# Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

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## Abstract

**:**

## 1. Introduction

_{re}). Their work showed that the CI

_{re}had the best correlation with the measured biomass. Kross et al. [18] used multispectral RapidEye data with a high spatial resolution to estimate the leaf area index (LAI) and biomass of corn and soybeans. They believed that the SR

_{red-edge}performed well in terms of its ability to estimate the total biomass of corn. Frampton et al. [19] constructed two new indices S2REP (sentinel-2 red edge position) and IRECI (red-edge chlorophyll index) using the red edge band of sentinel-2 data. They reported that S2REP was more suitable for retrieving chlorophyll concentration, while the performance of IREC was still better, even when the saturation point was exceeded.

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. Irrigation facilities are complete, which provides a large amount of water resources for agricultural production. The irrigation area has a continental monsoon climate. The total amount of precipitation in the irrigation area is about 488 mm, the annual frost-free period is 190–200 days, the annual sunshine hours are about 2626 h, and the accumulated temperature above zero is 4600–5000 °C. The good climatic conditions in the irrigation area provide sufficient water and heat resources for the winter wheat and summer corn rotation system, and they are beneficial to the development of characteristic orchards, such as apple and pear orchards, in the irrigation area. In 2019, we made a field survey route for the irrigation area and conducted field survey for the main crops to obtain the aboveground biomass data of winter wheat and summer maize (Figure 1c).

#### 2.2. Datasets and Processing

#### 2.2.1. Meteorological Data

#### 2.2.2. Remote Sensing Data

#### 2.2.3. Measured Field Data

#### 2.3. Methods

#### 2.3.1. Processing Flow of the Original CASA Model

#### 2.3.2. Vegetation Indices

#### 2.3.3. Conversion of NPP into Aboveground Biomass

#### 2.3.4. Accuracy Assessment

^{2}) and root mean square error (RMSE), as shown in Equations (13) and (14).

#### 2.4. Workflow

_{red-edge}, SR

_{red-edge}from previous research and the experiments in this study. For the estimation of aboveground biomass, we gathered all of the input parameters of the improved CASA model, and then input them into the model to obtain the estimated crop biomass. According to the root-to-shoot ratio and the ratio of carbon content, the NPP was transformed into the aboveground biomass, and the aboveground dry weight data of winter wheat and summer maize were used to validate the accuracy of the biomass estimated by the improved CASA model.

## 3. Results

#### 3.1. Modeling of FPAR Based on Red-Edge Vegetation Indices

#### 3.2. FPAR Inversion Results Based on Red-Edge Vegetation Indices

_{red-edge}and FPAR also reached the maximum.

#### 3.3. Improved Inversion Results Based on Red-Edge Vegetation Indices

#### 3.3.1. Cumulative NPP and Biomass of Crops

^{−2}. From the perspective of spatial distribution, the biomass in the southeastern part of the study area was the largest, while the biomass in the northwest was relatively small. The biomass of summer maize varied from 187.27 to 2763.93 g·m

^{−2}. The maximum biomass value was mainly distributed in the northwest, and the minimum value was mostly distributed in the south. Due to the linear relationship between the NPP of winter wheat and summer maize and the biomass, regardless of the influence of other factors, the spatial distribution of the biomass was similar to the spatial distribution of the accumulated NPP.

^{−2}interval. The cumulative frequency of the NPP reached the maximum value of 670 gC·m

^{−2}, and the relative distribution frequency was close to 40% (Figure 10a). For the cumulative NPP distribution frequency map of maize, the cumulative NPP was mainly concentrated in the 700–800 gC·m

^{−2}interval, the NPP cumulative frequency reached the maximum value of 753 gC·m

^{−2}, and the relative distribution frequency was close to 60% (Figure 10b).

^{−2}, with a relative distribution frequency of more than 30%. For the distribution frequency map of summer maize’s cumulative biomass, the cumulative NPP was mainly concentrated in the range of 1200–1800. The maximum biomass cumulative frequency was 1627 g·m

^{−2}, and the relative distribution frequency was close to 50%. As such, the cumulative biomass distribution of winter wheat and summer maize was close to the standard normal distribution, but their concentration frequency distribution intervals were different, which indicated that the average biomass of summer maize was higher than that of winter wheat.

#### 3.3.2. Aboveground Biomass Estimation Accuracy of Crops

^{2}is 0.73. For the predicted and measured biomass of summer maize, the R

^{2}is 0.70, and the linear relationship was significant. However, the accuracy of the predicted biomass of the original CASA model is lower than the improved CASA model (Figure 11b). The accuracies of predicted biomass of winter wheat and summer maize are 0.6 and 0.49, respectively, which are lower than those of the improved CASA model.

