Study on Soil Moisture Status of Soybean and Corn across the Whole Growth Period Based on UAV Multimodal Remote Sensing
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Site and Setup
2.2. Data Acquisition
2.2.1. UAV Data
2.2.2. In Situ Soil Moisture
2.3. Methods
2.3.1. Regression Model Building
2.3.2. Validation
3. Results
3.1. Modeling and Validation of Soil Moisture Content
3.1.1. The Relationship between Vegetation Indices and Soil Moisture
3.1.2. Model Performance
3.2. Contributions of Different Sensor Data
3.3. Comparison of Estimation Accuracy at Different Soil Depths
3.4. Accuracy Comparison and Optimal Estimation Depths for Different Growth Stages
3.5. Spatial Distribution of Soil Moisture in the Field
4. Discussion
5. Conclusions
- (1)
- For different crop growth stages and soil depths, multispectral remote sensing data outperformed thermal infrared remote sensing in terms of SMC estimation accuracy. Moreover, combining thermal infrared remote sensing data with multispectral data improved the accuracy of SMC estimation. The combined approach achieved a 14% increase in R2 compared to using multispectral data alone and a 39% increase compared to using thermal infrared data alone.
- (2)
- Across the entire growth period, the optimal SMC estimation depths for soybean were found to be 10 cm and 20 cm, with a significant correlation between them. The average R2 for these depths were 0.81 and 0.82, respectively. In contrast, the accuracy decreased for depths between 40 cm and 80 cm, with all R2 below 0.4. For corn, the optimal SMC estimation depths were 10 cm, 20 cm, and 40 cm, with average R2 of 0.59, 0.60, and 0.55, respectively. The performances at depths of 60 cm and 80 cm were poorer, with all R2 below 0.3.
- (3)
- From the perspective of different growth stages, the SMC estimation model performed well for both crops during the first three growth stages and decreased in accuracy during the maturity stage. On average, corn showed a decrease of 0.35 in R2, while soybean showed a decrease of 0.26. In terms of the optimal estimation depth across the first three growth stages, the optimal estimation depths for both soybean and corn are relatively stable, with soybean at 10 cm and 20 cm, and corn at 10 cm, 20 cm, and 40 cm.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sensor Name | Sensor Type | Spectral Region (µm) | Resolution (Pixels) | Field of View (H° × V°) |
---|---|---|---|---|
RGB camera | DJI Zenmuse Z3 | N/A | 1240 | 92° (wide-angle)–35° (tele) |
Multispectral camera | Micasense RedEdge-MX | 0.475, 0.560, 0.668, 0.717, 0.842 | 1280 × 960 | 47.2° × 35.4° |
Thermal camera | Flir Vue Pro R64 | 7.5–13.5 | 640 × 512 | 32.0° × 26° |
Sensor | Spectral Index | Formulation | References |
---|---|---|---|
MS | Normalized difference vegetation index (NDVI) | NDVI = (NIR − R)/(NIR + R) | [41] |
