in a complex planting structure. Crops with the corresponding period of rice have similar phenological dynamics and vegetation cover change patterns, which will lead to confusion between crops and rice. For various crops, trends of red-edge bands differ in the same growth period. Therefore, it can be considered to distinguish rice from crops by using the variation of red-edge bands in different growth periods. At present, it is rarely used to map rice with red-edge bands.

## 2. Materials and Data Processing

#### 2.1. Study Area

#### 2.2. Data and Preprocessing

#### 2.2.1. GF-6/GF-1 Data and Preprocessing

#### 2.2.2. UAV Image Data and Preprocessing

_{λ}and DN

_{λ}were the surface reflectance and the digital number of a pixel at wavelength λ, respectively; G

_{λ}and B

_{λ}were gains and bias at wavelength λ, respectively. For each wavelength λ, G

_{λ}and B

_{λ}can be calculated using the least-square method by R and DN values (referring to DN

_{0.03}, DN

_{0.06}, DN

_{0.12}, DN

_{0.24}, DN

_{0.36}, DN

_{0.48}, DN

_{0.56}, and DN

_{0.80}) of eight calibration targets.

#### 2.3. Ancillary Data

^{2}. There are 48 rice hybrid species recommended by breeding experts. All rice cultivars were sown on 11 May 2019 and transplanted on 8 June 2019 with the transplanting density of 15,000 plants/ha. The planting density, nitrogen rate, and water rate of these plots were the same. A protected planting area with a width of 2 m was provided around the rice plot. From 26 June 2019 to 3 September 2019, destructive samplings were conducted every five days, and a total of 13 times were carried out for each plot during the whole growing period. The sampling time is consistent with the flight time of UAV. For each plot, three bundles were randomly dug out from soil with root, placed in a bucket full of water, and taken to the laboratory. The root of the plant was cut, the remaining part of the plant was cleaned and divided into stems, leaves, and ears. All the samples were dried for half an hour at 105 °C and later dried at 80 °C until their weights remained unchanged. All samples were weighed and recorded, and the AGB (g·m

^{−2}) per square meter was calculated.

## 3. Methodology

#### 3.1. Non-Cropland Masks

_{555nm}, R

_{660nm}, and R

_{830nm}are the reflectance corresponding to the central wavelength of 550 nm, 660 nm, and 830 nm, respectively.

#### 3.2. Red-Edge Decision Tree Method Based on Time Series

#### 3.2.1. Phenological Analysis of Rice, Corn, and Soybean

#### 3.2.2. Red-Edge Decision Tree Classification

_{t}represents the spectral integral value of the image at time t from the central wavelength of 704 nm to the central wavelength of 830 nm. S

_{t}represents the spectral integral value of the image at time t from the central wavelength of 660 nm to the central wavelength of 830 nm. VI_S

_{ti~tj}represents the integral value of vegetation index (VI) from time t

_{i}to t

_{j}, VI

_{i}, VI

_{j}represents the vegetation index values corresponding to t

_{i}and t

_{j,}respectively. R

_{704nm}and R

_{752nm}are the reflectance corresponding to the central wavelength of 704 nm and 752 nm, respectively.

#### 3.3. Methods of the NDVI, EVI, and NDWI Time Series

_{12 August~20 August}< 4.3, EVI_S

_{12 August~20 August}< 3 (corn is masked), NDWI

_{04 June}− NDWI

_{20 A}

_{ugust}< 0.4, S

_{27 July}< 32 (soybean is masked) (Figure 6a). The condition of the NNE method based on GF-1 by using histogram threshold is set as S

_{15 August}< 32, EVI_S

_{02 August~12 September}< 15 (corn is masked), NDWI

_{02 June}− NDWI

_{15 A}

_{ugust}< 0.35, NDVI_S

_{02 August~12 September}< 23, EVI_S

_{02 August~12 September}< 20 soybean is masked)(Figure 6b).

#### 3.4. Results Validation

#### 3.5. Rice Growth Monitoring Based on Red-Edge Band

## 4. Results

#### 4.1. Analysis of Red-Edge Characteristics of Different Crops

_{27 July}> 35, RE_S

_{12 August}> 38 and the corn was masked by setting conditions RE_S

_{20 August}< 34, NDRE_S

_{12 August~20 August}< 3.6, NDREI_S

_{12 August~20 August}< 3.1. The remaining crop is rice through the implementation of the above process.