#### 3.4. Seasonal Variation and Factors Influencing Crops Aboveground Biomass

## 4. Discussion

#### 4.1. Accuracy Differences of Various Vegetation Indices

^{2}of 0.71 and RSME of 0.05. The accuracy was higher than that of the winter wheat FPAR retrieved with the NDVI-SR. For inversion with ${\mathrm{SR}}_{\mathrm{red}-\mathrm{edge}}$, the maximum FPAR accuracy of summer corn was 0.55, and the RSME was 0.04. The retrieval accuracy was higher than that of the summer corn FPAR retrieved with the NDVI. This also shows that the red-edge information of Sentinel-2 helps to improve the accuracy of the FPAR inversion.

#### 4.2. Mapping Differences of Various Models

_{red-edge}and SR

_{red-edge}, we find that the 10 m FPAR mapping based on the red-edge vegetation indices is in good agreement with the MODIS FPAR product in spatial distribution and numerical range (Figure 15a,d). The value of FPAR retrieved by NDVI is higher than FPAR retrieved by red-edge indices and MODIS FPAR. This is because the NDVI value of the combination of near-infrared and red band is larger, and it is close to saturation in dense vegetation. Moreover, Figure 14 shows the spatial distribution of aboveground biomass based on improved CASA model, the original CASA model and the MODIS FPAR-driven. The result shows that the aboveground biomass of 10 m predicted by the improved CASA model and driven by MODIS FPAR are also in accordance with spatial distribution and numerical range (Figure 16a,d). The original CASA model based on NDVI, despite the spatial distribution consistent with the aboveground biomass of improved CASA model and MODIS FPAR-driven, shows a significant overestimation of its biomass (Figure 16b,e).

#### 4.3. Features of Improved CASA Model

_{red-edge}and SR

_{red-edge}can retrieve crops FPAR more effectively. This is due to the faster change of canopy reflectance at 705 nm of red edge, which leads to more sharp response to crops chlorophyll. Compared with the former two, the canopy reflectance at 842 nm of NDVI does not change dramatically, so the accuracy of FPAR inversion is lower than that of red edge based FPAR, which leads to the low accuracy of biomass estimation.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

NPP | Net primary productivity | NPP = APAR × LUE |

APAR | Photosynthetically active radiation absorbed by the vegetation canopy | APAR = PAR × FPAR |

FPAR | Photosynthetically active radiation absorption proportion | FPAR_{wheat} = 0.8287 × NDVI_{red-edge} + 0.1889 FPAR _{maize} = 0.1023 × SR_{red-edge} + 0.3011 |

PAR | Photosynthetically active radiation | PAR = SOL × 0.5 |

SOL | Monthly radiation | Defined as the total radiation of crops in main growth stage |

LUE | Actual light use efficiency | LUE = T_{ε1} × T_{ε2} × W_{ε} × LUE_{max} |

LUE_{max} | Maximum LUE | For winter wheat, LUE_{max} = 1.95, for summer maize, LUE_{max} = 2.55 |

T_{ε1} | Effects of temperature stress | T_{ε1} = 0.8 + 0.02 × T_{opt} − 0.0005 × T_{opt}^{2} |

T_{ε2} | Effects of temperature stress | T_{ε2} = 1.184/{1 + exp [0.2 × (T_{opt}-10-T_{x})]} × 1/{1 + exp[0.3 × (−T_{opt}-10 + T_{x})]} |

T_{opt} | Optimal temperature | Defined as the air temperature in the month when the NDVI reaches its maximum |

T_{x} | Monthly temperature | Defined as monthly temperature of crops in main growth stage |

W_{ε} | Effects of water stress | W_{ε} = (1 − (1 + LSWI)/(1 + LSWI_{max})) + 0.5 |

LSWI | Land Surface Water Index | LSWI = (${\mathsf{\rho}}_{\mathrm{nir}}{-\mathsf{\rho}}_{\mathrm{Swir}}$)/(${\text{}\mathsf{\rho}}_{\mathrm{nir}}{-\mathsf{\rho}}_{\mathrm{Swir}}$) |

B | Aboveground biomass | $\mathrm{B}={\displaystyle \sum}\mathrm{NPP}\text{}\times \text{}\mathsf{\alpha}/\mathsf{\beta}$ |

