Response of Evapotranspiration (ET) to Climate Factors and Crop Planting Structures in the Shiyang River Basin, Northwestern China
<p>Study area of the Liangzhou District (LD). The LD is mosaicked by 2 Landsat-8 images.</p> "> Figure 2
<p>Monthly variations in temperature (Ta), precipitation (P), and vapor pressure difference (VPD) during the study period.</p> "> Figure 3
<p>The workflow of crop classification using the random forest method.</p> "> Figure 4
<p>Crop classification accuracy with single-band and multi-temporal information.</p> "> Figure 5
<p>Crop classification’s overall accuracy with different band number combinations. The scatter plot in the figure represents the overall accuracy for different band combinations.</p> "> Figure 6
<p>Wheat and corn distribution map extracted from Landsat-8 Operational Land Imager (OLI) data in the Liangzhou District (LD): (<b>a</b>) the result for 2019, and (<b>b</b>) the result for 2020.</p> "> Figure 7
<p>Estimation results for the normalized difference vegetation index (NDVI): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; (<b>c</b>) the result from 13 June 2019; and (<b>d</b>) the result from 1 September 2019.</p> "> Figure 8
<p>Estimation results for the land surface temperature (LST): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; (<b>c</b>) the result from 13 June 2019; and (<b>d</b>) the result from 1 September 2019.</p> "> Figure 8 Cont.
<p>Estimation results for the land surface temperature (LST): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; (<b>c</b>) the result from 13 June 2019; and (<b>d</b>) the result from 1 September 2019.</p> "> Figure 9
<p>Estimation results for evapotranspiration (ET, mm/h): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; (<b>c</b>) the result from 13 June 2019; (<b>d</b>) the result from 1 September 2019; and (<b>e</b>) represents the instantaneous ET frequency map in each period.</p> "> Figure 9 Cont.
<p>Estimation results for evapotranspiration (ET, mm/h): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; (<b>c</b>) the result from 13 June 2019; (<b>d</b>) the result from 1 September 2019; and (<b>e</b>) represents the instantaneous ET frequency map in each period.</p> "> Figure 10
<p>Estimation results for evapotranspiration (ET, mm/d): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; (<b>c</b>) the result from 13 June 2019; (<b>d</b>) the result from 1 September 2019; and (<b>e</b>) represents the frequency of daily ET in each period.</p> "> Figure 10 Cont.
<p>Estimation results for evapotranspiration (ET, mm/d): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; (<b>c</b>) the result from 13 June 2019; (<b>d</b>) the result from 1 September 2019; and (<b>e</b>) represents the frequency of daily ET in each period.</p> "> Figure 11
<p>Daily evapotranspiration (ET) results for 24 June 2020.</p> "> Figure 12
<p>Daily evapotranspiration (ET) extracted by mask in the wheat area (mm/d): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; and (<b>c</b>) the result from 13 June 2019. Since the wheat was harvested in September, no date from the month is analyzed here. Finally, (<b>d</b>–<b>f</b>) represent the frequency of daily ET on the corresponding dates.</p> "> Figure 12 Cont.
