Obtaining an agricultural drought index using solely remotely sensed products has numerous benefits over their in situ counter-parts such as if a country does not have the resources to implement an in situ ground network. One such index, created by Rhee et al. (2010), uses a combination of precipitation data from the Tropical Rainfall Measuring Mission (TRMM), with land-surface temperature (LST) data and vegetation indices (VIs) using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess drought conditions. With TRMM data becoming no longer available (as of mid-2015), this study sought to test precipitation data from the Climate Prediction Center (CPC) Morphing (CMORPH) Technique over the study period of January 2003–September 2014, in order to take the place of the TRMM data set in a drought severity index (DSI). This study also attempted to refine the methodology using the quasi-climatological anomalies (short-term climatological anomalies) of each parameter within the DSI. We validated the results of the DSI against in situ percentage available water (PAW) data from a soil water balance (SWB) model over the country of Uruguay. The results of the DSI correlated well with the PAW over the warmer months (October–March) of the year with average r-values ranging from 0.74 to 0.81, but underperformed during the colder months (April–September) with average r-values ranging from 0.38 to 0.50. This underperformance is due to the fact that precipitation during this season continues to have high variability, whereas PAW stays relatively constant. Spatially the DSI correlates well over the majority of the country with the possible exception of underperformance near the coastal area in the southeastern portion of the country. Ultimately, this research has the ability to aid Uruguay in better drought monitoring and mitigation practices as well as emergency aid resource allocation.

Fires associated with land use conversion activities such as agricultural expansion, palm and pulp plantations, peat land alteration, and industrial deforestation are significant in the country of Indonesia. The use of remotely sensed data to assess deforestation and carbon emissions over Indonesia is crucial in the monitoring of fires, as ground-based methods are not viable. Fires are currently mapped using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, but its spatial resolution (500 m) is not ideal for accurate mapping of burn scars in the region. Thus, researchers have sought to map burn scars at a higher spatial resolution. This study utilized Landsat to accomplish this task, given its spatial resolution of 30 m and tested a new methodology for identifying burn scars utilizing remotely sensed products over Central Kalimantan, Indonesia using scenes from Landsat’s Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+).

These scenes were used to assess a technique of transforming Red, Green, and Blue (RGB) color space to Hue, Saturation, and Value (HSV) space to decouple the hue from the saturation and value. When this technique was applied to a mid-infrared (MIR), near-infrared (NIR), and red false color composite, it enhanced the discrimination between vegetation, soil, and water – distinguishing burn scars from their surroundings. A hue value range for burn scars was determined; however, clouds were a limiting factor in the analysis. The approach was a good first step in reducing the amount of information one must sift through to isolate burn scars; however, more work is needed to improve this technique and develop a more automated approach for their detection.

The importance of monitoring drought is indispensable for countries whose economic viability is strongly tied to agriculture. Droughts are a major concern for the country of Uruguay, affecting their agricultural and energy sectors. Developing an accurate and reliable remotely sensed drought-monitoring tool can aid government agencies in disseminating drought information to local stakeholders will be helpful in sustaining these important economic sectors. This study is built on the Drought Severity Index (DSI) from previous terms by modifying the scaling method within the model as well as adding a ternary diagram showing the values of each of the parameters within the DSI. The DSI itself is based off of methodology from Rhee et al. (2010), which uses the climatological anomalies of NASA's Moderate Resolution Imaging Spectrometer (MODIS) daytime land surface temperature (LST) data, precipitation data from NOAA's Climate Prediction Center's Morphing Technique (CMORPH), and MODIS Normalized Difference Water Index (NDWI) data. This modified DSI as well as the parameter ternary diagrams have the potential to aid the Instituto Nacional de Investigacion Agropecuaria (INIA) and the Ministry of Agriculture in informing land managers, insurance providers, and policy makers in drought preparation and mitigation practices.