# Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China

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## Abstract

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_{(654,679)}), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI

_{(551,1998)}); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI

_{(654,679)}and V-RR-DSI

_{(551,1998)}is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. The terrain is higher in the west and lower in the east, and the ground elevation is mostly between 4.5 and 7 m. The river network is dense. The whole area lies in a temperate monsoon climate zone with abundant sunshine and average annual precipitation of 937.60 mm [30,31,32]. The soil texture of the topsoil (0–15 cm) in Shuyang County is mainly silt, and the contents of clay and sand are relatively small. The soil types include cambisols, regosols, luvisols, greyzems, anthrosols, gleysols, fluvisols, nitisols and arenosols (Figure 1b) [33,34], and the crops are mainly rice, wheat, maize and soybean [35]. The land-use types of cultivated land in the study area mainly include paddy fields, dry land and nurseries (Figure 1c).

#### 2.2. Hyperspectral Satellite Data Acquisition and Preprocessing

#### 2.3. Ground Sampling and Soil Measurements

#### 2.4. Methods

#### 2.4.1. Research Process

#### 2.4.2. Construction of the Optimal SIs

#### 2.4.3. Application Assessment of the Optimal SIs

^{2}) was calculated to evaluate the performance of each index. In addition, the LR models of the optimal SIs here were also applied to obtain the inversion map of SOMC for recognition of SOMC levels.

^{2}, the root mean square error (RMSE), and the residual prediction deviation (RPD) to evaluate the SOMC characterization ability of the inversion model constructed by the optimal SIs. In general, a well-established model usually has a high R

^{2}and RPD, and a low RMSE [23,39]. According to the research of Yuan et al. [49], the RPD values are divided into five levels to interpret the model performance, RPD < 1.4 (unacceptable), 1.4 ≤ RPD < 1.8 (fair), 1.8 ≤ RPD < 2.0 (good), 2.0 ≤ RPD < 2.5 (very good), and RPD ≥ 2.5 (excellent). Their calculation formulas are as follows:

Index Type | Abbreviation | Formula | Properties | References |
---|---|---|---|---|

Soil SI | SOC1 | $\frac{1}{{{\displaystyle \sum}}_{400nm}^{700nm}R}$ | SOMC | [22] |

SOC2 | $\frac{1}{\left({R}_{600nm}-{R}_{400nm}\right)/\left(600-400\right)}$ | SOMC | [22] | |

SOC3 | $\frac{1}{\left({R}_{2200nm}-{R}_{2138nm}\right)/\left(2200-2138\right)}$ | SOMC | [22] | |

NSMI | $\frac{{R}_{1800nm}-{R}_{2119nm}}{{R}_{1800nm}+{R}_{2119nm}}$ | Soil moisture | [50,51] | |

Vegetation SI | CAI | $\frac{{R}_{2000nm}+{R}_{2200nm}}{2}-{R}_{2100nm}$ | Cellulose Absorption | [52,53,54] |

NDLI | $\frac{\mathrm{log}\left(1/{R}_{1754nm}\right)-\mathrm{log}\left(1/{R}_{1680nm}\right)}{\mathrm{log}\left(1/{R}_{1754nm}\right)+\mathrm{log}\left(1/{R}_{1680nm}\right)}$ | Lignin concentration | [53,55,56,57] | |

MSI | $\frac{{R}_{1610nm}}{{R}_{868nm}}$ | Leaf water content | [58,59] | |

SATVI | $\frac{{R}_{1610nm}-{R}_{665nm}}{{R}_{1610nm}+{R}_{665nm}}\times 2-\frac{{R}_{2190nm}}{2}$ | Total vegetation cover | [58,60] |

## 3. Results

#### 3.1. Descriptive Statistics of Samples

#### 3.2. Spectral Characteristics of the Pixel Reflectance of the Sample Sites

#### 3.3. Correlation between Transformed Spectra and SOMC

#### 3.4. Correlation between SIs and SOMC

_{max}) between each type of SI and SOMC in bare soil and vegetation-covered areas is listed in Table 8 and Table 9. Considering the performance of various types of SIs in the bare soil area and vegetation-covered area, |$\rho $| greater than 0.600 was utilized as the threshold to obtain the optimal SIs.

_{(654,679)}), with |$\rho $| as high as 0.627.

