# Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Data Sets

**Figure 1.**Field soil moisture measuring area: Central Facility (CF), El Reno (ER), and Little Washita (LW) placed on ESTAR derived soil moisture and RADARSAT backscatter image. Two study areas (A and B) are extracted from overlapping area.

^{nd}and July 12

^{th}, 1997. Two regions (A and B) were selected within the study area (Figure 1) for both the images. Region A covers 26.4 km x 96 km (2534.4 km

^{2}) and region B covers 31.2 km x 103.2 km (3220.0 km

^{2}).

**Figure 2.**SAR backscatter v/s field measured soil moisture for different vegetation covers for (a) 2

^{nd}July 1997 and (b) 12

^{th}July 1997 data, show better correlation in harvested field than vegetated field.

## 3. Methodology

#### 3.1 Input Variable Selection approach

Sr. No. | Data | Data Source | Spatial Resolution |
---|---|---|---|

1 | Active microwave SAR data | RADARSAT-1 images | 25 m * 25 m (aggregated to 800 m * 800 m) |

2 | Soil moisture | ESTAR based brightness temperature | 800 m * 800 m |

3 | Soil moisture | Field Measurement | Point measurements |

4 | Normalized Difference Vegetation Index (NDVI) | Landsat images (Visible and Near Infrared band) | 30 m * 30 m (aggregated to 800 m * 800 m) |

5 | Vegetation Water Content (VWC) | Algorithm given by Jackson et al (1999) using NDVI | 800 m * 800 m |

6 | Vegetation Optical Depth (VOD) | Algorithm given in Jackson et al (1999) using NDVI | 800 m * 800 m |

7 | SAR textural images (Homogeneity, Contrast, Dissimilarity, Mean, Variance, Entropy, Angular Second Moment, and Correlation) | RADARSAT-1 images | 25 m * 25 m (aggregated to 800 m * 800 m) |

8 | Soil texture (percent of sand) | STATSGO of USDA | 1 km * 1 km (re-sampled to 800 m * 800 m) |

**Table 2.**Correlation matrix of SAR textural images generated using Grey Level Co-occurrence Matrix. Three least correlated (independent) textural images: mean, variance, and homogeneity, were retained as inputs to the models.

Textural images | Homogeneity | Contrast | Dissimilarity | Mean | Variance | Entropy | AS-Moment | Correlation |
---|---|---|---|---|---|---|---|---|

Homogeneity | 1 | 0.647 | 0.848 | 0.303 | 0.053 | 0.949 | 0.870 | 0.544 |

Contrast | 0.647 | 1 | 0.948 | 0.308 | 0.413 | 0.700 | 0.481 | 0.565 |

Dissimilarity | 0.848 | 0.949 | 1 | 0.089 | 0.302 | 0.868 | 0.667 | 0.612 |

Mean | 0.303 | 0.308 | 0.089 | 1 | 0.194 | 0.252 | 0.324 | 0.152 |

Variance | 0.053 | 0.413 | 0.302 | 0.194 | 1 | 0.298 | 0.096 | 0.509 |

Entropy | 0.949 | 0.700 | 0.868 | 0.252 | 0.298 | 1 | 0.859 | 0.372 |

AS Moment | 0.870 | 0.481 | 0.667 | 0.324 | 0.096 | 0.859 | 1 | 0.337 |

Correlation | 0.544 | 0.565 | 0.612 | 0.152 | 0.509 | 0.372 | 0.337 | 1 |

^{nd}and 12

^{th}July (76 data points) were used to train the models. However, we observed high RMSE error (9.50) between retrieved soil moisture at 10 jackknifed soil moisture data points. This high RMSE can be attributed to less data points, which were not enough to train the neural network and fuzzy logic models. Also, as shown in Figure 2, there is low correlation between in-situ soil moisture and backscatter under vegetated area. Therefore, soil moisture data [1] derived using ESTAR were used instead of the in-situ soil moisture measurements. The models were then tested for areas A and B of backscatter images taken on July 02 and July 12 using independent pixels which were not used in training the process.

#### 3.2 Multiple Regression Analysis

^{2}values) for the observed versus predicted values and an overall test of significance. The test of significance is done by: (1) a standardized regression coefficient (b if all variables are standardized), (2) a t value, and (3) a p value associated with the t value. The multiple correlations R

^{2}, associated with the regression model, is the percent of the variance in the dependent variable explained collectively by all of the independent variables [44].

