Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
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
Index Type | Abbreviation | Formula | Properties | References |
---|---|---|---|---|
Soil SI | SOC1 | SOMC | [22] | |
SOC2 | SOMC | [22] | ||
SOC3 | SOMC | [22] | ||
NSMI | Soil moisture | [50,51] | ||
Vegetation SI | CAI | Cellulose Absorption | [52,53,54] | |
NDLI | Lignin concentration | [53,55,56,57] | ||
MSI | Leaf water content | [58,59] | ||
SATVI | 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
3.5. Application of the Optimal SIs
3.5.1. Characterization of SOMC in Soil Samples
3.5.2. Recognition of SOMC Levels in Soil Samples
3.5.3. Estimation of SOMC in Soil Samples
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
4.3. The Impacts of Soil Types and Cultivated Land-Use Type on SOMC
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 R2, 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
References
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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) | |
First-order differential reflectance (FDR) | |
First-order differential of reciprocal reflectance (RFDR) | |
First-order differential of square root reflectance (SRFDR) | |
Second-order differential reflectance (SDR) |
SIs | Expression |
---|---|
Difference index (DI) | |
Ratio index (RI) | |
Normalized difference index (NDI) | |
Square root index of difference (DSI) |
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 | WLsb 3 (nm) | Nsb 2 | 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 |
Type | ||||
---|---|---|---|---|
OR | DI | 1 | 0.612 *** | |
RI | 6 | 0.627 *** | ||
NDI | 5 | 0.626 *** | ||
DSI | 0 | 0.548 *** | ||
RR | DI | 3 | 0.614 *** | |
RI | 6 | 0.623 *** | ||
NDI | 5 | 0.626 *** | ||
DSI | 0 | 0.426 ** | ||
FDR | DI | 0 | 0.518 *** | |
RI | 0 | 0.488 ** | ||
NDI | 1 | 0.603 *** | ||
DSI | 0 | 0.555 *** | ||
RFDR | DI | 0 | 0.488 ** | |
RI | 0 | 0.587 *** | ||
NDI | 2 | 0.625 *** | ||
DSI | 0 | 0.494 ** |
Type | ||||
---|---|---|---|---|
OR | DI | 0 | 0.576 *** | |
RI | 19 | 0.604 *** | ||
NDI | 3 | 0.601 *** | ||
DSI | 0 | 0.569 *** | ||
RR | DI | 84 | 0.634 *** | |
RI | 0 | 0.587 *** | ||
NDI | 3 | 0.601 *** | ||
DSI | 83 | 0.639 *** | ||
FDR | DI | 0 | 0.575 *** | |
RI | 0 | 0.490 ** | ||
NDI | 0 | 0.489 *** | ||
DSI | 0 | 0.581 *** | ||
RFDR | DI | 0 | 0.421 ** | |
RI | 0 | 0.393 ** | ||
NDI | 0 | 0.468 *** | ||
DSI | 0 | 0.348 ** |
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 |
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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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
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 StyleYang, 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
APA StyleYang, Y., Shang, K., Xiao, C., Wang, C., & Tang, H. (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(2), 111. https://doi.org/10.3390/ijgi11020111