_{(551,1998)}), with |$\rho $| as high as 0.639.

#### 3.5. Application of the Optimal SIs

#### 3.5.1. Characterization of SOMC in Soil Samples

_{(654,679)}and RR-DSI

_{(551,1998)}have the highest correlation with SOMC in bare soil and vegetation-covered areas, respectively (the letter ’V’ is added to RR-DSI

_{(551,1998)}as a prefix (V-RR-DSI

_{(551,1998)}), which represents the optimal SI in the vegetation-covered area). These two indices were selected to construct LR models and compare them with other traditional indices (Table 4). The correlations of SOMC and different SIs are shown in Figure 7.

^{2}of the model constructed by OR-RI

_{(654,679)}is 0.398, much larger than the R

^{2}of the models constructed by other indices. In the vegetation-covered area, the R

^{2}of the model constructed by V-RR-DSI

_{(551,1998)}is 0.408, whereas the other indices are all less than 0.2. In general, the OR-RI

_{(654,679)}and V-RR-DSI

_{(551,1998)}proposed in this study well characterize SOMC in bare soil and vegetation-covered areas, respectively.

#### 3.5.2. Recognition of SOMC Levels in Soil Samples

_{(654,679)}, whereas SOMC in the vegetation-covered area was calculated by V-RR-DSI

_{(551,1998)}. After the SOMCs of bare soil and vegetation-covered areas were merged, SOMC of the whole cultivated land in Shuyang County was obtained. Based on the numerical distribution of SOMC, the calculated and measured SOMC was similarly divided into five levels by the equal interval method, as shown in Figure 8.

_{(654,679)}and V-RR-DSI

_{(551,1998)}can well recognize soil samples with different SOMC levels.

#### 3.5.3. Estimation of SOMC in Soil Samples

^{2}, RMSE and RPD of the soil-vegetation combined prediction results are 0.775, 3.72 g/kg and 2.12, respectively, suggesting the model is very good. Specifically, the RMSE of bare soil and vegetation-covered samples is 4.59 and 2.96 g/kg, respectively. Most scatters are very close to the 1:1 line, especially in the vegetation-covered area. About 88.04% of the predicted results are distributed within the expected error (80% precision lines). It can be seen that the optimal SIs proposed in this study have a great potential in predicting SOMC in both bare soil and vegetation-covered areas.

## 4. Discussion

#### 4.1. Image Quality of Transformed Spectra and SIs

#### 4.2. Advantages of Constructing SIs Separately in Bare Soil Area and Vegetation-Covered Area

_{(654,679)}), and that in the vegetation area is 0.639 (V-RR-DSI

_{(551,1998)}). For comparison, index construction and correlation analysis were also conducted for all sample points together. Figure 11 illustrates the correlation between SOMC and SIs constructed by different combinations of spectral transformations and index formulas. It can be seen that the highest correlation coefficient is 0.501 (p < 0.001), which is lower than the correlation coefficient of the indices constructed separately, revealing the advantage of constructing SIs separately.

#### 4.3. The Impacts of Soil Types and Cultivated Land-Use Type on SOMC

_{(654,679)}and V-RR-DSI

_{(551,1998)}(Figure 8a), the statistical results of SOMC of different soil types are shown in Table 10. The average values of the three soil types are 24.39, 24.39 and 24.05 g/kg, respectively, and the difference among them is very small, which is consistent with previous studies [33].

_{(654,679)}and V-RR-DSI

_{(551,1998)}(Figure 8a), the statistical results of SOMC of different cultivated land-use types are shown in Table 11. Figure 12a–c show SOMC distribution in the paddy field, dry land and nurseries, respectively.

## 5. Conclusions

- In the bare soil area, the SIs constructed based on OR and RR have higher correlations with SOMC. For the same transformed spectrum, the SIs calculated by RI and NDI have the highest correlations with SOMC, followed by DI. Among all the constructed SIs, OR-RI
_{(654,679)}has the highest correlation with SOMC, and the correlation coefficient is $-$0.627. In the vegetation-covered area, the correlations between SOMC and the SIs based on RR are higher than those of other transformed spectra. Among the different index formulas, the correlations between the SIs calculated by DSI and DI and SOMC are higher than those of RI and NDI. The correlation coefficient between V-RR-DSI_{(551,1998)}and SOMC is −0.639, which is the highest among all the calculated SIs. - The results show that the optimal SIs can be used to present the spatial distribution trend of SOMC and recognize SOMC levels. Based on the optimal SIs, the SOMC predicted by the model has a good linear relationship with the actual SOMC of samples. The R
^{2}, RMSE and RPD of the soil-vegetation combined prediction results are 0.775, 3.72 g/kg and 2.12, respectively.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Overview of the study area and sampling points: (

