Remote sensing of soil surface properties

2009 ◽  
Vol 33 (4) ◽  
pp. 457-473 ◽  
Author(s):  
K. Anderson ◽  
H. Croft
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Research Question ◽  
Soil Surface ◽  
Microwave Remote Sensing ◽  
Optical Remote Sensing ◽  
Future Challenges ◽  
Soil Surface Roughness ◽  
Remote Sensing Techniques ◽  
New Research

Remote sensing is now in a strong position to provide meaningful spatial data for use in soil science investigations. In the last 10 years, advancements in remote sensing techniques and technologies have given rise to a wealth of exciting new research findings in soil-related disciplines. This paper provides a critical insight into the role played by remote sensing in this field, with a specific focus on soil surface monitoring. Two key soil properties are considered in this review, soil surface roughness and moisture, because these two variables have benefited most from recent cutting-edge advances in remote sensing. Of note is the fact that the major recent advancements in spatial assessment of soil structure have emerged from optical remote sensing, while the soil moisture community has benefited from advancements in microwave systems, justifying the focus of this paper in these specific directions. The paper considers the newest techniques within active, passive, optical and microwave remote sensing and concludes by considering future challenges, multisensor approaches and the issue of scale — which is a key cross-disciplinary research question of relevance to soil scientists and remote sensing scientists alike.

Experimental Study of C Band Passive Microwave Remote Sensing of Soil Moisture

2013 ◽  
Vol 477-478 ◽  
pp. 624-627
Author(s):  
Xiao Liu Gao ◽  
Hui Hui Zhang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Emission Rate ◽  
Soil Surface ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Passive Microwave Remote Sensing ◽  
Soil Surface Roughness ◽  
Relationship Of

Passive microwave remote sensing is one of the most effective methods for inversing soil moisture. Under the condition of laboratory, firstly, C band microwave radiation was used to achieve the trial of ground-based remote sensing soil moisture, and then regression analysis was carried out according to the data measured, finally, got the C band experience regression model of soil moisture inversion. The results showed that: in the level-off state of soil surface, soil humidity and soil microwave emission rate is linear negative correlation, in the other words, soil microwave emission rate decreased while the soil moisture increased. Besides, with the increasing of soil surface roughness, both the value of microwave polarization index (MPDI) and microwave emission rate polarization difference Δe have the same trend of quick drop, stabilization and slow raise, and it presented the relationship of quadratic curve with the change of roughness.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

Optimal Detection and Estimation of Marine Oil Spills Through Coherent Pluralism

2013 ◽  
Vol 4 (1) ◽  
pp. 84-111
Author(s):  
Kufre Bassey ◽  
Polycarp Chigbu
Keyword(s):  
Remote Sensing ◽  
Environmental Science ◽  
Oil Spills ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Marine Oil ◽  
Detection And Estimation ◽  
Number Of False Alarms ◽  
Marine Oil Spills ◽  
Remote Sensing Techniques

An important area of environmental science involves the combination of information from diverse sources relating to a similar endpoint. Majority of optical remote sensing techniques used for marine oil spills detection have been reported lately of having high number of false alarms (oil slick look-a-likes) phenomena which give rise to signals which appear to be oil but are not. Suggestions for radar image as an operational tool has also been made. However, due to the inherent risk in these tools, this paper presents the possible research directions of combining statistical techniques with remote sensing in marine oil spill detection and estimation.

scholarly journals Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images

2019 ◽  
Vol 11 (10) ◽  
pp. 1163
Author(s):  
Wenting Cai ◽  
Shuhe Zhao ◽  
Yamei Wang ◽  
Fanchen Peng ◽  
Joon Heo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Crop Residues ◽  
Regional Scale ◽  
Microwave Remote Sensing ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Optical Remote Sensing ◽  
Optimal Subset ◽  
Subset Regression

As an important part of the farmland ecosystem, crop residues provide a barrier against water erosion, and improve soil quality. Timely and accurate estimation of crop residue coverage (CRC) on a regional scale is essential for understanding the condition of ecosystems and the interactions with the surrounding environment. Satellite remote sensing is an effective way of regional CRC estimation. Both optical remote sensing and microwave remote sensing are common means of CRC estimation. However, CRC estimation based on optical imagery has the shortcomings of signal saturation in high coverage areas and susceptibility to weather conditions, while CRC estimation using microwave imagery is easily influenced by soil moisture and crop types. Synergistic use of optical and microwave remote sensing information may have the potential to improve estimation accuracy. Therefore, the objectives of this study were to: (i) Analyze the correlation between field measured CRC and satellite derived variables based on Sentinel-1 and Sentinel-2, (ii) investigate the relationship of CRC with new indices (OCRI-RPs) which combine optical crop residues indices (OCRIs) and radar parameters (RPs), and (iii) to estimate CRC in Yucheng County based on OCRI-RPs by optimal subset regression. The correlations between field measured CRC and satellite derived variables were evaluated by coefficient of determination (R2) and root mean square error (RMSE). The results showed that the normalized difference tillage index (NDTI) and radar indices 2 (RI2) had relatively higher correlations with field measured CRC in OCRIs and RPs (R2 = 0.570, RMSE = 6.560% and R2 = 0.430, RMSE = 7.052%, respectively). Combining OCRIs with RPs by multiplying each OCRI with each RP could significantly improve the ability of indices to estimate CRC, as NDTI × RI2 had the highest R2 value of 0.738 and lowest RMSE value of 5.140%. The optimal model for CRC estimation by optimal subset regression was constructed by NDI71 × σ V V 0 and NDTI × σ V H 0 , with a R2 value of 0.770 and a RMSE value of 4.846%, which had a great improvement when compared with the best results in OCRIs and RPs. The results demonstrated that the combination of optical remote sensing information and microwave remote sensing information could improve the accuracy of CRC estimation.

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