RESUMO
Accurate information on soil moisture (SM) is critical in various applications including agriculture, climate, hydrology, soil and drought. In this paper, various predictive relationships including regression (Multiple Linear Regression, MLR), machine learning (Random Forest, RF; Triangular regression, Tr) and spatial modeling (Inverse Distance Weighing, IDW and Ordinary kriging, OK) approaches were compared to estimate SM in a semi-arid mountainous watershed. In developing predictive relationship, Remote Sensing datasets including Landsat 8 satellite imagery derived surface biophysical characteristic, ASTER digital elevation model (DEM) derived surface topographical characteristic, climatic data recorded at the synoptic station and in situ SM data measured at Landsat 8 overpass time were utilized, while in spatial modeling, point-based SM measurements were interpolated. While 70%(calibration set) of the measured SM data were used for modeling, 30%(validation set) were used to evaluate modeling accuracy. Finally, the SM uncertainty maps were created for different models based on a bootstrapping approach. Among the environmental parameter sets, land surface temperature (LST) showed the highest impact on the spatial distribution of SM in the region at all dates. Mean R2(RMSE) between measured and modeled SM on three dates obtained from the MLR, RF, IDW, OK, and Tr models were 0.70(1.97%), 0.72(1.92%), 0.59(2.38%), 0.59(2.27%) and 0.71(1.99%), respectively. The results showed that RF and IDW produced the highest and lowest performance in SM modeling, respectively. Generally, the performance of RS-based models was higher than interpolation models for estimating SM due to the influence from combination of topographic parameters and surface biophysical characteristics. Modeled SM uncertainty with different models varies in the study area. The highest uncertainty in SM modeling was observed at the north part of the study area where the surface heterogeneity is high. Using RS data increased the accuracy of SM modeling because they can capture the surface biophysical characteristics and topographical properties heterogeneity.
RESUMO
Modeling and mapping of soil properties are critical in many environmental, climatic, ecological and hydrological applications. Digital soil mapping (DSM) techniques are now commonly applied to predict soil properties with limited data by developing predictive relationships with environmental covariates. Most studies derive covariates from a digital elevation model (named static covariates). Many works also include single-day remotely sensed satellite imagery. However, multitemporal satellite images can capture information about soil properties over time and bring additional information in predicting soil properties in DSM. We refer to covariates derived from multitemporal satellite images as dynamic covariates. The objective of this study was to assess the performance of DSM when using terrain derivatives (static covariates), single-date remotely sensed satellite indices (limited dynamic covariates), multitemporal satellite indices (dynamic covariates), and combinations of terrain derivatives and satellite indices (covariate fusion) as covariates in predicting soil properties and estimating uncertainty. Three soil properties are considered in this study: organic carbon (OC), sand content, and calcium carbonate equivalent (CCE). Inclusion of single and/or multitemporal remotely sensed satellite indices improved the prediction of soil properties over traditionally used terrain indices. Significant improvements were observed in the prediction of soil properties using two models, Cubist and random forest (RF). The increase in the R2 values for Cubist and RF were 126% and 78% for OC, 110% and 54% for sand, and 87% and 32% for CCE. The RMSE decreased by 34% and 27% for OC, 25% and 12% for sand, and 39% and 19% for CCE, when compared to the terrain indices only model. This also reduced the uncertainty of estimation and mapping. These clearly showed the advantage of using multitemporal satellite data fusion rather than simply using static terrain indices for DSM of soil properties to deliver a great potential in improving soil modeling and mapping for many applications.
RESUMO
Soil erosion by water is a three-phase process that consists of detachment of soil particles from the soil mass, transportation of detached particles either by raindrop impact or surface water flow, and sedimentation. Detachment by raindrops is a key component of the soil erosion process. However, little information is available on the role of raindrop impact on soil losses in the semi-arid regions where vegetation cover is often poor and does not protect the soil from rainfall. The objective of this study is to determine the contribution of raindrop impact to changes in soil physical properties and soil losses in a semiarid weakly-aggregated agricultural soil. Soil losses were measured under simulated rainfalls of 10, 20, 30, 40, 50, 60 and 70mmh-1, and under two conditions: i) with raindrop impact; and, ii) without raindrop impact. Three replications at each rainfall intensity and condition resulted in a total of 42 microplots of 1m×1.4m installed on a 10% slope according to a randomized complete block design. The contribution of raindrop impact to soil loss was computed using the difference between soil loss with raindrop impact and without raindrop impact at each rainfall intensity. Soil physical properties (aggregate size, bulk density and infiltration rate) were strongly damaged by raindrop impact as rainfall intensity increased. Soil loss was significantly affected by rainfall intensity under both soil surface conditions. The contribution of raindrop impact to soil loss decreased steadily with increasing rainfall intensity. At the lower rainfall intensities (20-30mmh-1), raindrop impact was the dominant factor controlling soil loss from the plots (68%) while at the higher rainfall intensities (40-70mmh-1) soil loss was mostly affected by increasing runoff discharge. At higher rainfall intensities the sheet flow protected the soil from raindrop impact.