**a**) location, (

**b**) soil types, and (

**c**) land-use types.

**Figure 2.**(

**a**) The true-color satellite image (R: 662 nm, G: 551 nm, B: 482 nm) of Advanced Hyperspectral Imager (AHSI) data of ZY1-02D satellite obtained on 22 October 2020, in Shuyang County, (

**b**) the schemata the for five-point method, (

**c**) distribution of bare soil and vegetation-covered areas.

**Figure 4.**The reflectance spectra of soil samples with different soil organic matter content (SOMC) in (

**a**) bare soil and (

**b**) vegetation-covered area.

**Figure 5.**Correlation coefficient matrix of spectral indices (SIs) and SOMC of soil samples in the bare soil area.

**Figure 6.**Correlation coefficient matrix of SIs and SOMC of soil samples in the vegetation-covered area.

**Figure 8.**(

**a**) Recognition of SOMC levels in soil samples in cultivated land area of Shuyang County and (

**b**) details of partial enlargement.

**Figure 10.**Reflectance images after different spectral transformations at 645 nm (from the AHSI/ZY1-02D data). The meanings of OR, RR, SRR, FDR, RFDR, SRFDR and SDR are consistent with Table 2.

**Figure 11.**The maximum correlation coefficient between SOMC of SIs constructed by different combinations of spectral transformations and index formulas.

**Figure 12.**Distribution of SOMC in different land-use types ((

**a**) paddy field, (

**b**) dry land and (

**c**) nursery) in Shuyang County.

Spectral range | 400–2500 nm |

Number of bands | 166 |

Spatial resolution | 30 m |

Spectral resolution | VNIR 10 nm |

SWIR 20 nm | |

Swath width | 60 km |

Orbital period | 55 days |

Type | Expression |
---|---|

Original reflectance (OR) | R |

Reciprocal reflectance (RR) | 1/R |

Square root reflectance (SRR) | $\sqrt{R}$ |

First-order differential reflectance (FDR) | ${{R}^{\prime}}_{{\lambda}_{i}}=\frac{1}{2}\times \left(\frac{{R}_{{\lambda}_{i+1}}-{R}_{{\lambda}_{i}}}{{\lambda}_{i+1}-{\lambda}_{i}}+\frac{{R}_{{\lambda}_{i}}-{R}_{{\lambda}_{i-1}}}{{\lambda}_{i}-{\lambda}_{i-1}}\right)$ |

First-order differential of reciprocal reflectance (RFDR) | $\left(1/{R}_{{\lambda}_{i}}\right)\u2019=\frac{1}{2}\times \left(\frac{\left(1/{R}_{{\lambda}_{i+1}}\right)-\left(1/{R}_{{\lambda}_{i}}\right)}{{\lambda}_{i+1}-{\lambda}_{i}}+\frac{\left(1/{R}_{{\lambda}_{i}}\right)-\left(1/{R}_{{\lambda}_{i-1}}\right)}{{\lambda}_{i}-{\lambda}_{i-1}}\right)$ |

First-order differential of square root reflectance (SRFDR) | $\left(\sqrt{{R}_{{\lambda}_{i}}}\right)\u2019=\frac{1}{2}\times \left(\frac{\sqrt{{R}_{{\lambda}_{i+1}}}-\sqrt{{R}_{{\lambda}_{i}}}}{{\lambda}_{i+1}-{\lambda}_{i}}+\frac{\sqrt{{R}_{{\lambda}_{i}}}-\sqrt{{R}_{{\lambda}_{i}-1}}}{{\lambda}_{i}-{\lambda}_{i-1}}\right)$ |

Second-order differential reflectance (SDR) | ${{R}^{\u2033}}_{{\lambda}_{i}}=\frac{1}{2}\times \left(\frac{R{\u2019}_{{\lambda}_{i+1}}-R{\u2019}_{{\lambda}_{i}}}{{\lambda}_{i+1}-{\lambda}_{i}}+\frac{R{\u2019}_{{\lambda}_{i}}-R{\u2019}_{{\lambda}_{i-1}}}{{\lambda}_{i}-{\lambda}_{i-1}}\right)$ |

^{1}i−1 and i+1 denote the former and latter band of band i, λ is the wavelength.

