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Remote Sens. 2015, 7(9), 11801-11820; doi:10.3390/rs70911801

Data-Gap Filling to Understand the Dynamic Feedback Pattern of Soil

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
3
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, 210023, China
4
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
5
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
6
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 5 May 2015 / Revised: 9 August 2015 / Accepted: 1 September 2015 / Published: 15 September 2015
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Abstract

Detailed and accurate information on the spatial variation of soil over low-relief areas is a critical component of environmental studies and agricultural management. Early studies show that the pattern of soil dynamics provides comprehensive information about soil and can be used as a new environmental covariate to indicate spatial variation in soil in low relief areas. In practice, however, data gaps caused by cloud cover can lead to incomplete patterns over a large area. Missing data reduce the accuracy of soil information and make it hard to compare two patterns from different locations. In this study, we introduced a new method to fill data gaps based on historical data. A strong correlation between MODIS band 7 and cumulated reference evapotranspiration has been confirmed by theoretical derivation and by the real data. Based on this correlation, data gaps in MODIS band 7 can be predicted by daily evaporation data. Furthermore, correlations among bands are used to predict soil reflectance in MODIS bands 1–6 from MODIS band 7. A location in northeastern Illinois with a large area of low relief farmland was selected to examine this idea. The results show a good exponential relationship between MODIS band 7 and CET00.5 in most locations of the study area (with average R2 = 0.55, p < 0.001, and average NRMSE 10.40%). A five-fold cross validation shows that the approach proposed in this study captures the regular pattern of soil surface reflectance change in bands 6 and 7 during the soil drying process, with a Normalized Root Mean Square Error (NRMSE) of prediction of 13.04% and 10.40%, respectively. Average NRMSE of bands 1–5 is less than 20%. This suggests that the proposed approach is effective for filling the data gaps from cloud cover and that the method reduces the data collection requirement for understanding the dynamic feedback pattern of soil, making it easier to apply to larger areas for soil mapping. View Full-Text
Keywords: dynamic feedback pattern of soil; soil mapping; MODIS; soil surface reflectance; soil drying process; soil evaporation dynamic feedback pattern of soil; soil mapping; MODIS; soil surface reflectance; soil drying process; soil evaporation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Guo, S.; Meng, L.; Zhu, A.-X.; Burt, J.E.; Du, F.; Liu, J.; Zhang, G. Data-Gap Filling to Understand the Dynamic Feedback Pattern of Soil. Remote Sens. 2015, 7, 11801-11820.

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