Estimating Soil Moisture from C and X Band Sar Using Machine Learning Algorithms and Compact Polarimetry

2018 ◽  
Author(s):  
E. Santi ◽  
S. Pettinato ◽  
S. Paloscia ◽  
M. Dabboor ◽  
C. Notarnicola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Band

Spatiotemporal modeling of soil moisture in an Atlantic forest through machine learning algorithms

2021 ◽  
Author(s):  
Vinicius Augusto Oliveira ◽  
André Ferreira Rodrigues ◽  
Marco Antônio Vieira Morais ◽  
Marcela de Castro Nunes Santos Terra ◽  
Li Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Atlantic Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spatiotemporal Modeling

scholarly journals Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6926 ◽  
Author(s):  
Xiangyu Ge ◽  
Jingzhe Wang ◽  
Jianli Ding ◽  
Xiaoyi Cao ◽  
Zipeng Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Moisture Content ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spectral Indices ◽  
Processing Scheme

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

scholarly journals Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques

2021 ◽  
Vol 25 (5) ◽  
pp. 2739-2758
Author(s):  
Samuel N. Araya ◽  
Anna Fryjoff-Hung ◽  
Andreas Anderson ◽  
Joshua H. Viers ◽  
Teamrat A. Ghezzehei
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Potential Evapotranspiration ◽  
Learning Algorithms ◽  
Variable Importance ◽  
Machine Learning Algorithms ◽  
Surface Soil Moisture ◽  
Multispectral Remote Sensing

Abstract. This study investigates the ability of machine learning models to retrieve the surface soil moisture of a grassland area from multispectral remote sensing carried out using an unoccupied aircraft system (UAS). In addition to multispectral images, we use terrain attributes derived from a digital elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture. We tested four different machine learning algorithms and interrogated the models to rank the importance of different variables and to understand their relationship with surface soil moisture. All the machine learning algorithms we tested were able to predict soil moisture with good accuracy. The boosted regression tree algorithm was marginally the best, with a mean absolute error of 3.8 % volumetric moisture content. Variable importance analysis revealed that the four most important variables were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

scholarly journals Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France

2020 ◽  
Vol 7 (10) ◽  
Author(s):  
Yangxiaoyue Liu ◽  
Xiaolin Xia ◽  
Ling Yao ◽  
Wenlong Jing ◽  
Chenghu Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Learning Algorithms ◽  
Regression Tree ◽  
Machine Learning Algorithms

scholarly journals Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 713 ◽  
Author(s):  
Aliva Nanda ◽  
Sumit Sen ◽  
Awshesh Nath Sharma ◽  
K. P. Sudheer
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Learning Algorithms ◽  
Temperature Drop ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Soil temperature plays an important role in understanding hydrological, ecological, meteorological, and land surface processes. However, studies related to soil temperature variability are very scarce in various parts of the world, especially in the Indian Himalayan Region (IHR). Thus, this study aims to analyze the spatio-temporal variability of soil temperature in two nested hillslopes of the lesser Himalaya and to check the efficiency of different machine learning algorithms to estimate soil temperature in the data-scarce region. To accomplish this goal, grassed (GA) and agro-forested (AgF) hillslopes were instrumented with Odyssey water level and decagon soil moisture and temperature sensors. The average soil temperature of the south aspect hillslope (i.e., GA hillslope) was higher than the north aspect hillslope (i.e., AgF hillslope). After analyzing 40 rainfall events from both hillslopes, it was observed that a rainfall duration of greater than 7.5 h or an event with an average rainfall intensity greater than 7.5 mm/h results in more than 2 °C soil temperature drop. Further, a drop in soil temperature less than 1 °C was also observed during very high-intensity rainfall which has a very short event duration. During the rainy season, the soil temperature drop of the GA hillslope is higher than the AgF hillslope as the former one infiltrates more water. This observation indicates the significant correlation between soil moisture rise and soil temperature drop. The potential of four machine learning algorithms was also explored in predicting soil temperature under data-scarce conditions. Among the four machine learning algorithms, an extreme gradient boosting system (XGBoost) performed better for both the hillslopes followed by random forests (RF), multilayer perceptron (MLP), and support vector machine (SVMs). The addition of rainfall to meteorological and meteorological + soil moisture datasets did not improve the models considerably. However, the addition of soil moisture to meteorological parameters improved the model significantly.

