Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Feature Selection
2.2. Study Area
2.3. Image and Yield Data Acquistion
2.4. Regression Models
2.5. Constructing the Yield Estimation Model
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Vegetation Indices | Variables | Formula | Reference |
---|---|---|---|
Cropping Management Factor Index | CMFI | [65] | |
Transformed Soil-Adjusted Vegetation Index | TSAVI | [66,67] | |
Optimized Soil-Adjusted Vegetation Index | OSAVI | Y = 0.16 | [66,68] |
Infrared Percentage Vegetation Index | IPVI | [12] | |
Ratio Vegetation Index | RVI | [9,12] | |
Modified Soil Adjust Vegetation Index | MSAVI | [69] | |
Greenness Index | GI | [70] | |
Perpendicular Vegetation Index | PVI | [71,72] | |
Soil Adjusted Vegetation Index | SAVI | L = 0.5 | [9,12,72] |
Normalized Difference Vegetation Index | NDVI | [9,12,68] | |
Generalized Soil-Adjusted Vegetation Index | GESAVI | [73] |
GLCM Indices | Variable | Formula | Description | Expected Sign | Reference |
---|---|---|---|---|---|
GLCM Mean | Mea | The local mean grey level value in a given area. This measure helps distinguish the spectral difference between higher and lower paddy rice yield. Rice fields with a higher yield absorb more blue and red light, and reflect more green and near infrared light. | B/G/R/NIR −/+/−/+ | [74,75,76] | |
GLCM Variance | Var | A measure of heterogeneity. The variance increases when the grey level values differ from their mean. Paddy rice with higher yields usually show denser canopy cover, less shadows, and less bare soil; and thus, its surface shows little variation and lower variance. | − | [74,75,76] | |
GLCM Contrast | Con | Measures the linear dependency of the grey levels of the neighboring pixels. High contrast represents heavy textures. Similar to the characteristics of the paddy rice with higher yields mentioned above, this usually shows lower contrast. | − | [74,75,76,77] | |
GLCM Dissimilarity | Dis | Defines the variation of grey level pairs in an image. It is the closest to Contrast with a difference in the weight. Contrast will always give slightly higher values than Dissimilarity. | − | [74,75,76] | |
GLCM Homogeneity | Hom | Measures the level of variation in a given area. A high homogeneity refers to the textures that contain ideal repetitive structures. Paddy rice with higher yields usually show a higher homogeneity. | + | [74,75,76] | |
GLCM Correlation | Cor | A measure of the grey level linear dependence between the pixels at the specified positions relative to each other. Paddy rice with a higher yield usually shows a higher correlation. | + | [74,75,76] | |
GLCM Entropy | Ent | Measures the level of chaos in a given area. A completely random distribution would have a very high entropy because it represents chaos. A solid tone image would have an entropy value of 0. Paddy rice with higher yields usually show lower entropy. | − | [74,75,76] | |
GLCM Angular second moment | Sec | Measures the textural uniformity that is pixel pair repetitions. High angular second moment values occur when the grey level distribution is constant or periodic. Paddy rice with higher yields usually show higher angular second moment values. | + | [74,75,76,77] |
Variable Combination | Selection Methods of Variables |
---|---|
Combination 1 | 47 variables |
Combination 2 | 47 variables + Pearson correlation + Regression calculation |
Combination 3 | 47 variables + Pearson correlation + Multiple Regression calculation |
Year | Date of SPOT 1 | Num. | Max. (t. ha−1) | Min. (t. ha−1) | Aver. (t. ha−1) | Std. (t. ha−1) | Proportion to All Rice Fields | Actual Total Yield (ton) | |
---|---|---|---|---|---|---|---|---|---|
Erlin | 2016 | 19/05/2016 | 40 | 8.473 | 6.462 | 7.500 | 0.583 | 0.514% | 21.882.4 |
2017 | 07/05/2017 | 63 | 10.205 | 6.347 | 7.858 | 0.709 | 0.655% | 24.661.3 | |
Dapi | 2016 | 28/03/2016 | 26 | 11.530 | 4.864 | 8.230 | 1.393 | 0.242% | 21.311.2 |
2017 | 07/05/2017 | 57 | 9.679 | 3.726 | 8.127 | 1.036 | 0.547% | 22.232.1 |
Year | Combination | Selected Variable | OLS | SVR | GWR |
---|---|---|---|---|---|
2016 | 1 | 47 | −6.50% | 5.42% | F 2 |
2 | B, G, R, NIR, GI, MSAVI, NDVI, RVI, SecR 1 | −2.59% | 6.31% | 7.46% | |
3 | B, MSAVI, RVI, SecR | −2.53% | 6.31% | −2.47% | |
2017 | 1 | 47 | 5.13% | 7.16% | F |
2 | G, NIR, GI, RVI, TSAVI, HomG, VarR, VarNIR | 6.96% | 6.84% | 6.91% | |
3 | G, NIR, GI, RVI, TSAVI, VarR | 6.87% | 6.64% | 6.80% |
Year | Combination | Selected Variable | OLS | SVR | GWR |
---|---|---|---|---|---|
2016 | 1 | 47 | −10.35% | 11.34% | F |
2 | B, G, R, NIR, GI, MSAVI, RVI, SAVI, VarNIR | 11.07% | 11.34% | 10.34% | |
3 | B, NIR, GI, SAVI, VarNIR | 10.91% | 13.22% | 10.32% | |
2017 | 1 | 47 | −3.64% | 2.78% | F |
2 | B, R, MSAVI, SAVI, CorNIR, DisNIR, EntNIR, HomNIR, MeaNIR | −1.47% | 3.20% | −1.81% | |
3 | B, GI, MSAVI, SAVI, EntNIR, MeaNIR | −1.79% | 2.75% | 0.06% |
Area | Combination | Selected Variable | OLS | SVR | GWR |
---|---|---|---|---|---|
Erlin | 1 | 47 | 6.36% | 9.39% | F |
2 | B, R, CMFI, GI, CorN, EntNIR, HomNIR, MeaNIR | 7.84% | 8.68% | 6.31% | |
3 | B, GI, HomNIR, MeaNIR | 7.71% | 10.69% | 3.89% | |
Dapi | 1 | 47 | −2.00% | −0.33% | F |
2 | B, R, CMFI, GI, CorN, EntNIR, HomNIR, MeaNIR | −1.11% | 1.16% | −0.81% | |
3 | B, GI, HomNIR, MeaNIR | −0.98% | 1.84% | −1.03% |
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Shiu, Y.-S.; Chuang, Y.-C. Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models. Remote Sens. 2019, 11, 111. https://doi.org/10.3390/rs11020111
Shiu Y-S, Chuang Y-C. Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models. Remote Sensing. 2019; 11(2):111. https://doi.org/10.3390/rs11020111
Chicago/Turabian StyleShiu, Yi-Shiang, and Yung-Chung Chuang. 2019. "Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models" Remote Sensing 11, no. 2: 111. https://doi.org/10.3390/rs11020111
APA StyleShiu, Y. -S., & Chuang, Y. -C. (2019). Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models. Remote Sensing, 11(2), 111. https://doi.org/10.3390/rs11020111