A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data
<p>Location and meteorological stations of the study area.</p> "> Figure 2
<p>The seasonal pattern of the Normalized Difference Vegetation Index (NDVI) corresponding to different wine grape phenology.</p> "> Figure 3
<p>Survey route and sample set of the study area based on field survey and visual interpretation of Google Earth.</p> "> Figure 4
<p>The framework of wine grape late frost damage monitoring.</p> "> Figure 5
<p>The ranking of top 53 features of importance.</p> "> Figure 6
<p>The performance of OA and kappa coefficient changes with the number of feature variables.</p> "> Figure 7
<p>Wine grape map of the study area.</p> "> Figure 8
<p>Scatter plot of the LST calculated by the two algorithms versus CLDAS-V2.0 LST: (<b>a</b>) LST_MW; (<b>b</b>) LST_SW.</p> "> Figure 9
<p>Statistics of valid pixel ratio before and after interpolation.</p> "> Figure 10
<p>Scatter plot of fused MODIS LST and CLDAS-V2.0 LST.</p> "> Figure 11
<p>Results of data fusion: (<b>a</b>) Results of data fusion in temporal resolution; (<b>b</b>) results of data fusion in spatial resolution (comparisons of 1 km × 1 km MODIS LST and 100 m × 100 m LST fused by ESTARFM).</p> "> Figure 11 Cont.
<p>Results of data fusion: (<b>a</b>) Results of data fusion in temporal resolution; (<b>b</b>) results of data fusion in spatial resolution (comparisons of 1 km × 1 km MODIS LST and 100 m × 100 m LST fused by ESTARFM).</p> "> Figure 12
<p>Calibration and validation of daily minimum air temperature: (<b>a</b>) Calibration of <span class="html-italic">T</span><sub>min</sub> estimation model; (<b>b</b>) scatter plot of estimated <span class="html-italic">T</span><sub>min</sub> using fused daily high resolution LST and measured <span class="html-italic">T<sub>mi</sub></span><sub>n</sub> from meteorological stations.</p> "> Figure 13
<p>Late frost damaged area of wine grapes in the study area in April 2020.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Satellite Data and Preprocessing
2.3. Meteorological Data
2.4. Field Survey and Sample Set
2.5. Methods
2.5.1. Extraction of the Wine Grape Planting Area Using RF
2.5.2. Data Fusion of LST
2.5.3. Estimation Model of Daily Minimum Air Temperature
2.5.4. Mapping the Late Frost Damaged Area
3. Results
3.1. Extraction of the Wine Grape Planting Area
3.2. Data Fusion of Landsat-8 LST and MODIS LST Using the ESTARFM Method
3.2.1. LST Retrieval Using Landsat-8 Thermal Infrared Data
3.2.2. Cloud Gap-Filling of MODIS LST Data
3.2.3. Data Fusion of Landsat-8 LST and MODIS LST Using the ESTARFM Method
3.3. Calibration and Validation of Daily Minimum Air Temperature Estimation Using the Downscaled LST Data
3.4. Mapping of the Late Frost Damaged Area
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Order Number | Date | Satellite |
---|---|---|
1 | 5 April 2019 | Landsat-8 |
2 | 6 July 2019 | Sentinel-2 |
3 | 26 July 2019 | Landsat-8 |
4 | 15 August 2019 | Sentinel-2 |
5 | 30 September 2019 | GF-1 |
6 | 22 November 2019 | GF-1 |
7 | 6 February 2020 | Sentinel-2 |
8 | 22 March 2020 | Landsat-8 |
9 | 16 April 2020 | Sentinel-2 |
10 | 23 April 2020 | Landsat-8 |