#### 4.2. Mapping Paddy Rice Using REDT Method and Accuracy Assessment

#### 4.3. Adaptability Verification of REDT Method

#### 4.4. Rice Growth Monitoring

^{2}value of 0.90. Therefore, we can apply this vegetation index model to the GF-6 image (pre-heading stage), and use this model to get simulated AGB of rice. The red-edge vegetation index of rice calculated by the GF-6 image is fitted with the simulated AGB, and the results are shown in Figure 13.

^{2}value ranged from 0.56 to 0.62. The single vegetation index model cannot estimate AGB well. Therefore, we use the PLSR model combined with multiple VIs, in which 280 sets of data were used for modeling and 152 sets of data were used for verification. The fitting results between the measured AGB and estimated AGB are shown in Figure 15, and the R

^{2}was 0.82, which is much better than the single vegetation index model. The model was applied to GF-6 images (post heading stage), and the simulated AGB was obtained. The red edge vegetation index of rice calculated by the GF-6 image is fitted with the simulated AGB, and the results are shown in Figure 16. NREDI performed best with a R

^{2}value of 0.84. The CI, NDRE, and EVI calculated by the red edge1 band are better than those calculated by the red edge2 band. The TCI and TCARI calculated by the red edge2 band are better than those calculated by the red edge1 band. This result is the same as that obtained at the pre-heading stage.

## 5. Discussion

#### 5.1. Advantages of REDT Method in Rice Mapping Strategy

#### 5.2. Application of Red-Edge Band in Rice Growth Monitoring

_{red-edge}) and AGB is good, and the highest R

^{2}was 0.90. The change rate of reflectance from the red-edge band to the near-infrared band is significantly higher than that from the red band to the near-infrared band (Figure 7a). Therefore, we consider whether the two red-edge bands of GF-6 could be used to monitor the growth of rice. The fitting effect of the red-edge vegetation index and AGB is better than that of the non-red-edge vegetation index, which indicates that the red-edge band plays an important role in rice growth monitoring. In this paper, our objection is to use GF-6 images for large-area rice growth monitoring. The spatial resolution of the GF-6 image is 16 m, so it is time-consuming and labor-consuming to measure AGB and chlorophyll in the field. How to explore a method to simulate the corresponding AGB of rice is very meaningful. The best model obtained from UAV growth monitoring results was applied to the GF-6 image, and the corresponding AGB of rice on the GF-6 image was obtained. In the study of rice growth monitoring, vegetation index constructed from visible light band to near-infrared band was mostly used [49,50,51]. Based on the GF-6 image, the red-edge vegetation index was used to monitor rice growth.

^{2}of the vegetation index model constructed by the two bands is not as good as the vegetation index model constructed by the red-edge band1 and near-infrared band (Figure 13 and Figure 16). The vegetation index calculated by the two red-edge bands (NREDI) is effective in monitoring rice growth. The results show that the two red-edge bands of the GF-6 image can be well applied to rice growth monitoring research, which provides a new research method and theoretical basis for large area rice mapping and growth monitoring.

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 2.**The illustration of (

**a**) rice field photographed by UAV, 48 rice plots (

**b**) eight calibration ground targets (

**c**) UAV (

**d**) Mini-MCA.

**Figure 5.**Phenological calendars for the main crop types, and the dates of the selected GF-6 images.

**Figure 6.**Normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) time series of rice, corn, and soybean-based on GF-6 (

**a**) and GF-1 (

**b**).

**Figure 7.**The spectral curves of soybean, corn, and rice in different periods (

**a**) and Normalized Red-edge Difference Index (NREDI) time series and NDRE time series (

**b**), the dates are 4 June 2019, 27 July 2019, 12 August 2019, 30 September 2019 respectively.

**Figure 8.**Threshold histogram of integral value from red-edge band to near-infrared band on (

**a**) 27 July 2019; (

**b**) 12 August 2019; (

**c**) 20 August 2019; and the integral value of red-edge vegetation index based on time series (

**d**) NDRE_S

_{12 August~20 August}(

**e**) NDREI_S

_{12 August~20 August}for three croplands.

