Humanity deals with several challenges in this century such as climate change, land use, and land use/cover change (LUCC). Determining the patterns, developments, and consequences of LUCC issues for the livelihoods of people, especially poor people, is very important. Therefore, this paper aims to investigate the interactions between LUCC and climate change over the period of 1966–2015 (50 years) as a complex system at the global level. CO2 emissions and surface temperature are considered as the main indicators of climate change (CC). The data were analyzed in time-oriented (time-based) and local or place-oriented (country-based) manners. The results showed that arable and rangeland use changes (LUC) affect CO2 emissions in both direct and indirect ways. However, the direct effect of rangeland use change is positive, and its indirect effect is negative. In addition, deforestation has increased CO2 emissions indirectly. LUCC can also change the ability of the ecosystem to deliver services to people, including biodiversity and other resources such as food, fiber, water, etc. Therefore, it is critical to determine the patterns, trends, and impacts of LUCC on CC. Thus, CC mitigation policies should be followed by considering both direct and indirect effects. Without a doubt, this will be realized when the decision and policymakers have a better understanding of the structure and interaction between CC, LUCC, and their components as a whole system.

Humanity deals with several challenges in this century such as climate change, land use, and land use/cover change (LUCC). Determining the patterns, developments, and consequences of LUCC issues for the livelihoods of people, especially poor people, is very important. Therefore, this paper aims to investigate the interactions between LUCC and climate change over the period of 1966–2015 (50 years) as a complex system at the global level. CO2 emissions and surface temperature are considered as the main indicators of climate change (CC). The data were analyzed in time-oriented (time-based) and local or place-oriented (country-based) manners. The results showed that arable and rangeland use changes (LUC) affect CO2 emissions in both direct and indirect ways. However, the direct effect of rangeland use change is positive, and its indirect effect is negative. In addition, deforestation has increased CO2 emissions indirectly. LUCC can also change the ability of the ecosystem to deliver services to people, including biodiversity and other resources such as food, fiber, water, etc. Therefore, it is critical to determine the patterns, trends, and impacts of LUCC on CC. Thus, CC mitigation policies should be followed by considering both direct and indirect effects. Without a doubt, this will be realized when the decision and policymakers have a better understanding of the structure and interaction between CC, LUCC, and their components as a whole system.

Satellites are designed to monitor geospatial data over large areas at a catchment scale. However, most of satellite validation works are conducted at local point scales with a lack of spatial representativeness. Although upscaling them with a spatial average of several point data collected in the field, it is almost impossible to reorganize backscattering responses at pixel scales. Considering the influence of soil storage on watershed streamflow, we thus suggested watershed-scale hydrological validation. In addition, to overcome the limitations of backscattering models that are widely used for C-band Synthetic Aperture Radar (SAR) soil moisture but applied to bare soils only, in this study, RADARSAT-2 soil moisture was stochastically retrieved to correct vegetation effects arising from agricultural lands. Roughness-corrected soil moisture retrievals were assessed at various spatial scales over the Brightwater Creek basin (land cover: crop lands, gross drainage area: 1540 km2) in Saskatchewan, Canada. At the point scale, local station data showed that the Root Mean Square Errors (RMSEs), Unbiased RMSEs (ubRMSEs) and biases of Radarsat-2 were 0.06~0.09 m3/m3, 0.04~0.08 m3/m3 and 0.01~0.05 m3/m3, respectively, while 1 km Soil Moisture Active Passive (SMAP) showed underestimation at RMSEs of 0.1~0.22 m3/m3 and biases of −0.036~−0.2080 m3/m3. Although SMAP soil moisture better distinguished the contributing area at the catchment scale, Radarsat-2 soil moisture showed a better discharge hysteresis. A reliable estimation of the soil storage dynamics is more important for discharge forecasting than a static classification of contributing and noncontributing areas.

Abstract SAR data provide the high-resolution images useful for monitoring environment, and natural resources. Nevertheless, it has been a great challenge to retrieve soil moisture over vegetated sites from SAR backscatter coefficients, as it is almost impossible to parameterize spatially heterogeneous and time-varying roughness, the effect of rainfall or canopy volume scattering with implicit equations. We suggest a Monte Carlo Method (MCM) as a strategy to mitigate non-linear errors in retrievals arising from rainfall, and vegetation growth. The Advanced Integral Equation Model (AIEM) is repeatedly run in a forward mode for establishing the Gaussian-distributed soil roughness and backscatter coefficients. The mean value of soil moisture ensembles inverted from those was taken as an optimal estimate. Local validations show that Root Mean Square Errors (RMSEs) were 0.05 ∼ 0.07 m3/m3 at the stations in Saskatchewan, Canada. Biases were 0.01 m3/m3. Spatial distribution illustrates that the retrieval biases were mitigated, resolving AIEM inversion errors.

Abstract SAR data provide the high-resolution images useful for monitoring environment, and natural resources. Nevertheless, it has been a great challenge to retrieve soil moisture over vegetated sites from SAR backscatter coefficients, as it is almost impossible to parameterize spatially heterogeneous and time-varying roughness, the effect of rainfall or canopy volume scattering with implicit equations. We suggest a Monte Carlo Method (MCM) as a strategy to mitigate non-linear errors in retrievals arising from rainfall, and vegetation growth. The Advanced Integral Equation Model (AIEM) is repeatedly run in a forward mode for establishing the Gaussian-distributed soil roughness and backscatter coefficients. The mean value of soil moisture ensembles inverted from those was taken as an optimal estimate. Local validations show that Root Mean Square Errors (RMSEs) were 0.05 ∼ 0.07 m3/m3 at the stations in Saskatchewan, Canada. Biases were 0.01 m3/m3. Spatial distribution illustrates that the retrieval biases were mitigated, resolving AIEM inversion errors.