Phenology-Based Remote Sensing Assessment of Crop Water Productivity
Abstract
:1. Introduction
2. Study Area and Data Acquisition
2.1. Study Area
2.2. Data Acquisition
2.2.1. Satellite Data
2.2.2. Meteorological Data
2.2.3. Field Data
3. Methods
3.1. Crop Phenological Period Detection Method
3.1.1. The EVI Curve Extraction from the Fusion Data of MODIS and Sentinel-2
3.1.2. Crop Phenological Period Detection
3.2. Crop ET
3.2.1. Twenty-four-Hour ET (ET24-hour)
3.2.2. Daily ET (ETdaily)
- ETC at the weather station point based on the Penman–Monteith equation.
- 2.
- ETdaily images based on ET24-hour images and ETc at the weather station
3.2.3. Seasonal ET (ETseason)
3.3. Crop Yield
3.4. CWP
4. Results
4.1. Crop Phenological Period
4.2. Crop ET
4.3. Crop Yield
4.4. CWP
5. Discussion
5.1. Analysis of Phenological Period Results
5.2. Analysis of ET Results Based on Phenology Results
5.3. Relationships among CWP, Yield, and ET
6. Conclusions
- The phenological periods detected from remote sensing images are in good agreement with other studies. The average duration of the growing season for wheat and maize were 197 days and 97 days, respectively.
- The daily ET estimated by the SEBAL model and P-M equation is consistent with the measured ET, with R2, NSE, and RMSE values of 0.96, 0.90, and 0.56 mm/day, respectively. The average ETseason of wheat and maize from 2020 to 2021 were 428.51 mm and 422.85 mm, respectively.
- The average yields of wheat and maize were 7169 kg/ha and 6081 kg/ha, respectively, and the yield estimated by the dry mass–harvest index was consistent with the measured yield. Based on the analysis of yield and ETseason, there is a negative parabolic relationship between crop yield and ETseason.
- The average CWPs of wheat and maize were 1.6 kg/m3 and 1.39 kg/m3, respectively, and there was a close linear relationship between CWP and crop yield.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Datasets | Source | Path, Row/Tile | Spatial Resolution | Time Resolution |
---|---|---|---|---|
MODIS(MOD09A1) | https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 9 December 2021) | h27v05 | 500 m and 250 m | 8 d |
Landsat7 and 8 | https://earthexplorer.usgs.gov/ (accessed on 15 December 2021) | 122,037 | 30 m | 16 d |
Sentinel-2 | https://scihub.copernicus.eu/ (accessed on 17 December 2021) | T50SLB and T50SMB | 10 m | 5 d |
Datasets | 2020 | 2021 |
---|---|---|
Landsat7 | 249,297,313 | 123,155,251 |
Landsat8 | 177,241 | 019,051,083,099,211,243,275,307 |
Sentinel-2 | 315,320,335,355,365 | 009,014,019,029,049,084,094,099,109,124,129,184,254,264,274,279,304,314 |
Phenological Period | Sowing–Emergence | Emergence–Heading | Heading–Maturity | Growing Season |
---|---|---|---|---|
Kc (wheat) | 0.67 | 0.75 | 0.92 | 0.75 |
Growing Season | 0~18% | 18~54% | 54~86% | 86~100% |
Kc (maize) | 0.1 | 1.1 | 1.2 | 0.6 |
Other Studies | Xinmaqiao Station | Predicted Value | ||||
---|---|---|---|---|---|---|
Wheat | Maize | Wheat | Maize | Wheat | Maize | |
Sowing period | 10.13 | 6.01 | 10.12–11.3 | 6.6–7.4 | 10.10–11.20 | 5.31–6.31 |
Heading period | 4.21 | 8.10 | 4.8–4.24 | 7.31–8.15 | 4.2–5.2 | 7.26–8.16 |
Maturity period | 5.31 | 9.30 | 4.25–5.25 | 8.16–9.21 | 5.2–5.31 | 8.31–10.5 |
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Gao, H.; Zhang, X.; Wang, X.; Zeng, Y. Phenology-Based Remote Sensing Assessment of Crop Water Productivity. Water 2023, 15, 329. https://doi.org/10.3390/w15020329
Gao H, Zhang X, Wang X, Zeng Y. Phenology-Based Remote Sensing Assessment of Crop Water Productivity. Water. 2023; 15(2):329. https://doi.org/10.3390/w15020329
Chicago/Turabian StyleGao, Hongsi, Xiaochun Zhang, Xiugui Wang, and Yuhong Zeng. 2023. "Phenology-Based Remote Sensing Assessment of Crop Water Productivity" Water 15, no. 2: 329. https://doi.org/10.3390/w15020329
APA StyleGao, H., Zhang, X., Wang, X., & Zeng, Y. (2023). Phenology-Based Remote Sensing Assessment of Crop Water Productivity. Water, 15(2), 329. https://doi.org/10.3390/w15020329