$\mathsf{\alpha}$ | Ratio of the aboveground biomass to the whole vegetation | For winter wheat, $\mathsf{\alpha}=0$.90, for summer maize, $\mathsf{\alpha}=0$.91, |

$\mathsf{\beta}$ | The C ratio of a crop | For winter wheat, $\mathsf{\alpha}=0$.49, for summer maize, $\mathsf{\alpha}=0$.47, |

NDVI | Normalized Difference Vegetation Index | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}{-\mathsf{\rho}}_{\mathrm{red}}}{{\mathsf{\rho}}_{\mathrm{nir}}{+\mathsf{\rho}}_{\mathrm{red}}}$ |

NDVI_{red-edge} | Red-Edge Normalized Difference Vegetation Index | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}{-\mathsf{\rho}}_{\mathrm{red}-\mathrm{edge}}}{{\mathsf{\rho}}_{\mathrm{nir}}{+\mathsf{\rho}}_{\mathrm{red}-\mathrm{edge}}}$ |

$\mathrm{SR}$ | Simple Ratio Vegetation Index | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}}{{\mathsf{\rho}}_{\mathrm{red}}}$ |

MSR | Modified Simple Ratio Vegetation Index | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}{/\mathsf{\rho}}_{\mathrm{red}}-1}{\sqrt{{\mathsf{\rho}}_{\mathrm{nir}}{/\mathsf{\rho}}_{\mathrm{red}}+1}}$ |

MSR_{red-edge} | Modified Red-Edge Simple Ratio Vegetation Index | $\frac{\frac{{\mathsf{\rho}}_{\mathrm{nir}}}{{\mathsf{\rho}}_{\mathrm{red}-\mathrm{edge}}}-1}{\sqrt{\frac{{\mathsf{\rho}}_{\mathrm{nir}}}{{\mathsf{\rho}}_{\mathrm{red}-\mathrm{edge}}}+1}}$ |

SR_{red-edge} | Red-Edge Simple Ratio Vegetation Index | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}}{{\mathsf{\rho}}_{\mathrm{red}\_\mathrm{edge}}}$ |

EVI | Enhanced Vegetation Index | $\frac{2{.5(\mathsf{\rho}}_{\mathrm{nir}}{-\mathsf{\rho}}_{\mathrm{red}})}{{(\mathsf{\rho}}_{\mathrm{nir}}{+6\mathsf{\rho}}_{\mathrm{red}}-7{.5\mathsf{\rho}}_{\mathrm{blue}})}+1$ |

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**Figure 1.**Location of the Shijin irrigation area and the field survey samples. (

**a**,

**b**) National and provincial boundaries. (

**c**) Sample routes and elevation. (

**d**) Land use for irrigation.

**Figure 2.**Sentinel-2 product tiles and the number of images at key growth stages. (

**a**,

**b**) High-quality pixel observations at the key winter wheat and maize growth stages. (

**c**,

**d**) The numbers of images at the key winter wheat and maize growth stages.

**Figure 3.**Field survey of winter wheat and maize. (

**a**,

**b**) The sampling of winter wheat. (

**c**,

**d**) The sampling of maize.

**Figure 5.**Spectral reflectance curves and variations of crops in red-edge information. (

**a**) Spectral curves of winter wheat and maize. (

**b**) Variations in the red-edge positions of winter wheat and maize.

**Figure 6.**Correlation between the vegetation indices and the reference FPAR. (

**a**–

**c**) Correlation between the ${\mathrm{NDVI}}_{\mathrm{red}-\mathrm{edge}}$, ${\mathrm{SR}}_{\mathrm{red}-\mathrm{edge}}$, NDVI, and MODIS FPAR during the winter wheat growing season. (

**d**–

**f**) Correlation between the ${\mathrm{NDVI}}_{\mathrm{red}-\mathrm{edge}}$, ${\mathrm{SR}}_{\mathrm{red}-\mathrm{edge}}$, NDVI, and MODIS FPAR during the maize growing season.

**Figure 7.**The spatial distribution of the red-edge vegetation indices of crops in the main growing season. (

**a**–

**c**) Spatial distribution of NDVI

_{red-edge}of winter wheat from March to May. (

**d**–

**f**) Spatial distribution of SR

_{red-edge}of maize from July to September.

