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

Guo, Siming; Meng, Lingkui; Zhu, A‐Xing; Burt, Janis M.; Du, Fei; Liu, Jing; Zhang, Guiming

doi:10.3390/rs70911801

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…This model was derived from the equations used in soil evaporation studies and the relationship between soil surface reflectance and soil water content found in previous studies (Muller and Decamps, 2001;Ventura et al, 2006). The details of derivation have been shown in our previous study (Guo et al, 2015). After surface fitting, the soil feedback pattern at a given location can be described by serval equations, and the continuous soil feedback pattern for that location can be predicted by using these equations.…”

Section: Discussionmentioning

confidence: 99%

Unification of soil feedback patterns under different evaporation conditions to improve soil differentiation over flat area

Guo

Zhu

Meng

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tDetailed and accurate information on the spatial variation of soil types and soil properties are critical components of environmental research and hydrological modeling. Early studies introduced a soil feedback pattern as a promising environmental covariate to predict spatial variation over low-relief areas. However, in practice, local evaporation can have a significant influence on these patterns, making them incomparable at different locations. This study aims to solve this problem by examining the concept of transforming the dynamic patterns of soil feedback from the original time-related space to a new evaporation-related space. A study area in northeastern Illinois with large low-relief farmland was selected to examine the effectiveness of this idea. Images from MODIS in Terra for every April-May period over 12 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) were used to extract the soil feedback patterns. Compared to the original time-related space, the results indicate that the patterns in the new evaporation-related space tend to be more stable and more easily captured from multiple rain events regardless of local evaporation conditions. Random samples selected for soil subgroups from the SSURGO soil map show that patterns in the new space reveal a difference between different soil types. And these differences in patterns are closely related to the difference in the soil structure of the surface layer.

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Section: Discussionmentioning

confidence: 99%

Unification of soil feedback patterns under different evaporation conditions to improve soil differentiation over flat area

Guo

Zhu

Meng

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

Self Cite

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“…Considering that data gaps of remote sensing images can occur due to cloud cover during the days after rain events, Guo et al. () built a simple empirical relationship between cumulative meteorological evaporation observations and remote sensing band data and used that to interpolate the missing data. Zeng et al.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al (2012) and Zhao et al (2014) applied this idea in mapping soil texture and soil organic matter (SOM) content in two small areas in Jiangsu Province of China. Considering that data gaps of remote sensing images can occur due to cloud cover during the days after rain events, Guo et al (2015) built a simple empirical relationship between cumulative meteorological evaporation observations and remote sensing band data and used that to interpolate the missing data. Zeng et al (2017) analyzed the impact of different rainfall magnitudes on soil mapping performance of the covariates derived from land surface dynamic feedback after rain events.…”

Section: Introductionmentioning

confidence: 99%

An approach for broad‐scale predictive soil properties mapping in low‐relief areas based on responses to solar radiation

Liu

Rossiter

Song

et al. 2020

Soil Science Soc of Amer J

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In low‐relief areas, easily observed landscape features such as terrain and vegetation often do not spatially co‐vary with soil conditions to the level that they can be effectively used in predictive soil mapping. This paper proposes an approach to predicting soil spatial variation over such areas at a coarse resolution by constructing environmental covariates from the dynamic feedbacks of land surface in response to solar radiation. These feedbacks are captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature observations as a time series of four temperatures per day (1:30 AM, 10:30 AM, 1:30 PM, 10:30 PM) converted into five covariates representing the features of the time series. The approach is demonstrated by mapping sand, silt, soil organic matter (SOM), and pH over a 12,000‐km2 low‐relief area of Jiangsu Province, China, using a random forest model trained on 144 soil observations. The model was built and evaluated for 13 single days. For the non‐winter days with relatively high solar radiation, the covariates were strongly correlated with the soil properties, so that their use in predictive mapping gives good results for soil texture and useful results for SOM and pH. We also present plausible physical interpretations for the action of the covariates based on thermal properties of soils. We conclude that, despite the coarse resolution of the MODIS‐derived covariates, the proposed approach is a feasible solution for predictive soil mapping over large areas of low relief and should be investigated at finer resolutions.

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“…。由于环境因子在空间的分布大多具有连续性，土壤在空间分布规律呈现出空间连续渐变的特征，往往体现出空间上距离越近的点土壤属性越相似的特点，也即是所谓的 "地理学第一定律" (Tobler, 1970)。这是数字土壤制图的第二个理论基础。国内外学者的研究也证实了这一点 (Wilding et al, 1965;Burrough, 1989;杨琳, 2009 (杨奇勇等, 2011;邓红眉, 2013;Miller et al, 2015) (Zhu et al, 1994;Gray et al, 2016;Hengl et al, 2017) (刘峰等, 2009;Zhu et al, 2010;Wang et al, 2012;Zhao et al, 2014;Guo et al, 2015Guo et al, , 2016Zeng et al, 2017 (Qi et al, 2003;Yang et al, 2011;黄魏, 2016)。此外，土壤近地传感器获得的数据，如电导率数据、多光谱等数据也被用于土壤制图 (Rossel et al, 2008;Besson et al, 2010;Myers et al, 2010;史舟等, 2011;Shi et al, 2015) Brus, 1994;Yang et al, 2018 (Sacks et al, 1988 ;van Groenigen et al, 1998 )。该方法以模型估算方差最小化为目标，设计最优的样点数量和空间分布格局，获得具有全局代表性的样点 (Hughes et al, 1981;Russo, 1984;Warrick et al, 1987;Wang et al, 2009)。基于空间自相关模型的采样方法能得到样点数量和分布的最优解，其采样效果完全取决于空间自相关模型对于目标地理变量空间变化模拟的效果。然而，建立空间自相关模型通常需要有关目标地理变量空间变化特征的先验知识 (Webster et al, 1990)，同时也需要满足目标地理变量空间变化二阶平稳假设。因此，在多数实际情况下，特别是在大范围研究区，目标地理变量空间变化特征的先验知识需要大量的先验样本往往很难获得 (Webster et al, 1992;Simbahan et al, 2006)，二阶平稳假设也很难得到满足，这使得基于空间自相关模型的采样设计方法在实际应用中具有一定局限性 (Isaaks et al, 1989;Goovaerts, 1999)...…”

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