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

• Published in: Remote sensing (Basel, Switzerland) Vol. 7; no. 9; pp. 11801 - 11820
• Main Authors: Guo, Shanxin, Meng, Lingkui, Zhu, A-Xing, Burt, James E, Du, Fei, Liu, Jing, Zhang, Guiming
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2015
• Subjects: Agricultural land; Agricultural management; Bands; Cloud cover; Correlation; Data collection; dynamic feedback pattern of soil; Dynamics; Environmental studies; Evaporation; Evapotranspiration; Feedback; MODIS; Reflectance; Remote sensing; Soil (material); soil drying process; Soil dynamics; soil evaporation; soil mapping; soil surface reflectance; Soil surfaces
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs70911801

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.