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Shi H, Liu Z, Li S, Jin M, Tang Z, Sun T, Liu X, Li Z, Zhang F, Xiang Y. Monitoring Soybean Soil Moisture Content Based on UAV Multispectral and Thermal-Infrared Remote-Sensing Information Fusion. PLANTS (BASEL, SWITZERLAND) 2024; 13:2417. [PMID: 39273901 PMCID: PMC11396815 DOI: 10.3390/plants13172417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Revised: 08/27/2024] [Accepted: 08/28/2024] [Indexed: 09/15/2024]
Abstract
By integrating the thermal characteristics from thermal-infrared remote sensing with the physiological and structural information of vegetation revealed by multispectral remote sensing, a more comprehensive assessment of the crop soil-moisture-status response can be achieved. In this study, multispectral and thermal-infrared remote-sensing data, along with soil-moisture-content (SMC) samples (0~20 cm, 20~40 cm, and 40~60 cm soil layers), were collected during the flowering stage of soybean. Data sources included vegetation indices, texture features, texture indices, and thermal-infrared vegetation indices. Spectral parameters with a significant correlation level (p < 0.01) were selected and input into the model as single- and fuse-input variables. Three machine learning methods, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP), were utilized to construct prediction models for soybean SMC based on the fusion of UAV multispectral and thermal-infrared remote-sensing information. The results indicated that among the single-input variables, the vegetation indices (VIs) derived from multispectral sensors had the optimal accuracy for monitoring SMC in different soil layers under soybean cultivation. The prediction accuracy was the lowest when using single-texture information, while the combination of texture feature values into new texture indices significantly improved the performance of estimating SMC. The fusion of vegetation indices (VIs), texture indices (TIs), and thermal-infrared vegetation indices (TVIs) provided a better prediction of soybean SMC. The optimal prediction model for SMC in different soil layers under soybean cultivation was constructed based on the input combination of VIs + TIs + TVIs, and XGBoost was identified as the preferred method for soybean SMC monitoring and modeling, with its R2 = 0.780, RMSE = 0.437%, and MRE = 1.667% in predicting 0~20 cm SMC. In summary, the fusion of UAV multispectral and thermal-infrared remote-sensing information has good application value in predicting SMC in different soil layers under soybean cultivation. This study can provide technical support for precise management of soybean soil moisture status using the UAV platform.
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Affiliation(s)
- Hongzhao Shi
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Zhiying Liu
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Siqi Li
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Ming Jin
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Zijun Tang
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Tao Sun
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Xiaochi Liu
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Zhijun Li
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Fucang Zhang
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Youzhen Xiang
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
- Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
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Wang Y, Wei Y, Du Y, Li Z, Wang T. Estimation of spatial distribution of soil moisture on steep hillslopes by state-space approach (SSA). THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169973. [PMID: 38211854 DOI: 10.1016/j.scitotenv.2024.169973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
Soil moisture is a critical variable that quantifies precipitation, floods, droughts, irrigation, and other factors with regard to decision-making and risk evaluation. An accurate prediction of soil moisture dynamics is important for soil and environmental management. However, the complex topographic condition and land use in hilly and mountainous areas make it a challenge to monitor and predict soil moisture dynamics in these areas. In this study, the determinants of soil moisture variability were determined by structural equation modeling, and then an attempt was made to estimate the spatial distribution of soil moisture content on steep hillslope using the state-space method. Herein, soil moisture at different depths (0-10, 10-20, and 20-30 cm) was monitored by portable time-domain reflectometer (TDR) along this hillslope (100 m × 180 m). It showed that the spatial variability of soil moisture decreased with increasing soil wetness, primarily in the topsoil (0-10 cm). Soil moisture was correlated with elevation (r = 0.28, 0.50, and 0.28), capillary porosity (r = 0.06, 0.37, and 0.28), soil texture (r for Clay: 0.20, 0.24, and 0.16; r for Sand: -0.25, -0.18, and -0.28), organic carbon (r = -0.31, -0.08, and 0.10) and land use (r = -0.01, 0.28, and 0.24) under different conditions (dry, moderate, and wet). Among these determinants, elevation made direct contributions to soil moisture variation, especially under moderate conditions, while land use made its impacts by altering soil texture. It is encouraging that the state-space approach yielded precise and cost-effective predictions of soil moisture dynamics along this steep hillslope since it gives the minimum root-mean-square error (RMSE) and Akaike information criterion (AIC). Moreover, soil organic carbon (AIC = -4.497, RMSE = 0.104, R2 = 0.899), rock fragment contents (AIC = -4.366, RMSE = 0.111, R2 = 0.878), and elevation (AIC = -3.693, RMSE = 0.156, R2 = 0.629) effectively anticipated the spatial distribution of soil moisture under dry, moderate, and wet conditions, respectively. This study confirms the efficacy of the state-space approach as a valuable tool for soil moisture prediction in areas characterized by complex and spatially heterogeneous conditions.
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Affiliation(s)
- Yundong Wang
- College of Resources and Environment, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Yujie Wei
- College of Resources and Environment, Huazhong Agricultural University, Wuhan, Hubei 430070, China.
| | - Yingni Du
- College of Resources and Environment, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Zhaoxia Li
- College of Resources and Environment, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Tianwei Wang
- College of Resources and Environment, Huazhong Agricultural University, Wuhan, Hubei 430070, China
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Bai Y, Liu M, Guo Q, Wu G, Wang W, Li S. Diverse responses of gross primary production and leaf area index to drought on the Mongolian Plateau. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166507. [PMID: 37619736 DOI: 10.1016/j.scitotenv.2023.166507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 08/04/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023]
Abstract
Drought is a crucial factor regulating vegetation growth on the Mongolian Plateau (MP). Previous studies of drought effects on the MP have mainly concentrated on drought characterization, while the response of vegetation to drought remains unclear. To close this knowledge gap, we examined the response of MP vegetation to drought in terms of gross primary production (GPP) and leaf area index (LAI) from 1982 to 2018. Our findings show that intra-seasonally the frequency of drought occurrence in autumn had a greater impact on GPP (relative importance over 70 %), while the intensity of drought was more influential for LAI (relative importance approximately 60 %). Inter-seasonally, summer droughts had the most pronounced effect on vegetation (with median standardized anomalies of -0.72 for GPP and -0.4 for LAI, respectively). Additionally, we found that meteorological drought was more consistent with atmospheric aridity (high vapor pressure deficit) than soil drought (low soil moisture). This study advances knowledge of vegetation's susceptibility to climate extremes and improves the precision of predicting ecosystem response to climate change.
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Affiliation(s)
- Yu Bai
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Menghang Liu
- University of Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Qun Guo
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Genan Wu
- Institute of Spacecraft Application System Engineering, China Academy of Space Technology, Beijing 100094, China
| | - Weimin Wang
- Shenzhen Ecological Environmental Monitoring Center of Guangdong Province, Shenzhen 518049, China; Guangdong Greater Bay Area, Change and Comprehensive Treatment of Regional Ecology and Environment, National Observation and Research Station, Shenzhen 523722, China; State Environmental Protection Scientific Observation and Research Station for Ecology and Environment of Rapid Urbanization Region, Shenzhen 518000, China
| | - Shenggong Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China.
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