_{v}) is:

_{v}(%) = 0.313 (σ

_{0}) + (4.471 * NDVI) – 8.50 * PS

#### 3.3 Neural Network

#### 3.4 Fuzzy Logic

**Figure 4.**Effect of number hidden nodes in single hidden layer and training pixel on soil moisture retrieval accuracy.

**Figure 5.**Effect of cluster radii on soil moisture retrieval accuracy for random datasets (SAR backscatter, NDVI and soil characteristics). The corresponding change in the number of clusters with radii is also shown.

## 4. Results and Discussion

#### 4.1 Comparison of Results

^{th}July. Soil moisture data predicted by the neural network, fuzzy logic and multiple regression models were compared with two independent data sets: ESTAR derived soil moisture and field soil moisture measurements (Figure 6). The RMSE of values predicted and in-situ soil moisture measured at 38 field sites locations, in terms of soil moisture percentage, using neural network and fuzzy logic are 6.44 and 6.97 respectively. Lower RMSE was observed for Washita area (LW) compared to El Reno and Central Facility of study area. The comparison (Figure 6) shows that, soil moisture predicted using neural network and fuzzy logic overestimate the soil moisture at lower values and underestimate at higher soil moisture values.

^{th}July is found to be smaller than that for Area B and 2

^{nd}July data. The correlation coefficient between neural network and fuzzy logic predicted soil moisture and the ESTAR derived soil moisture varies between 0.62% and 0.77%.

#### 4.2 Effect of Vegetation and Soil Characteristics

**Figure 6.**Comparison of predicted soil moisture from fuzzy logic model with field and ESTAR soil moisture with field soil moisture measuring area: Central Facility (CF), El Reno (ER), and Little Washita (LW) for July 02

^{nd}(a), July 12

^{th}(b) data.

**Figure 7.**Plot showing difference of neural network based with field measured soil moisture is increase with Normalized Difference Vegetation Index.

**Table 3.**Root means square error (RMSE) and correlation coefficient (R) of estimated soil moisture using neural network and fuzzy logic with ESTER soil moisture.

Date | Area | Neural Network Model | Fuzzy Logic Model | Multiple Regression Model | |||
---|---|---|---|---|---|---|---|

RMSE | R | RMSE | R | RMSE | R | ||

2^{nd} July | A | 7.967 | 0.414 | 5.763 | 0.493 | 6.696 | 0.523 |

B | 8.294 | 0.397 | 6.195 | 0.448 | 5.834 | 0.405 | |

12^{nd} July | A | 3.621 | 0.715 | 3.722 | 0.702 | 4.570 | 0.665 |

B | 4.493 | 0.458 | 3.853 | 0.504 | 4.822 | 0.483 |

**Table 4.**Effect of data input configuration used in neural network, fuzzy logic and multiple regression model in terms of Root means square error (RMSE) and correlation coefficient (R) values of predicted soil moisture and ESTAR soil moisture for independent 300 pixels from Area A on 12

^{th}July data.

Data Input | Neural Network Model | Fuzzy Logic Model | Multiple Regression Model | |||
---|---|---|---|---|---|---|

RMSE | R | RMSE | R | RMSE | R | |

SAR | 4.847 | 0.620 | 4.506 | 0.645 | 7.436 | 0.591 |

SAR+NDVI | 3.940 | 0.716 | 4.075 | 0.693 | 5.421 | 0.653 |

SAR+PS | 4.344 | 0.660 | 3.955 | 0.684 | 5.631 | 0.634 |

SAR+NDVI+PS | 3.396 | 0.767 | 3.454 | 0.759 | 4.482 | 0.719 |

## 5. Conclusions

## Acknowledgments

## References and Notes

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## Share and Cite

**MDPI and ACS Style**

Lakhankar, T.; Ghedira, H.; Temimi, M.; Sengupta, M.; Khanbilvardi, R.; Blake, R.
Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data. *Remote Sens.* **2009**, *1*, 3-21.
https://doi.org/10.3390/rs1010003

**AMA Style**

Lakhankar T, Ghedira H, Temimi M, Sengupta M, Khanbilvardi R, Blake R.
Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data. *Remote Sensing*. 2009; 1(1):3-21.
https://doi.org/10.3390/rs1010003

**Chicago/Turabian Style**

Lakhankar, Tarendra, Hosni Ghedira, Marouane Temimi, Manajit Sengupta, Reza Khanbilvardi, and Reginald Blake.
2009. "Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data" *Remote Sensing* 1, no. 1: 3-21.
https://doi.org/10.3390/rs1010003