Land use and land cover (LULC) classification using satellite images is an important approach to monitor changes on earth. To produce LULC maps, supervised classification methods are often used. For many supervised classification algorithms, independence of features is an implied assumption. However, this assumption is rarely tested. For LULC classification, using all bands as input features to models is the default approach. However, some of the bands may be highly correlated, which may cause model performances unstable. In this research, correlations and multicollinearity among multi-spectral bands are analyzed for four major LULC types, i.e. cropland, forest, developed area and water bodies. Guided by the correlation analysis, different band combinations were used to train Support Vector Machines (SVM) for four-class LULC classification and the results were compared. From our experiments, band 4, 5, 6 is the best three-band combination and band 1, 2, 5, 7 is the best four-band combination which achieved almost identical performance as using all bands for LULC classification.

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

Monitoring the effects of soil moisture on crop health is important for applications such as irrigation decision support, food security, precision agriculture as well as for more broadly understanding relationships between water and carbon cycles. In this study, we examine the relationships between soil moisture and crop health indicators, how they respond to each other on timescales from days to weeks. The satellite-derived indicators considered are the normalized difference vegetation index (NDVI) derived from reflectance obtained with moderately wide (20-40 nm) spectral bands in the visible and near-infrared (NIR) and soil moisture products that extends to about 1 m depth from Soil Moisture Active Passive (SMAP) mission. We find that NDVI have significant correlations with soil moisture in crop fields of Nebraska. Crop types and soil types also greatly influence the correlation between crop health and soil moisture changes at various rates and strength.

Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, to stakeholders. The disseminated datasets include both numerical model results of reanalysis and forecasting from HRLDAS (High-Resolution Land Data Assimilation System), and the remote sensing datasets from NASA SMAP (Soil Moisture Active Passive) and MODIS (Moderate-Resolution Imaging Spectroradiometer). The system aims to quickly and easily create a smart, customized irrigation scheduler for individual fields to relieve the burden on farmers and to significantly reduce wasted water, energy, and equipment due to excessive irrigation. The system is prototyped here with an application in Nebraska, demonstrating its ability to collect and deliver information to end-users via the web application, which provides online analytic functionality such as point-based query, spatial statistics, and timeseries query. Systems such as this will play a critical role in the next few decades to sustain agriculture, which faces great challenges from climate change and increased natural disasters.