**Figure 9.**The comparison of three local paddy rice maps in A and B region ((

**a**) A-GF-6 image; (

**b**) A-red-edge decision tree (REDT) method based on GF-6 rice map; (

**c**) A-normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (NNE) method based on GF-6 rice map; (

**d**) A-NNE method based on GF-1 rice map; (

**e**) B-GF-6 image; (

**f**) B-REDT method based on GF-6 rice map; (

**g**) B-NNE method based on GF-6 rice map; (

**h**) B-NNE method based on GF-1 rice map).

**Figure 10.**The rice mapping of Jingzhou City ((

**a**) REDT method based on GF-6 rice map; (

**b**) NNE method based on GF-6 rice map; (

**c**)-NNE method based on GF-1 rice map).

**Figure 11.**The comparison of three local paddy rice maps of Tianmen city ((

**a**) GF-6 image; (

**b**) REDT method based on GF-6 rice map; (

**c**) NNE method based on GF-6 rice map; (

**d**) NNE method based on GF-6 rice map).

**Figure 12.**The fitting results of vegetation index and above-ground dry biomass (AGB) based on UAV at the pre-heading stage.

**Figure 13.**The fitting results of red-edge vegetation index and simulated AGB based on GF-6 images at the pre-heading stage.

**Figure 15.**The fitting results of measured AGB and estimated AGB based on the partial least squares regression (PLSR) model.

Acquisition Date | Image Number | Sensor | Temporal Resolution | Central Wavelength/nm | |
---|---|---|---|---|---|

GF-6 | 4 June 2019 | 2 | WFV | 2 days | B1 (blue) : 485 B2 (green): 555 B3 (red): 660 B4 (near-infrared): 830 B5 (red-edge1): 704 B6 (red-edge2): 752 B7 (coast blue): 425 B8 (yellow): 610 |

27 July 2019 | 3 | ||||

12 August 2019 | 2 | ||||

20 August 2019 | 1 | ||||

30 September 2019 | 2 | ||||

GF-1 | 2 June 2019 | 4 | WFV1 WFV2 WFV3 WFV4 | 4 days | B1 (blue): 485 B2 (green): 555 B3 (red): 660 B4 (near-infrared): 830 |

26 June 2019 | 4 | ||||

2 August 2019 | 4 | ||||

15 August 2019 | 4 | ||||

12 September 2019 | 4 | ||||

28 September 2019 | 4 |

VIs | Formula | Reference | |
---|---|---|---|

UAV | Normalized Difference Vegetation Index (NDVI) | $\frac{{\mathrm{R}}_{830\mathrm{nm}}-{\mathrm{R}}_{704\mathrm{nm}}}{{\mathrm{R}}_{704\mathrm{nm}}-{\mathrm{R}}_{660\mathrm{nm}}}$ | Rouse et al., 1974 [56] |

Normalized Difference Red edge (NDRE) | $\frac{{\mathrm{R}}_{800\mathrm{nm}}-{\mathrm{R}}_{720\mathrm{nm}}}{{\mathrm{R}}_{800\mathrm{nm}}{+\mathrm{R}}_{720\mathrm{nm}}}$ | Glenn et al., 2010 [57] | |

MERIS Terrestrial Chlorophyll Index (MTCI) | $\frac{{\mathrm{R}}_{800\mathrm{nm}}-{\mathrm{R}}_{720\mathrm{nm}}}{{\mathrm{R}}_{720\mathrm{nm}}-{\mathrm{R}}_{670\mathrm{nm}}}$ | Dashand Curran, 2004 [58] | |

Red-edge Chlorophyll Index (CI_{red-edge}) | $\frac{{\mathrm{R}}_{800\mathrm{nm}}}{{\mathrm{R}}_{720\mathrm{nm}}}-1$ | Gitelson et al., 2005 [59] | |

Two-band Enhanced Vegetation Index (EVI2) | $\frac{2.5\left({\mathrm{R}}_{800\mathrm{nm}}-{\mathrm{R}}_{670\mathrm{nm}}\right)}{{\mathrm{R}}_{800\mathrm{nm}}+{2.4\mathrm{R}}_{670\mathrm{nm}}+1}$ | Jiang et al., 2008 [60] | |

Leaf Chlorophyll Index (LCI) | $\frac{{\mathrm{R}}_{850\mathrm{nm}}-{\mathrm{R}}_{720\mathrm{nm}}}{{\mathrm{R}}_{850\mathrm{nm}}-{\mathrm{R}}_{680\mathrm{nm}}}$ | Datt, B. et al, 1999 [61] | |

Normalized Red-edge Difference Index (NREDI) | $\frac{{\mathrm{R}}_{720\mathrm{nm}}-{\mathrm{R}}_{700\mathrm{nm}}}{{\mathrm{R}}_{720\mathrm{nm}}{+\mathrm{R}}_{700\mathrm{nm}}}$ | Feng G U, 2019 [62] | |