SIs | Expression |
---|---|

Difference index (DI) | $p-q$ |

Ratio index (RI) | $\frac{p}{q}$ |

Normalized difference index (NDI) | $\frac{p-q}{p+q}$ |

Square root index of difference (DSI) | $\sqrt{p-q}$ |

^{1}p and q are the transformed spectra values corresponding to any two bands and p ≠ q.

All Samples | Samples in Bare Soil Areas | Samples in Vegetation-Covered Areas | |
---|---|---|---|

Number of samples | 92 | 38 | 54 |

Range (g/kg) | 10.27–47.80 | 10.27–34.40 | 10.96–47.80 |

Mean (g/kg) | 25.17 | 23.45 | 26.36 |

Standard deviation(g/kg) | 7.88 | 6.65 | 8.49 |

Coefficient of variation (%) | 31.32 | 28.36 | 32.22 |

Spectral Transformation Type | Wavelengths of Bands of Low Quality (nm) | The Number of Bands Removed |
---|---|---|

OR/RR/SRR | 395–456; 1006–1014; 1122–1156; 1341–1442; 1795–1947; 2484–2501 | 36 |

FDR | 395–559; 585–637; 679; 748–1089; 1122–1156; 1207–1308; 1341–1442; 1475–1526; 1660–1762; 1795–1947; 1981; 2014–2082; 2233–2384; 2484–2501 | 125 |

RFDR | 395–473; 550–662; 748–757; 774; 791–808; 885; 920; 996–1022; 1122–1156; 1341–1442; 1475–1510; 1762–1947; 1981; 2014–2031; 2132–2250; 2334–2401; 2434–2501 | 86 |

SRFDR | 395–508; 524–559; 576–637; 748–1089; 1122–1156; 1190–1308; 1341–1442; 1475–1526; 1660–1712; 1762; 1795–1947; 1981; 2014–2082; 2216–2401; 2450–2501 | 127 |

SDR | 395–456; 473–671; 696–1073; 1106; 1122–1156; 1207–1291; 1341–1442; 1492–1745; 1795–1947; 1998; 2031–2216; 2249–2367; 2434–2501 | 147 |

TRs | Nb^{.1} | Bare Soil Region | Vegetation-Covered Area | ||||
---|---|---|---|---|---|---|---|

Nsb ^{2} | $\mathbf{Maximum}\left|\mathit{\rho}\right|$ | WLsb ^{3} (nm) | Nsb ^{2} | $\mathbf{Maximum}\left|\mathit{\rho}\right|$ | WLsb ^{3} (nm) | ||

OR | 130 | 0 | 52 | 0.558 ^{***} | 533–610, 696–1106 (722) | ||

RR | 130 | 0 | 62 | 0.573 ^{***} | 524–1106 (722) | ||

SRR | 130 | 0 | 56 | 0.563 ^{***} | 524–636, 696–1106 (722) | ||

FDR | 41 | 3 | 0.350 ^{*} | 662, 1459, 1779 | 6 | 0.502 ^{***} | 687, 696, 1106, 1173, 1190, 132445 |

RFDR | 80 | 2 | 0.370 ^{*} | 2048, 2317 | 3 | 0.381 ^{**} | 687, 1694, 1745 |

SRFDR | 39 | 5 | 0.462 ^{**} | 654, 662, 671, 1459, 1779 | 5 | 0.477 ^{***} | 516, 1106, 1173, 1324, 1745 |

SDR | 19 | 6 | 0.493 ^{**} | 1173, 1190, 1308, 1425, 1762, 1779 | 4 | 0.462 ^{***} | 1173, 1190, 1762, 1779 |

^{1}Nb stands for the number of available bands.

^{2}Nsb stands for the number of sensitive bands.

^{3}WLsb stands for the wavelength of the sensitive band.

**: Wavelength of the maximum correlation coefficient.**

**Value**^{*}Correlation is significant at 0.05 level.

^{**}Correlation is significant at 0.01 level.