**Figure 8.**The spatial distribution of the FPAR of crops in the main growing season. (

**a**–

**c**) Spatial distribution of the FPAR of winter wheat from March to May. (

**d**–

**f**) Spatial distribution of the FPAR of maize from July to September.

**Figure 9.**The spatial distribution of the accumulated aboveground NPP and biomass of the crops during the main growing season. (

**a**) Cumulative NPP of winter wheat from March to May. (

**b**) Cumulative biomass of summer maize from July to September.

**Figure 10.**The frequency of the accumulated NPP and biomass in the main growing seasons of the crops. (

**a**,

**b**) The frequency of the cumulative NPP of winter wheat and maize. (

**c**,

**d**) The frequency of the cumulative biomass of winter wheat and maize.

**Figure 11.**Correlation of the aboveground biomass of winter wheat and maize in the main growing seasons.

**Figure 12.**Seasonal variations in NDVI, FPAR, LUE, and biomass in the main growing seasons. (

**a**,

**b**) Variations in the biophysical parameters of winter wheat and maize, respectively.

**Figure 13.**Correlation between the predicted FPAR and the reference data in the main growing seasons. (

**a**–

**h**) Correlation between the FPAR retrieved by the NDVI, SR, MSR, EVI, ${\mathrm{NDVI}}_{\mathrm{red}-\mathrm{edge}}$, ${\mathrm{SR}}_{\mathrm{red}-\mathrm{edge}}$, ${\mathrm{MSR}}_{\mathrm{red}-\mathrm{edge}}$, NDVI-SR, and MCD15A3H FPAR predictions.

**Figure 14.**Correlation histogram of the predicted FPAR of winter wheat and maize in the main growing seasons.

**Figure 15.**Spatial distribution of seasonal average FPAR of different crops. (

**a**–

**c**) Seasonal average FPAR of winter wheat based on NDVI

_{red-edge}, NDVI and MODIS FPAR product. (

**d**–

**f**) Seasonal average FPAR of summer maize based on SR

_{red-edge}, NDVI and MODIS FPAR product.

**Figure 16.**Spatial distribution of aboveground biomass of different crops. (

**a**–

**c**) Aboveground biomass of winter wheat derived by improved CASA model, original CASA model and MODIS FPAR product. (

**d**–

**f**) Aboveground biomass of summer maize derived by improved CASA model, original CASA model and MODIS FPAR product.

Satellite Data | Band | Center Wavelength (nm) | Resolution (m) | Source |
---|---|---|---|---|

Sentinel-2 | B1 | 443 | 60 | European Space Agency (https://sentinel.esa.int/web/sentinel/, accessed on 6 November 2020) |

B2 | 490 | 10 | ||

B3 | 560 | 10 | ||

B4 | 665 | 10 | ||

B5 | 705 | 20 | ||

B6 | 740 | 20 | ||

B7 | 783 | 20 | ||

B8 | 842 | 10 | ||

B8A | 865 | 20 | ||

B9 | 940 | 60 | ||

B10 | 1375 | 60 | ||

B11 | 1610 | 20 | ||

B12 | 2190 | 20 | ||

QA10 | — | 10 | ||

QA20 | — | 20 | ||

QA60 | — | 60 | ||

MCD15A3H | FPAR | — | 500 | NASA LP DAAC at the USGS EROS Center (https://lpdaac.usgs.gov/products/mcd15a3hv006/, accessed on 6 November 2020) |

LAI | — | 500 |

Parameter | Unit | Description | Source |
---|---|---|---|

Meteorological Data | |||

Temperature | °C | Near-surface (2 m) air temperature | China Meteorological Data Service Center (CMDSC) |

Radiation | MJ/m^{2} | Surface downward shortwave radiation | Angstrom model |

Measured Field Data | |||

Winter Wheat Aboveground Biomass | g/m^{2} | Aboveground biomass at maturity stage | Field measurements |

Summer Maize Aboveground Biomass | g/m^{2} | Aboveground biomass at maturity stage | Field measurements |

Crops | Vegetation Indices | Regression Model | R^{2} |
---|---|---|---|

Winter wheat | ${\mathrm{NDVI}}_{\mathrm{red}-\mathrm{edge}}$ | y = 0.8287x + 0.1889 | 0.72 |

$\mathrm{NDVI}$ | y = 0.1656x + 0.7371 | 0.71 | |

${\mathrm{SR}}_{\mathrm{red}-\mathrm{edge}}$ | y = 0.1619x + 0.0979 | 0.69 | |

Maize | ${\mathrm{NDVI}}_{\mathrm{red}-\mathrm{edge}}$ | y = 0.7081x − 0.0026 | 0.45 |

${\mathrm{SR}}_{\mathrm{red}-\mathrm{edge}}$ | y = 0.1023x + 0.3011 | 0.53 | |

$\mathrm{NDVI}$ | y = 0.5270x + 0.3305 | 0.40 |

**Table 4.**Vegetation indices selected for the difference analysis.${\text{}\mathsf{\rho}}_{\mathrm{nir}}$ is the near-infrared band; ${\mathsf{\rho}}_{\mathrm{red}}$ is the red band; ${\mathsf{\rho}}_{\mathrm{red}\_\mathrm{edge}}$ is the red-edge band.