Normalized difference vegetation index 2 (NDVIgb) | NDVIgb = (G − B)/(G + B) | [42] | |
Ratio vegetation index (RVI) | RVI = NIR/R | [43] | |
Ratio vegetation index 2 (RVI2) | RVI2 = NIR/G | [44] | |
Triangular vegetation index (TVI) | TVI = 60 (NIR − G) − 100(R − G) | [45] | |
Enhanced vegetation index (EVI) | EVI = 2.5 (NIR − R)/(NIR + 6 R − 7.5B + 1) | [46] | |
Green index (GI) | GI = G/R | [47] | |
Soil-adjusted vegetation index (SAVI) | SAVI = 1.5 (NIR − R) (NIR + R + 0.5) | [48] | |
Optimized soil-adjusted vegetation index (OSAVI) | OSAVI = 1.16 (NIR − R)/(NIR + R + 0.16) | [49] | |
Structure insensitive pigment index (SIPI) | SIPI = (NIR − B)/(NIR + B) | [50] | |
Transformed chlorophyll absorption in reflectance index (TCARI) | TCARI = 3[(RE − R) − 0.2(RE − G)(RE/R)] | [51] | |
Modified chlorophyll absorption in reflectance index (MCARI) | MCARI = [(RE − R) − 0.2(RE − G)](RE/R) | [51] | |
Modified simple ratio index (MSR) | MSR = (NIR/R − 1)/(NIR/R + 1)1/2 | [52] | |
Green normalized difference vegetation index (GNDVI) | GNDVI = (NIR − G)/(NIR + G) | [53] | |
Simple ratio pigment index (SRPI) | SRPI = B/R | [50] | |
Normalized pigment chlorophyll index (NPCI) | NPCI = (R − B)/(R + B) | [54] | |
Plant senescence reflectance index (PSRI) | PSRI = (B − R)/G | [55] | |
Visible Atmospheric Resistant Index (VARI) | VARI = (G − R)/(G + R − B) | [56] | |
Color Index of Vegetation Extraction (CIVE) | CIVE = 0.44R − 0.81G + 0.39B + 18.79 | [57] | |
TIR | Temperature vegetation dryness index (TVDI) | TVDI = (Ts − Tsmin)/(Tsmax − Tsmin) | [58] |
Crop water stress index (CWSI) | CWSI = (Tc − Tw)/(Td − Tw) | [59] | |
Canopy temperature depression (CTD) | CTD = Tc − Ta | [60,61] | |
Vegetation transpiration coefficient (hac) | hac = (Tc − Ta)/(Tcp − Ta) | [62,63] |
Sensor | Spectral Index | d10 cm | d20 cm | d40 cm | d60 cm | d80 cm |
---|---|---|---|---|---|---|
MS | CIVE | −0.245 ** | −0.240 ** | −0.089 ** | −0.155 ** | −0.460 ** |
EVI | 0.257 ** | 0.225 ** | 0.129 ** | 0.074 * | 0.281 ** | |
GI | 0.310 ** | 0.136 ** | 0.059 * | 0.154 ** | 0.499 ** | |
GNDVI | 0.084 ** | −0.091 ** | 0.032 | −0.060 * | −0.014 | |
MCARI | 0.339 ** | 0.215 ** | 0.067 * | 0.185 ** | 0.642 ** | |
MSR | 0.253 ** | 0.142 ** | 0.075 ** | 0.043 | 0.225 ** | |
NDVI | 0.106 ** | −0.047 | 0.037 | 0 | 0.116 ** | |
NDVIgb | 0.373 ** | 0.199 ** | −0.005 | 0.192 ** | 0.631 ** | |
NPCI | −0.070 * | 0.024 | −0.081 ** | −0.009 | −0.081 ** | |
OSAVI | 0.219 ** | 0.154 ** | 0.100 ** | 0.052 | 0.224 ** | |
PSRI | 0.084 ** | −0.018 | 0.060 * | 0.024 | 0.128 ** | |
RVI | 0.312 ** | 0.112 ** | 0.078 ** | 0.090 ** | 0.367 ** | |
RVI2 | 0.137 ** | −0.052 | 0.047 | −0.049 | 0.02 | |
SAVI | 0.314 ** | 0.297 ** | 0.157 ** | 0.101 ** | 0.396 ** | |
SIPI | 0.212 ** | −0.016 | 0.019 | 0.023 | 0.248 ** | |
SRPI | −0.356 ** | −0.238 ** | −0.01 | −0.117 ** | −0.471 ** | |