^{***}Correlation is significant at 0.001 level.

Type | $\mathbf{Number}\mathbf{of}\mathbf{SIs}\mathbf{with}\left|\mathit{\rho}\right|0.6$ | $|\mathit{\rho}{|}_{\mathbf{max}}$ | $\mathbf{Index}\mathbf{Formula}\mathbf{Corresponding}\mathbf{to}|\mathit{\rho}{|}_{\mathbf{max}}$ | |
---|---|---|---|---|

OR | DI | 1 | 0.612 ^{***} | ${R}_{1459nm}-{R}_{2014nm}$ |

RI | 6 | 0.627 ^{***} | ${R}_{654nm}/{R}_{679nm}$ | |

NDI | 5 | 0.626 ^{***} | $\left({R}_{654nm}-{R}_{679nm}\right)/\left({R}_{654nm}+{R}_{679nm}\right)$ | |

DSI | 0 | 0.548 ^{***} | $\sqrt{{R}_{1543nm}-{R}_{2216nm}}$ | |

RR | DI | 3 | 0.614 ^{***} | $1/{R}_{1493nm}-1/{R}_{2014nm}$ |

RI | 6 | 0.623 ^{***} | $\left(1/{R}_{679nm}\right)/\left(1/{R}_{654nm}\right)$ | |

NDI | 5 | 0.626 ^{***} | $\frac{1/{R}_{654nm}-1/{R}_{679nm}}{1/{R}_{654nm}+1/{R}_{679nm}}$ | |

DSI | 0 | 0.426 ^{**} | $\sqrt{1/{R}_{559nm}-1/{R}_{671nm}}$ | |

FDR | DI | 0 | 0.518 ^{***} | $R{\u2019}_{662nm}-R{\u2019}_{1779nm}$ |

RI | 0 | 0.488 ^{**} | $R{\u2019}_{1190nm}/R{\u2019}_{2216nm}$ | |

NDI | 1 | 0.603 ^{***} | $\left(R{\u2019}_{662nm}-R{\u2019}_{2216nm}\right)/\left(R{\u2019}_{662nm}+R{\u2019}_{2216nm}\right)$ | |

DSI | 0 | 0.555 ^{***} | $\sqrt{R{\u2019}_{662nm}-R{\u2019}_{1779nm}}$ | |

RFDR | DI | 0 | 0.488 ^{**} | ${\left(1/{R}_{2048nm}\right)}^{\u2019}-\left(1/{R}_{2081nm}\right)\u2019$ |

RI | 0 | 0.587 ^{***} | ${\left(1/{R}_{765nm}\right)}^{\u2019}/{\left(1/{R}_{1291nm}\right)}^{\u2019}$ | |

NDI | 2 | 0.625 ^{***} | $\frac{{\left(1/{R}_{1106nm}\right)}^{\u2019}-\left(1/{R}_{2048nm}\right)\u2019}{{\left(1/{R}_{1106nm}\right)}^{\u2019}+\left(1/{R}_{2048nm}\right)\u2019}$ | |

DSI | 0 | 0.494 ^{**} | $\sqrt{{\left(1/{R}_{1728nm}\right)}^{\u2019}-\left(1/{R}_{2301nm}\right)\u2019}$ |

^{**}Correlation is significant at 0.01 level.

^{***}Correlation is significant at 0.001 level.

Type | $\mathbf{Number}\mathbf{of}\mathbf{SIs}\mathbf{with}\left|\mathit{\rho}\right|0.6$ | $|\mathit{\rho}{|}_{\mathbf{max}}$ | $\mathbf{Index}\mathbf{Formula}\mathbf{Corresponding}\mathbf{to}|\mathit{\rho}{|}_{\mathbf{max}}$ | |
---|---|---|---|---|

OR | DI | 0 | 0.576 ^{***} | ${R}_{722nm}-{R}_{2468nm}$ |

RI | 19 | 0.604 ^{***} | ${R}_{525nm}/{R}_{2267nm}$ | |

NDI | 3 | 0.601 ^{***} | $\left({R}_{525nm}-{R}_{2250nm}\right)/\left({R}_{525nm}+{R}_{2250nm}\right)$ | |

