Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.

Vertical soil moisture profiles based on the principle of maximum entropy (POME) were validated using field and model data and applied to guide an irrigation cycle over a maize field in north central Alabama (USA). The results demonstrate that a simple two-constraint entropy model under the assumption of a uniform initial soil moisture distribution can simulate most soil moisture profiles that occur in the particular soil and climate regime that prevails in the study area. The results of the irrigation simulation demonstrated that the POME model produced a very efficient irrigation strategy with minimal losses (about 1.9% of total applied water). However, the results for finely-textured (silty clay) soils were problematic in that some plant stress did develop due to insufficient applied water. Soil moisture states in these soils fell to around 31% of available moisture content, but only on the last day of the drying side of the irrigation cycle. Overall, the POME approach showed promise as a general strategy to guide irrigation in humid environments, such as the Southeastern United States.

Regression models to predict stand size classes (sawtimber and saplings) and categories of species (hardwood and softwood) from fractal dimensions (FD) and Moran’s I derived from Landsat Thematic Mapper (TM) data were developed. Three study areas (Oakmulgee National Forest, Bankhead National Forest, and Talladega National Forest) were randomly selected and used to develop the prediction models, while one study area, Chattahoochee National Forest, was saved for validation. This study has shown that these spatial analytical indices (FD and Moran’s I) can distinguish between different forest trunk size classes and different categories of species (hardwood and softwood) using Landsat TM data. The results of this study also revealed that there is a linear relationship between each one of the spatial indices and the percentages of sawtimber–saplings size classes and hardwood–softwood categories of species. Given the high number of factors causing errors in the remotely sensed data as well as the Forest Inventory Analysis (FIA) data sets and compared to other studies in the research literature, the sawtimber–saplings models and hardwood–softwood models were reasonable in terms of significance and the levels of explained variance for both spatial indices FD and Moran’s I. The mean absolute percentage errors associated with the stand size classes prediction models and categories of species prediction models that take topographical elevation into consideration ranged from 4.4% to 19.8% and from 12.1% to 18.9%, respectively, while the root mean square errors ranged from 10% to 14% and from 11% to 13%, respectively.

Managing water resources requires consideration of both environmental and socio-economic benefits to effectively balance the benefits and costs. This includes identifying ecosystem services (ES) of concern and how to evaluate the project or proposed changes effect on these ES. The purpose of this effort is to describe methods to evaluate ecosystem services to provide expanded irrigation to existing agricultural lands in Alabama and the potential application to other areas. A case study has been undertaken on the Middle Alabama watershed in central Alabama and methods have been developed and applied to evaluate ES in terms of how irrigated versus rainfed fields will affect sediment retention, fertilizer usage and the effect of the subsequent discharges of sediment and nitrogen from fertilizer on water quality. The results of case studies in the Middle Alabama watershed indicate positive ES benefits from sustainable agricultural practices and the irrigation of agricultural lands versus rainfed fields. We anticipate these methods will be applicable to other watersheds outside the southeast region too.

Agricultural runoff ranks second only to atmospheric deposition as a source of nitrogen pollution to streams in the southeastern United States. Climate-smart practices such as irrigation have the potential to reduce these impacts and provide resilience in the face of climate change. The purpose of this study is to evaluate the impact of irrigation amounts and fertilizer application strategies on surface nitrate export to surrounding steams. Data from an existing experiment on corn nitrogen fertilization in the Southeastern US was utilized and a crop simulation model was employed to simulate the water and nitrogen dynamics within the soil with particular emphases on nutrient uptake and residual nutrients. left in the soil after harvest under varying fertilization scenarios. A hydrologic and nutrient export model was developed to run in conjunction with the crop model to simulate lateral export from the fields. The results of this study indicate that climate and nutrient management are the dominant factors in determining surface nutrient transport under both rain fed and irrigated conditions, confirming previous studies. The overall results show that irrigation, on average, reduced nutrient export from the surface, especially in dry years. The effect is even greater if the nutrients are applied later in the year while irrigation is on-going. While this present study provides an initial look at the potential impacts of irrigation on nutrient export in humid areas, the available on-farm observational data is limited in its content. However, the results obtained support existing literature and provide further evidence on the impact of irrigation as a climate resilient practice and will help direct future studies in the region.

The principle of maximum entropy (POME) can be used to develop vertical soil moisture profiles. The minimal inputs required by the POME model make it an excellent choice for remote sensing applications. Two of the major input requirements of the POME model are the surface boundary condition and profile-mean moisture content. Microwave-based soil moisture estimates from Advanced Microwave Scanning Radiometer (AMSR-E) can supply the surface boundary condition whereas thermal infrared-based moisture estimated from the Atmosphere Land Exchange Inverse (AELXI) surface energy balance model can provide the mean moisture condition. A disaggregation approach was followed to downscale coarse resolution (~ 25 km) microwave soil moisture estimates to match the finer resolution (~ 5 km) thermal data. The study was conducted over multiple years (2006–2010) in the southeastern United States. Disaggregated soil moisture estimates along with the developed profiles were compared with the Noah land surface model (Noah LSM) within the framework of NASA Land Information System (LIS), as well as in-situ measurements from 10 Natural Resource Conservation Services (NRCS) Soil Climate Analysis Network (SCAN) sites spatially distributed within the study region. The overall disaggregation results at the SCAN sites indicated that in most cases disaggregation improved the temporal correlations with unbiased root mean square errors in the range of 0.01–0.09 in volumetric soil moisture. The profile results at SCAN sites showed a mean bias of 0.03 and 0.05; unbiased RMSE of 0.05 and 0.06; and correlation coefficient of 0.44 and 0.48 against SCAN observations and Noah LSM, respectively.