TCARI | −0.112 ** | −0.105 ** | −0.01 | 0.053 | 0.01 | |
TVI | 0.289 ** | 0.266 ** | 0.141 ** | 0.093 ** | 0.352 ** | |
VARI | 0.242 ** | 0.073 * | 0.052 | 0.104 ** | 0.390 ** | |
TIR | TVDI | 0.044 | 0.217 ** | 0.106 ** | 0.063 * | −0.01 |
CTD | −0.211 ** | −0.139 ** | −0.054 | 0.029 | −0.001 | |
CWSI | −0.170 ** | 0.167 ** | 0.097 ** | −0.053 | −0.454 ** | |
ha | −0.132 ** | −0.145 ** | 0.016 | −0.05 | −0.301 ** |
Training Set | Testing Set | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
10 cm | 20 cm | 40 cm | 60 cm | 80 cm | 10 cm | 20 cm | 40 cm | 60 cm | 80 cm | |||
soybean | MS | R2 | 0.82 | 0.88 | 0.47 | 0.36 | 0.42 | 0.74 | 0.78 | 0.38 | 0.28 | 0.34 |
RMSE (%) | 0.011 | 0.133 | 0.094 | 0.408 | 0.22 | 2.669 | 0.225 | 1.049 | 0.015 | 1.1 | ||
TIR | R2 | 0.82 | 0.82 | 0.28 | 0.29 | 0.24 | 0.74 | 0.72 | 0.03 | 0.01 | 0.08 | |
RMSE (%) | 0.017 | 0.021 | 0.332 | 0.068 | 0.024 | 0.248 | 0.309 | 8.13 | 2.026 | 0.672 | ||
MS + TIR | R2 | 0.82 | 0.89 | 0.53 | 0.36 | 0.42 | 0.78 | 0.85 | 0.46 | 0.26 | 0.32 | |
RMSE (%) | 1.16 | 0.989 | 0.598 | 1.698 | 0.15 | 0.529 | 0.208 | 0.928 | 1.878 | 1.266 | ||
corn | MS | R2 | 0.53 | 0.81 | 0.82 | 0.32 | 0.32 | 0.33 | 0.67 | 0.68 | 0.15 | 0.14 |
RMSE (%) | 0.481 | 0.223 | 0.057 | 0.504 | 0.82 | 2.58 | 2.897 | 1.637 | 1.037 | 3.687 | ||
TIR | R2 | 0.43 | 0.79 | 0.75 | 0.33 | 0.31 | 0.26 | 0.61 | 0.6 | 0.19 | 0.14 | |
RMSE (%) | 0.152 | 0.351 | 0.164 | 0.273 | 0.173 | 2.476 | 3.387 | 1.045 | 3.793 | 1.823 | ||
MS + TIR | R2 | 0.55 | 0.85 | 0.89 | 0.4 | 0.42 | 0.4 | 0.7 | 0.73 | 0.24 | 0.28 | |
RMSE (%) | 0.433 | 0.032 | 0.009 | 0.738 | 0.821 | 1.287 | 0.571 | 1.317 | 2.377 | 7.589 |
Growth Period | R2 | MAE | |
---|---|---|---|
Soybean (10–20 cm) | Flowering stage | 0.92 ± 0.05 a | 0.006 ± 0.002 ab |
Podding stage | 0.84 ± 0.12 a | 0.005 ± 0.002 b | |
Filling stage | 0.86 ± 0.04 a | 0.008 ± 0.002 ab | |
Maturity stage | 0.63 ± 0.11 b | 0.01 ± 0.004 a | |
Corn (10–40 cm) | Jointing stage | 0.79 ± 0.07 a | 0.011 ± 0.005 c |
Tasseling stage | 0.56 ± 0.13 b | 0.012 ± 0.003 c | |
Filling stage | 0.72 ± 0.09 a | 0.014 ± 0.004 ab | |
Maturity stage | 0.24 ± 0.09 c | 0.022 ± 0.014 a |
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Zhang, Y.; Yang, X.; Tian, F. Study on Soil Moisture Status of Soybean and Corn across the Whole Growth Period Based on UAV Multimodal Remote Sensing. Remote Sens. 2024, 16, 3166. https://doi.org/10.3390/rs16173166
Zhang Y, Yang X, Tian F. Study on Soil Moisture Status of Soybean and Corn across the Whole Growth Period Based on UAV Multimodal Remote Sensing. Remote Sensing. 2024; 16(17):3166. https://doi.org/10.3390/rs16173166
Chicago/Turabian StyleZhang, Yaling, Xueyi Yang, and Fei Tian. 2024. "Study on Soil Moisture Status of Soybean and Corn across the Whole Growth Period Based on UAV Multimodal Remote Sensing" Remote Sensing 16, no. 17: 3166. https://doi.org/10.3390/rs16173166