GF-6 | Normalized Difference Vegetation Index (NDVI) | $\frac{{\mathrm{R}}_{830\mathrm{nm}}-{\mathrm{R}}_{660\mathrm{nm}}}{{\mathrm{R}}_{830\mathrm{nm}}{+\mathrm{R}}_{660\mathrm{nm}}}$ | Rouse et al., 1974 [56] |

Normalized Difference Red edge (NDRE_{red-edge5}) | $\frac{{\mathrm{R}}_{830\mathrm{nm}}-{\mathrm{R}}_{704\mathrm{nm}}}{{\mathrm{R}}_{830\mathrm{nm}}{+\mathrm{R}}_{704\mathrm{nm}}}$ | Glenn et al., 2010 [57] | |

Normalized Difference Red edge (NDRE_{red-edge6}) | $\frac{{\mathrm{R}}_{830\mathrm{nm}}-{\mathrm{R}}_{752\mathrm{nm}}}{{\mathrm{R}}_{830\mathrm{nm}}{+\mathrm{R}}_{752\mathrm{nm}}}$ | Dashand Curran, 2004 [58] | |

MERIS Terrestrial Chlorophyll Index (MTCI_{red-edge5}) | $\frac{{\mathrm{R}}_{830\mathrm{nm}}-{\mathrm{R}}_{704\mathrm{nm}}}{{\mathrm{R}}_{704\mathrm{nm}}-{\mathrm{R}}_{660\mathrm{nm}}}$ | Dashand Curran, 2004 [58] | |

MERIS Terrestrial Chlorophyll Index (MTCI_{red-edge6}) | $\frac{{\mathrm{R}}_{830\mathrm{nm}}-{\mathrm{R}}_{752\mathrm{nm}}}{{\mathrm{R}}_{752\mathrm{nm}}-{\mathrm{R}}_{660\mathrm{nm}}}$ | Gitelson et al., 2005 [59] | |

Red-edge Chlorophyll Index (CI_{red-edge5}) | $\frac{{\mathrm{R}}_{830\mathrm{nm}}}{{\mathrm{R}}_{704\mathrm{nm}}}-1$ | Gitelson et al., 2005 [59] | |

Red-edge Chlorophyll Index (CI_{red-edge6}) | $\frac{{\mathrm{R}}_{830\mathrm{nm}}}{{\mathrm{R}}_{752\mathrm{nm}}}-1$ | Jiang et al., 2008 [60] | |

Two-band Enhanced Vegetation Index (EVI2_{red-edge5}) | $\frac{2.5\left({\mathrm{R}}_{830\mathrm{nm}}-{\mathrm{R}}_{704\mathrm{nm}}\right)}{{\mathrm{R}}_{830\mathrm{nm}}+{2.4\mathrm{R}}_{704\mathrm{nm}}+1}$ | Jiang et al., 2008 [60] | |

Two-band Enhanced Vegetation Index (EVI2_{red-edge6}) | $\frac{2.5\left({\mathrm{R}}_{830\mathrm{nm}}-{\mathrm{R}}_{752\mathrm{nm}}\right)}{{\mathrm{R}}_{830\mathrm{nm}}+{2.4\mathrm{R}}_{752\mathrm{nm}}+1}$ | Feng G U, 2019 [62] | |

Red-edge Triangle Chlorophyll Index (TCI_{red-edge5}) | $1.2\left({\mathrm{R}}_{704\mathrm{nm}}-{\mathrm{R}}_{555\mathrm{nm}}\right)-{1.5(\mathrm{R}}_{660\mathrm{nm}}-{\mathrm{R}}_{555\mathrm{nm}})\sqrt{\frac{{\mathrm{R}}_{704\mathrm{nm}}}{{\mathrm{R}}_{660\mathrm{nm}}}}$ | Feng G U, 2019 [62] | |

Red-edge Triangle Chlorophyll Index (TCI_{red-edge6}) | $1.2\left({\mathrm{R}}_{704\mathrm{nm}}-{\mathrm{R}}_{555\mathrm{nm}}\right)-{1.5(\mathrm{R}}_{660\mathrm{nm}}-{\mathrm{R}}_{555\mathrm{nm}})\sqrt{\frac{{\mathrm{R}}_{752\mathrm{nm}}}{{\mathrm{R}}_{660\mathrm{nm}}}}$ | Feng G U, 2019 [62] | |