<p>Daily evapotranspiration (ET) extracted by mask in the wheat area (mm/d): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; and (<b>c</b>) the result from 13 June 2019. Since the wheat was harvested in September, no date from the month is analyzed here. Finally, (<b>d</b>–<b>f</b>) represent the frequency of daily ET on the corresponding dates.</p> "> Figure 13
<p>Daily evapotranspiration (ET) extracted by mask in the corn area (mm/d): (<b>a</b>) the result from 12 May 2019; (<b>b</b>) the result from 28 May 2019; (<b>c</b>) the result from 13 June 2019; (<b>d</b>) the result from 1 September 2019; and (<b>e</b>–<b>h</b>) represent the frequency of daily ET on the corresponding dates.</p> "> Figure 14
<p>The color-coded correlation matrices for ET interannual variation.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Materials
2.2.1. Remote Sensing Data
2.2.2. Meteorological Data
2.2.3. Sample Data
2.3. Data Analysis
2.3.1. Crop Classification Method
2.3.2. Evapotranspiration Remote Sensing Estimation Method
Submodel of Soil Evaporation
Submodel of Transpiration of Vegetation
Vegetation–Soil Mixed Zone Model
Vegetation Index and Fractional Vegetation Cover
Land Surface Temperature
Net Solar Radiation
Soil Heat Flux
Net Solar Radiation of Reference Surface
Temperature of Reference Surface
Soil Heat Flux of Reference Surface
2.3.3. Timescale Expansion of Evapotranspiration
2.3.4. Data Analysis
3. Results
3.1. Crop Identification
3.1.1. Classification Experiment 1: Single Band with Multi-Temporal
3.1.2. Classification Experiment 2: Multi-Band with Multi-Temporal
3.1.3. Crop Classification Result in the Liangzhou District
3.2. Evapotranspiration Estimation
3.2.1. Key Model Parameters Estimation
3.2.2. Variation of Evapotranspiration
3.3. The Response of Evapotranspiration to Planting Structure Factors
3.4. The Response of Evapotranspiration to Climatic Factors
4. Discussion
4.1. The Influence of Input Spectral Bands on the Identification of Crop Planting Structures
4.2. The Influence of the Spatial Heterogeneity of Planting Structures on Evapotranspiration
4.3. The Influence of Meteorological Factors on the Estimation of Evapotranspiration
5. Conclusions
- (1)
- Using a random forest classifier, a combination of the green and SWIR-1 bands from multi-temporal Landsat-8 images was found to be the optimal feature set to extract the crop planting structures in the LD. The wheat planting areas were 31.594 kha and 24.241 kha in 2019 and 2020, respectively, while the corn areas were 44.505 kha and 47.322 kha for those same years. The accuracy of the planting structure classification results ranged from 85.82% to 98.25%.
- (2)
- The integration of crop planting structures with the 3T model enhanced the realism and reliability of ET estimation. The spatial differences between the two crops were more pronounced from June to September. Among the four selected dates, the average daily ET in the wheat area was 1.16 mm/d, 3.44 mm/d, and 5.02 mm/d, respectively (wheat was harvested around 20 July). While in the corn area, it was 1.87 mm/d, 2.03 mm/d, 2.80 mm/d, and 3.20 mm/d, respectively.
- (3)
- Concerning the climatic factors considered in this study, ET’s spatiotemporal variations were primarily driven by Ta (R = 0.80, p ≤ 0.05). The research quantified the contributions of crop planting structures and climate factors to variations in ET, providing a basis for better understanding the impact of crop planting structure on remote sensing-based ET estimation. However, this study focused on major cash crops, and further exploration is needed to investigate the effects of more refined and diverse crop planting structures on ET using remote sensing techniques.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Crop Type | April | May | June | July | August | September | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
E | M | L | E | M | L | E | M | L | E | M | L | E | M | L | E | M | L | |
Wheat | sow | grow | maturity | harvest | ||||||||||||||