DSI | 0 | 0.569 ^{***} | $\sqrt{{R}_{722nm}-{R}_{2468nm}}$ | |

RR | DI | 84 | 0.634 ^{***} | $1/{R}_{722nm}-1/{R}_{1291nm}$ |

RI | 0 | 0.587 ^{***} | $\left(1/{R}_{525nm}\right)/\left(1/{R}_{1510nm}\right)$ | |

NDI | 3 | 0.601 ^{***} | $\frac{1/{R}_{525nm}-1/{R}_{2250nm}}{1/{R}_{525nm}+1/{R}_{2250nm}}$ | |

DSI | 83 | 0.639 ^{***} | $\sqrt{1/{R}_{551nm}-1/{R}_{1998nm}}$ | |

FDR | DI | 0 | 0.575 ^{***} | $R{\u2019}_{671nm}-R{\u2019}_{1173nm}$ |

RI | 0 | 0.490 ^{**} | $R{\u2019}_{1173nm}/R{\u2019}_{1627nm}$ | |

NDI | 0 | 0.489 ^{***} | $\left(R{\u2019}_{705nm}-R{\u2019}_{1173nm}\right)/\left(R{\u2019}_{705nm}+R{\u2019}_{1173nm}\right)$ | |

DSI | 0 | 0.581 ^{***} | $\sqrt{R{\u2019}_{671nm}-R{\u2019}_{1173nm}}$ | |

RFDR | DI | 0 | 0.421 ^{**} | ${\left(1/{R}_{1745nm}\right)}^{\u2019}-\left(1/{R}_{2301nm}\right)\u2019$ |

RI | 0 | 0.393 ^{**} | ${\left(1/{R}_{868nm}\right)}^{\u2019}/{\left(1/{R}_{1577nm}\right)}^{\u2019}$ | |

NDI | 0 | 0.468 ^{***} | $\frac{{\left(1/{R}_{688nm}\right)}^{\u2019}-\left(1/{R}_{1745nm}\right)\u2019}{{\left(1/{R}_{688nm}\right)}^{\u2019}+\left(1/{R}_{1745nm}\right)\u2019}$ | |

DSI | 0 | 0.348 ^{**} | $\sqrt{{\left(1/{R}_{1241nm}\right)}^{\u2019}-\left(1/{R}_{1627nm}\right)\u2019}$ |

^{**}Correlation is significant at 0.01 level.

^{***}Correlation is significant at 0.001 level.

Soil Type | Area Proportion (%) | The Minimum Value of SOMC (g/kg) | The Maximum Value of SOMC (g/kg) | Average SOMC (g/kg) |
---|---|---|---|---|

Cambisols | 70.28 | 7.02*10^{−4} | 49.42 | 24.39 |

Regosols | 9.38 | 0.03 | 45.51 | 24.39 |

Luvisols | 5.67 | 2.61*10^{−3} | 45.44 | 24.05 |

Other soil types ^{1} | 14.68 | 7.16*10^{−3} | 48.75 | 23.41 |

^{1}Other soil types include greyzems, anthrosols, gleysols, fluvisols, nitisols and arenosols.

Land-Use Type | The Minimum Value of SOMC (g/kg) | The Maximum Value of SOMC (g/kg) | Average SOMC (g/kg) |
---|---|---|---|

Paddy field | 1.00*10^{−3} | 49.42 | 25.34 |

Dry land | 0.02 | 48.75 | 23.23 |

Nursery | 0.11 | 45.43 | 21.73 |

All | 1.00*10^{−3} | 49.42 | 24.23 |

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## Share and Cite

**MDPI and ACS Style**

Yang, Y.; Shang, K.; Xiao, C.; Wang, C.; Tang, H.
Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China. *ISPRS Int. J. Geo-Inf.* **2022**, *11*, 111.
https://doi.org/10.3390/ijgi11020111

**AMA Style**

Yang Y, Shang K, Xiao C, Wang C, Tang H.
Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China. *ISPRS International Journal of Geo-Information*. 2022; 11(2):111.
https://doi.org/10.3390/ijgi11020111

**Chicago/Turabian Style**

Yang, Yayu, Kun Shang, Chenchao Xiao, Changkun Wang, and Hongzhao Tang.
2022. "Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China" *ISPRS International Journal of Geo-Information* 11, no. 2: 111.
https://doi.org/10.3390/ijgi11020111