Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China

Cheng, Minghan; Li, Binbin; Jiao, Xiyun; Huang, Xiao; Huang, Fan; Lin, Rencai; Liu, Kaihua

doi:10.1016/j.agwat.2021.107298

Cited by 31 publications

(12 citation statements)

References 61 publications

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“…Bertalan et al [96] compared the abilities of RF, Elastic Net Regression (ENR), the Generalized Linear Model (GLM), and the Robust Linear Model (RLM) for estimating RZSM, and found that RF had the highest accuracy, followed by ENR, while the GLM had the lowest accuracy. Based on optical and thermal infrared remote sensing data provided by Landsat-8, Cheng et al [97] employed RF to estimate the multi-layer soil moisture in Beijing, China, and the results showed that RF achieved higher accuracy, with R 2 ranging from 0.67 to 0.81, and relative root-mean-square error (rRMSE) ranging from 8.74% to 14.68%. Zhu et al [98] found that the ANN outperformed RF and linear models in RZSM estimation, and they constructed a simple model with only five input variables, which achieved high accuracy (RMSE was 0.039 cm 3 /cm 3 , and R 2 was 0.697).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

Li,

Sun,

Zhao

2023

Remote Sensing

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Root zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution processes and plays a key role in energy and water exchange between land surface and atmosphere; hence, accurate estimation of RZSM is crucial for agricultural irrigation management practices. Traditional methods to measure soil moisture at stations are laborious and spatially uneven, making it difficult to obtain soil moisture data on a large scale. Remote sensing techniques can provide soil moisture in a large-scale range, but they can only provide surface soil moisture (SSM) with a depth of approximately 5–10 cm. In order to obtain a large range of soil moisture for deeper soil layers, especially the crop root zone with a depth of about 100–200 cm, numerous methods based on remote sensing inversion have been proposed. This paper analyzes and summarizes the research progress of remote sensing-based RZSM estimation methods in the past few decades and classifies these methods into four categories: empirical methods, semi-empirical methods, physics-based methods, and machine learning methods. Then, the advantages and disadvantages of various methods are outlined. Additionally an outlook on the future development of RZSM estimation methods is made and discussed.

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Section: Machine Learning Methodsmentioning

confidence: 99%

A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

Li,

Sun,

Zhao

2023

Remote Sensing

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“…where Y E is the estimated rice yield and Y R is the recorded rice yield; n is 800, i.e., the count of samples. The three metrics R 2 , RMSE, and rRMSE have been widely employed to assess model performance [11][12][13]24]. R 2 varies from −1 to 1; a value closer to 1 indicates that the estimated rice yield is more consistent with the recorded rice yield.…”

Section: Validation Metricsmentioning

confidence: 99%

Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China

Liu,

Ju,

et al. 2024

Agriculture

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Effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. However, traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional rice yields. In this study, we employed four machine-learning techniques: partial least squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and back propagation neural network (BPNN). We combined these methods with multi-temporal rice NDVI (normalized difference vegetation index) data for rice yield estimation. Following an accuracy evaluation and a spatial analysis, the key findings of our study are as follows. (1) The RFR model emerged as the most accurate for rice yield estimation, achieving an R2 of 0.65, an RMSE of 388.79 kg/ha, and an rRMSE of 4.48%. While PLSR and SVR demonstrated comparable accuracy, they were both inferior to RFR. (2) Using the top seven predictors with the highest importance rankings as inputs for the RFR model (NDVI values on the 6th, 17th, 33rd, 44th, 71st, 90th, and 106th days after the rice transplanting stage) achieved comparable accuracy while reducing information redundancy. (3) The proposed model demonstrated good spatial applicability (MI = −0.03) for rice yield estimation in Jiangsu, China. (4) A high spatial resolution yearly rice yield dataset (1 km) spanning from 2001 to 2020 was generated using the proposed model and is accessible on the Zenodo database. In conclusion, this study has demonstrated the efficacy of combining multi-temporal remote sensing data with machine-learning techniques for accurate rice yield estimation, thereby aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season.

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“…Since establishing dense sensor networks could prove costly, labor‐intensive, and destructive to soil profiles (Lekshmi et al., 2014), SM estimation using remote sensing (RS) provides a practical solution to these challenges. RS has emerged as a powerful tool to provide spatially and temporally representative SM data, thus increasing the measurement possibilities, especially for remote catchments with sparse monitoring networks (Cheng et al., 2022; Hillel, 2003; Kundu et al., 2017; Peng et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Kibirige,

Gokool,

Mkhize

2023

Vadose Zone Journal

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Soil moisture (SM) is a fundamental constituent of the terrestrial environment and the hydrological cycle. Owing to its significant influence on catchment hydrological responses, it can be utilized as an indicator of floods and droughts to aid early warning systems. This study aimed to develop a field‐scale method to estimate SM using parametric and machine learning‐based methods to compare whether advanced artificial intelligence methods can give similar results as traditional methods. Considering this, monthly observed SM data (from the top 10 cm), environmental covariates, and remotely sensed data from March 2019 to July 2021 for the Cathedral Peak Research Catchments VI and IX in South Africa were obtained. From the 241 observations obtained across 12 sites, 160 (∼66%) were used for model training, while the remaining 81 (∼34%) were used for model testing. Employing 10‐fold cross‐validation, the individual machine learning models (viz., support vector machine [SVM], random forest (RF), k‐nearest neighbor, classification and regression trees [Rpart], and generalized linear model) displayed a satisfactory performance (R2 = 0.52–0.79; root mean square error = 3.79–5.80). In the validation phase, the RF model displayed a superior performance, followed by the SVM. Subsequent SM estimation using the hybrid model produced satisfactory results in training (R2 = 0.90) and testing (R2 = 0.45). The results obtained from this study can aid in predicting SM variations in catchments with limited monitoring. Furthermore, this model can be applied in drought monitoring, forecasting, and informing agricultural management practices.

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Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China

Cited by 31 publications

References 61 publications

A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

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