scholarly journals Decomposition-Based Soil Moisture Estimation Using UAVSAR Fully Polarimetric Images

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 145
Author(s):  
Zeinab Akhavan ◽  
Mahdi Hasanlou ◽  
Mehdi Hosseini ◽  
Heather McNairn
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Soil Moisture ◽  
Random Forest ◽  
Performance Enhancement ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Eigenvalue And Eigenvector ◽  
Moisture Estimation

Polarimetric decomposition extracts scattering features that are indicative of the physical characteristics of the target. In this study, three polarimetric decomposition methods were tested for soil moisture estimation over agricultural fields using machine learning algorithms. Features extracted from model-based Freeman–Durden, Eigenvalue and Eigenvector based H/A/α, and Van Zyl decompositions were used as inputs in random forest and neural network regression algorithms. These algorithms were applied to retrieve soil moisture over soybean, wheat, and corn fields. A time series of polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the Soil Moisture Active Passive Experiment 2012 (SMAPVEX12) field campaign was used for the training and validation of the algorithms. Three feature selection methods were tested to determine the best input features for the machine learning algorithms. The most accurate soil moisture estimates were derived from the random forest regression algorithm for soybeans, with a correlation of determination (R2) of 0.86, root mean square error (RMSE) of 0.041 m3 m−3 and mean absolute error (MAE) of 0.030 m3 m−3. Feature selection also impacted results. Some features like anisotropy, Horizontal transmit and Horizontal receive (HH), and surface roughness parameters (correlation length and RMS-H) had a direct effect on all algorithm performance enhancement as these parameters have a direct impact on the backscattered signal.

Machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation

2021 ◽  
Author(s):  
Sumit Kumar Chaudhary ◽  
Prashant K. Srivastava ◽  
Dileep Kumar Gupta ◽  
Pradeep Kumar ◽  
Rajendra Prasad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Learning Algorithms ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Moisture Estimation

scholarly journals A New Approach for Soil Moisture Downscaling in the Presence of Seasonal Difference

2020 ◽  
Vol 12 (17) ◽  
pp. 2818
Author(s):  
Ran Yan ◽  
Jianjun Bai
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Learning Algorithms ◽  
Seasonal Difference ◽  
Machine Learning Algorithms ◽  
Slope Aspect ◽  
Seasonal Differences ◽  
Explanatory Variables ◽  
Fine Spatial Resolution

The variation of soil moisture (SM) is a complex and synthetic process, which is impacted by numerous factors. The effects of these factors on soil moisture are dynamic. As a result, the relationship between soil moisture and explanatory variables varies with time and season. This kind of change should be considered in obtaining fine spatial resolution soil moisture products. We chose a study area with four distinct seasons in the temperate monsoon region. In this research, we established seasonal downscaling models to avoid the influence of seasonal differences. Precipitation, land surface temperature, evapotranspiration, vegetation index, land cover, elevation, slope, aspect and soil texture were taken as explanatory variables to produce fine spatial resolution SM. SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were downscaled with the help of machine learning algorithms. We compared three machine learning algorithms of random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) to determine the most suitable algorithm for this study. The results show that season-based downscaling is even better than continuous time series. In the analysis of seasonal differences, precipitation plays a dominant role, but its contribution rate is different in each season. Moreover, the influence of vegetation is more prominent in winter, while the influence of terrain is more important in the other three seasons. It could be noted that the accuracy of the RF model is the best among three machine learning algorithms, and the RF-downscaled products have superior matching performance to both AMSR (AMSR-E and AMSR2) SM products and in-situ measurements. The analysis indicates considering seasonal difference and the application of machine learning has high potential for spatial downscaling in remote sensing applications.

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