Red-edge Transformation Chlorophyll Absorption Reflectance Index (TCARI_{red-edge5}) | $3\left({\mathrm{R}}_{704\mathrm{nm}}-{\mathrm{R}}_{660\mathrm{nm}}\right)-0.2\left({\mathrm{R}}_{704\mathrm{nm}}-{\mathrm{R}}_{555\mathrm{nm}}\right)\frac{{\mathrm{R}}_{704\mathrm{nm}}}{{\mathrm{R}}_{555\mathrm{nm}}}$ | Feng G U, 2019 [62] | |

Red-edge Transformation Chlorophyll Absorption Reflectance Index (TCARI_{red-edge6}) | $3\left({\mathrm{R}}_{752\mathrm{nm}}-{\mathrm{R}}_{660\mathrm{nm}}\right)-0.2\left({\mathrm{R}}_{752\mathrm{nm}}-{\mathrm{R}}_{555\mathrm{nm}}\right)\frac{{\mathrm{R}}_{752\mathrm{nm}}}{{\mathrm{R}}_{555\mathrm{nm}}}$ | Feng G U, 2019 [62] |

**Table 3.**Confusion matrix of local (A and B) and Jingzhou city accuracy assessment using field measured data and Google Earth images (A: 800 pixels rice samples, 1300 pixels non-rice samples; B: 900 pixels rice samples, 700 pixels non-rice samples; Jingzhou city: 4000 pixels rice samples, 6000 pixels non-rice samples).

Study Area | Paddy Rice Map | Class | PA% | UA% | OA% | Kappa Coefficient |
---|---|---|---|---|---|---|

A | GF-6-REDT | Rice | 91.85 | 93.04 | 93.85 | 0.87 |

Non-rice | 95.24 | 94.40 | ||||

GF-6-NNE | Rice | 85.82 | 81.48 | 86.42 | 0.72 | |

Non-rice | 86.84 | 90.07 | ||||

GF-1-NNE | Rice | 86.37 | 77.55 | 85.29 | 0.69 | |

Non-rice | 84.61 | 90.98 | ||||

B | GF-6-REDT | Rice | 94.80 | 94.48 | 94.06 | 0.89 |

Non-rice | 93.14 | 93.54 | ||||

GF-6-NNE | Rice | 89.09 | 87.78 | 87.31 | 0.74 | |

Non-rice | 85.19 | 86.73 | ||||

GF-1-NNE | Rice | 90.73 | 84.76 | 86.37 | 0.72 | |

Non-rice | 81.42 | 88.52 | ||||

Jingzhou City | GF-6-REDT | Rice | 90.01 | 89.60 | 91.10 | 0.82 |

Non-rice | 91.93 | 92.26 | ||||

GF-6-NNE | Rice | 81.76 | 78.11 | 83.39 | 0.66 | |

Non-rice | 84.49 | 87.26 | ||||

GF-1-NNE | Rice | 86.34 | 75.43 | 82.17 | 0.64 | |

Non-rice | 79.06 | 88.60 |

Paddy Rice Map | Class | PA (%) | UA (%) | OA (%) | Kappa Coefficient |
---|---|---|---|---|---|

GF-6-REDT | Rice | 94.55 | 93.86 | 93.04 | 0.85 |

Non-rice | 90.81 | 91.80 | |||

GF-6-NNE | Rice | 87.68 | 90.64 | 87.17 | 0.73 |

Non-rice | 86.41 | 82.38 | |||

GF-1-NNE | Rice | 85.26 | 91.04 | 86.08 | 0.71 |

Non-rice | 87.33 | 79.68 |

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## Share and Cite

**MDPI and ACS Style**

Jiang, X.; Fang, S.; Huang, X.; Liu, Y.; Guo, L.
Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands. *Remote Sens.* **2021**, *13*, 579.
https://doi.org/10.3390/rs13040579

**AMA Style**

Jiang X, Fang S, Huang X, Liu Y, Guo L.
Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands. *Remote Sensing*. 2021; 13(4):579.
https://doi.org/10.3390/rs13040579

**Chicago/Turabian Style**

Jiang, Xueqin, Shenghui Fang, Xia Huang, Yanghua Liu, and Linlin Guo.
2021. "Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands" *Remote Sensing* 13, no. 4: 579.
https://doi.org/10.3390/rs13040579