Corn | sow | grow | maturity | harvest | ||||||||||||||
Veg. | sow | harvest |
Year | Date | Day of Year (DOY) | Path/Row | Transit Time |
---|---|---|---|---|
2019 | 12 May | 132 | 132/034 | 11:49 |
21 May | 141 | 131/034 | 11:43 | |
28 May | 148 | 132/034 | 11:49 | |
6 June | 157 | 131/034 | 11:43 | |
13 June | 164 | 132/034 | 11:49 | |
24 July | 205 | 131/034 | 11:43 | |
9 August | 221 | 131/034 | 11:43 | |
25 August | 237 | 131/034 | 11:43 | |
1 September | 244 | 132/034 | 11:49 | |
26 Septrmber | 269 | 131/034 | 11:43 | |
2020 | 24 June | 176 | 131/034 | 11:43 |
1 July | 183 | 132/034 | 11:49 | |
10 July | 192 | 131/034 | 11:43 | |
17 July | 199 | 132/034 | 11:49 | |
18 August | 231 | 132/034 | 11:49 |
Satellite | Band Name | Wavelength (nm) | Spatial Resolution (m) |
---|---|---|---|
Landsat-8 OLI | Blue (B2) | 450–515 | 30 |
Landsat-8 OLI | Green (B3) | 525–600 | 30 |
Landsat-8 OLI | Red (B4) | 630–680 | 30 |
Landsat-8 OLI | NIR (B5) | 845–885 | 30 |
Landsat-8 OLI | SWIR-1 (B6) | 1560–1660 | 30 |
Landsat-8 OLI | SWIR-2 (B7) | 2100–2300 | 30 |
Station No. | Station Name | Longitude | Latitude | Altitude (m) |
---|---|---|---|---|
52681 | Minqin | 103°08′ | 38°63 | 1367.8 |
52674 | Yongchang | 101°97′ | 38°23 | 1976.9 |
52679 | Wuwei | 102°67′ | 37°92 | 1531.5 |
52787 | Wushaoling | 102°87′ | 37°02 | 3045.1 |
52797 | Jingtai | 104°05′ | 37°18 | 1630.9 |
Crop Types | Number of Training Plots | Number of Testing Plots | Number of Training Pixels | Number of Testing Pixels |
---|---|---|---|---|
Wheat | 72 | 37 | 2384 | 665 |
Corn | 96 | 42 | 3248 | 963 |
Number of Temporal | Date | Overall Accuracy | Kappa Coefficient |
---|---|---|---|
1 | DOY148 | 90.00% | 0.84 |
1 | DOY164 | 87.40% | 0.79 |
1 | DOY244 | 89.95% | 0.84 |
2 | DOY148, DOY164 | 91.49% | 0.86 |
2 | DOY148, DOY244 | 92.60% | 0.88 |
2 | DOY164, DOY244 | 94.00% | 0.90 |
3 | DOY148, DOY164, DOY244 | 96.93% | 0.95 |
Number of Band Combination | Band of Landsat-8 OLI | Overall Accuracy | Kappa Coefficient |
---|---|---|---|
2 | Green, SWIR-1 | 97.77% | 0.96 |
3 | Blue, Green, Red | 97.48% | 0.96 |
4 | Blue, Green, Red, SWIR-2 | 97.39% | 0.96 |
5 | Blue, Green, Red, SWIR-1, SWIR-2 | 97.16% | 0.95 |
6 | Blue, Green, Red, NIR, SWIR-1, SWIR-2 | 96.93% | 0.95 |
Crop Types | Classification Results | Total | Producer Accuracy | ||
---|---|---|---|---|---|
Wheat | Corn | Others | |||
Wheat | 646 | 0 | 0 | 646 | 97.14% |
Corn | 19 | 963 | 29 | 1011 | 100.00% |
Others | 0 | 0 | 493 | 493 | 94.44% |
Total | 665 | 963 | 522 | 2150 | |
User Accuracy | 100.00% | 85.25% | 100 | ||
Overall Accuracy = 97.77% Kappa = 0.96 |
Crop Types | 2019 | 2020 | Change in Area | ||
---|---|---|---|---|---|
Number of Pixels | Area (kha) | Number of Pixels | Area (kha) | ||
Wheat | 351,221 | 31.594 | 269,483 | 24.241 | −7.353 |
Corn | 494,747 | 44.505 | 526,061 | 47.322 | 2.817 |
Total | 845,968 | 76.099 | 795,544 | 71.563 | −4.536 |
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Yang, X.; Shi, X.; Zhang, Y.; Tian, F.; Ortega-Farias, S. Response of Evapotranspiration (ET) to Climate Factors and Crop Planting Structures in the Shiyang River Basin, Northwestern China. Remote Sens. 2023, 15, 3923. https://doi.org/10.3390/rs15163923
Yang X, Shi X, Zhang Y, Tian F, Ortega-Farias S. Response of Evapotranspiration (ET) to Climate Factors and Crop Planting Structures in the Shiyang River Basin, Northwestern China. Remote Sensing. 2023; 15(16):3923. https://doi.org/10.3390/rs15163923
Chicago/Turabian StyleYang, Xueyi, Xiaojing Shi, Yaling Zhang, Fei Tian, and Samuel Ortega-Farias. 2023. "Response of Evapotranspiration (ET) to Climate Factors and Crop Planting Structures in the Shiyang River Basin, Northwestern China" Remote Sensing 15, no. 16: 3923. https://doi.org/10.3390/rs15163923
APA StyleYang, X., Shi, X., Zhang, Y., Tian, F., & Ortega-Farias, S. (2023). Response of Evapotranspiration (ET) to Climate Factors and Crop Planting Structures in the Shiyang River Basin, Northwestern China. Remote Sensing, 15(16), 3923. https://doi.org/10.3390/rs15163923