Feature Type | Order Number of Satellite Data | Abbreviation of Feature | Number of Features |
---|---|---|---|
Spectral feature | 1–10 1 | Blue 1~Blue 10, Green 1~Green 10 Red 1~Red 10, Nir 1~Nir 10 1Red-edge 2, 1Red-edge 4, 1Red-edge 7, 1Red-edge 9 2Red-edge 2, 2Red-edge 4, 2Red-edge 7, 2Red-edge 9 3Red-edge 2, 3Red-edge 4, 3Red-edge 7, 3Red-edge 9 2 | 52 |
Vegetation index feature | 1–10 1 | NDVI 1~NDVI 10 1NDRE 2, 1NDRE 4, 1NDRE 7, 1NDRE 9 2NDRE 2, 2NDRE 4, 2NDRE 7, 2NDRE 9 3NDRE 2, 3NDRE 4, 3NDRE 7, 3NDRE 9 | 22 |
Texture feature | 1–10 1 | Second 1~Second 10, Correlation 1~Correlation 10, | 50 |
Entropy 1~Entropy 10, Variance 1~Variance 10, Contrast 1~Contrast 10 |
Date of Predicted MODIS Images | Date of Landsat-8 Reference Image | Date of MODIS Reference Images |
---|---|---|
1 April 2020~22 April 2020 | 22 March 2020, 23 April 2020 | 22 March 2020, 23 April 2020 |
23 April 2020~30 April 2020 | 23 April 2020, 9 May 2020 | 23 April 2020, 9 May 2020 |
Class | Wine Grape | Farmland | Woodland | Meadow | Desert Steppe | Desert | Building | Water | UA (%) |
---|---|---|---|---|---|---|---|---|---|
Wine grape | 5274 | 112 | 110 | 104 | 53 | 80 | 113 | 0 | 90.22 |
Farmland | 56 | 4111 | 12 | 11 | 7 | 0 | 0 | 12 | 97.68 |
Woodland | 130 | 17 | 9580 | 132 | 49 | 68 | 464 | 0 | 91.76 |
Meadow | 135 | 74 | 701 | 11317 | 51 | 4 | 961 | 63 | 85.05 |
Desert steppe | 63 | 10 | 46 | 380 | 5935 | 54 | 0 | 27 | 91.10 |
Desert | 24 | 13 | 55 | 7 | 12 | 1544 | 100 | 0 | 88.00 |
Building | 108 | 10 | 494 | 847 | 390 | 90 | 8018 | 0 | 80.53 |
Water | 0 | 31 | 0 | 34 | 26 | 0 | 32 | 14707 | 99.17 |
PA (%) | 91.09 | 93.91 | 87.11 | 88.19 | 90.99 | 83.91 | 82.76 | 99.31 | |
OA (%) | 90.47 | ||||||||
Kappa | 0.89 |
Planting Area | Slight Damage | Moderate Damage | Severe Damage | Total | ||||
---|---|---|---|---|---|---|---|---|
DA 1 (ha) | DR 2 (%) | DA 1 (ha) | DR 2 (%) | DA 1 (ha) | DR 2 (%) | DA 1 (ha) | DR 2 (%) | |
Yinchuan | 3159 | 14.81 | 5051 | 23.68 | 8501 | 39.85 | 16711 | 78.33 |
Shizuishan | 178 | 15.66 | 195 | 17.15 | 487 | 42.83 | 860 | 75.64 |
Qingtongxia | 1256 | 13.41 | 2123 | 22.66 | 3983 | 42.52 | 7362 | 78.59 |
Hongsipu | 1051 | 13.14 | 1471 | 18.38 | 3410 | 42.63 | 5932 | 74.15 |
Total | 5644 | 14.17 | 8840 | 22.19 | 16381 | 41.12 | 30,865 | 77.48 |
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Li, W.; Huang, J.; Yang, L.; Chen, Y.; Fang, Y.; Jin, H.; Sun, H.; Huang, R. A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data. Remote Sens. 2021, 13, 3231. https://doi.org/10.3390/rs13163231
Li W, Huang J, Yang L, Chen Y, Fang Y, Jin H, Sun H, Huang R. A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data. Remote Sensing. 2021; 13(16):3231. https://doi.org/10.3390/rs13163231
Chicago/Turabian StyleLi, Wenjie, Jingfeng Huang, Lingbo Yang, Yan Chen, Yahua Fang, Hongwei Jin, Han Sun, and Ran Huang. 2021. "A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data" Remote Sensing 13, no. 16: 3231. https://doi.org/10.3390/rs13163231
APA StyleLi, W., Huang, J., Yang, L., Chen, Y., Fang, Y., Jin, H., Sun, H., & Huang, R. (2021). A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data. Remote Sensing, 13(16), 3231. https://doi.org/10.3390/rs13163231