Vegetation Indices | Descriptions | Equation | Reference | |
---|---|---|---|---|

$\mathrm{EVI}$ | It corrects for some atmospheric conditions and canopy background noise. | $\frac{2{.5(\mathsf{\rho}}_{\mathrm{nir}}{-\mathsf{\rho}}_{\mathrm{red}})}{{(\mathsf{\rho}}_{\mathrm{nir}}{+6\mathsf{\rho}}_{\mathrm{red}}-7{.5\mathsf{\rho}}_{\mathrm{blue}})}+1$ | (5) | [40] |

$\mathrm{SR}$ | It can distinguish green leaves from other objects and estimate the relative biomass. | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}}{{\mathsf{\rho}}_{\mathrm{red}}}$ | (6) | [41] |

${\mathrm{SR}}_{\mathrm{red}-\mathrm{edge}}$ | It is used to assess vegetation biomass and growth, which reduces the impact of atmosphere and topography. | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}}{{\mathsf{\rho}}_{\mathrm{red}\_\mathrm{edge}}}$ | (7) | [42] |

$\mathrm{MSR}$ | It is an improved version of SR, which is sensitive to vegetation biophysical parameters. | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}{/\mathsf{\rho}}_{\mathrm{red}}-1}{\sqrt{{\mathsf{\rho}}_{\mathrm{nir}}{/\mathsf{\rho}}_{\mathrm{red}}+1}}$ | (8) | [43] |

${\mathrm{MSR}}_{\mathrm{red}-\mathrm{edge}}$ | It uses bands in the red edge and incorporates a correction for leaf specular reflection. | $\frac{\frac{{\mathsf{\rho}}_{\mathrm{nir}}}{{\mathsf{\rho}}_{\mathrm{red}-\mathrm{edge}}}-1}{\sqrt{\frac{{\mathsf{\rho}}_{\mathrm{nir}}}{{\mathsf{\rho}}_{\mathrm{red}-\mathrm{edge}}}+1}}$ | (9) | [44] |

${\mathrm{NDVI}}_{\mathrm{red}-\mathrm{edge}}$ | It is similar to NDVI but capitalizes on the sensitivity of the vegetation red edge to small changes in canopy chlorophyll. | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}{-\mathsf{\rho}}_{\mathrm{red}-\mathrm{edge}}}{{\mathsf{\rho}}_{\mathrm{nir}}{+\mathsf{\rho}}_{\mathrm{red}-\mathrm{edge}}}$ | (10) | [45] |

$\mathrm{NDVI}$ | Combined with red band and near infrared band, it has strong robustness in a wide range. However, it saturates in dense vegetation conditions. | $\frac{{\mathsf{\rho}}_{\mathrm{nir}}{-\mathsf{\rho}}_{\mathrm{red}}}{{\mathsf{\rho}}_{\mathrm{nir}}{+\mathsf{\rho}}_{\mathrm{red}}}$ | (11) | [46] |

Crops | Carbon Content Ratio | Dry Matter Ratio | Root-Shoot Ratio | Moisture Content (%) | |
---|---|---|---|---|---|

Economic Production | Residual Carbon Ratio | ||||

Winter wheat | 0.39 | 0.49 | 0.85 | 0.11 | 12.5 |

Maize | 0.39 | 0.47 | 0.78 | 0.09 | 13.5 |

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## Share and Cite

**MDPI and ACS Style**

Fang, P.; Yan, N.; Wei, P.; Zhao, Y.; Zhang, X.
Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery. *Remote Sens.* **2021**, *13*, 2755.
https://doi.org/10.3390/rs13142755

**AMA Style**

Fang P, Yan N, Wei P, Zhao Y, Zhang X.
Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery. *Remote Sensing*. 2021; 13(14):2755.
https://doi.org/10.3390/rs13142755

**Chicago/Turabian Style**

Fang, Peng, Nana Yan, Panpan Wei, Yifan Zhao, and Xiwang Zhang.
2021. "Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery" *Remote Sensing* 13, no. 14: 2755.
https://doi.org/10.3390/rs13142755