Agricultural Drought Risk Assessment of Rainfed Agriculture in the Sudan Using Remote Sensing and GIS: The Case of El Gedaref State

Hitherto, most research conducted to monitor agricultural drought on the African continent has focused only on meteorological aspects, with less attention paid to soil moisture, which describes agricultural drought. Satellite missions dedicated to soil moisture monitoring must be used with caution across various scales. The rainfed sector of Sudan takes great importance due to it is high potential to support national food security. El Gedaref state is significant in Sudan given its potentiality of the agricultural sector under a mechanized system, where crop cultivation supports livelihood sources for about 80% of its population and households, directly through agricultural production and indirectly through labor workforce. The state is an essential rainfed region for sorghum production, located within Sudan's Central Clay Plain (CCP). Enhancing soil moisture estimation is key to boosting the understanding of agricultural drought in the farming lands of Sudan. Soil moisture measuring stations/sensors networks do not exist in the El Gedaref agricultural rainfed sector. The literature shows a significant gap in whether soil moisture is sufficient to meet the estimated water demands of cultivation or the start of the growing season. The purpose of this study is to focus principally on agricultural drought. The soil moisture data retrieved from the Soil Moisture Active Passive (SMAP) mission launched by NASA in 2015 were compared against in situ data measurements over the agricultural lands. In situ points (at 5 cm, 10 cm, and 20 cm depths) corresponding to 9×9 km SMAP pixel foot-print are rescaled to conduct a point-to-pixel evaluation of SMAP product over two locations, namely Samsam and Kilo-6, during the rainy season 2018. Four errors were measured; Root Mean Squared Error (RMSE), Mean Bias Error (MBE), unbiased RMSE (ubRMSE), Mean Absolute Bias Error (MABE), and the coefficient of determination R2. SMAP improve (significantly at the 5% level for SM). The results indicated that the SMAP product meets its soil moisture accuracy requirement at the top 5 cm and in the root zone (10 and 20 cm) depths at Samsam and Kilo-6. SMAP demonstrates higher performance indicated by the high R2 (0.96, 0.88, and 0.97) and (0.85, 0.94, and 0.94) over Samsam and Kilo-6, respectively, and met its accuracy targeted by SMAP retrieval domain at ubRMSE 0.04 m3m-3 or better in all locations, and most minor errors (MBE, MABE, and RMSE). The possibility of using SMAP products was discussed to measure agricultural drought and its impacts on crop growth during various growth stages in both locations and over the CCP entirely. The croplands of El Gedaref are located within the tropical savanna (AW, categorization following the Köppen climate classification), warm semi-arid climate (BSh), and warm desert climate (BWh). The areas of interest are predominantly rainfed agricultural lands, vulnerable to climate change and variability. The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), SMAP at the top surface of the soil and the root zone, and Soil Water Deficit Index (SWDI) derived from SMAP were analyzed against the Normalized Difference Vegetation Index (NDVI). The results indicate that the NDVI val-ues disagree with rainfall patterns at the dekadal scale. At all isohyets, SWDI in the root zone shows a reliable and expected response of capturing seasonal dynamics concerning the vegetation index (NDVI) over warm desert climates during 2015, 2016, 2017, 2018, and 2019, respectively. It is concluded that SWDI can be used to monitor agricultural drought better than rainfall data and SMAP data because it deals directly with the available water content of the crops. SWDI monitoring agricultural drought is a promising method for early drought warning, which can be used for agricultural drought risk management in semi-arid climates. The comparison between sorghum yield and the spatially distributed water balance model was assessed according to the length of the growing period. Late maturing (120 days), medium maturing (90-95 days), and early maturing variety (80-85 days). As a straightforward crop water deficit model. An adapted WRSI index was developed to characterize the effect of using different climatic and soil moisture remote sensing input datasets, such as CHIRPS rainfall, SMAP soil moisture at the top 5 cm and the root zone, MODIS actual evapotranspiration on key WRSI index parameters and outputs. Results from the analyses indicated that SMAP best captures season onset and length of the growing period, which are critical for the WRSI index. In addition, short-, medium-, and long-term sorghum cultivar planting scenarios were con-sidered and simulated. It was found that over half of the variability in yield is explained by water stress when the SMAP at root zone dataset is used in the WRSI model (R2=0.59–0.72 for sorghum varieties of 90–120 days growing length). Overall, CHIRPS and SMAP root zone show the highest skill (R2=0.53–0.64 and 0.54–0.56, respectively) in capturing state-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agrometeorological risk applications. The results of this study are important and valuable in supporting the continued development and improvement of satellite-based soil moisture sensing to produce higher accuracy soil moisture products in semi-arid regions. The results also highlight the growing awareness among various stakeholders of the impact of drought on crop production and the need to scale up adaptation measures to mitigate the adverse effects of drought

Connecting NASA Science and Engineering with Earth Science Applications

ABSTRACT The National Research Council (NRC) recently highlighted the dual role of NASA to support both science and applications in planning Earth observations. This article reports the efforts of the NASA Applied Sciences Program and NASA Soil Moisture Active Passive (SMAP) mission to integrate applications with science and engineering in prelaunch planning. The SMAP Early Adopter program supported the prelaunch applied research that comprises the SMAP Special Collection of the Journal of Hydrometeorology. This research, in turn, has resulted in unprecedented prelaunch preparation for SMAP applications and critical feedback to the mission to improve product specifications and distribution for postlaunch applications. These efforts have been a learning experience that should provide direction for upcoming missions and set some context for the next NRC decadal survey

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Synthesis of Satellite Microwave Observations for Monitoring Global Land-Atmosphere CO2 Exchange

This dissertation describes the estimation, error quantification, and incorporation of land surface information from microwave satellite remote sensing for modeling global ecosystem land-atmosphere net CO2 exchange. Retrieval algorithms were developed for estimating soil moisture, surface water, surface temperature, and vegetation phenology from microwave imagery timeseries. Soil moisture retrievals were merged with model-based soil moisture estimates and incorporated into a light-use efficiency model for vegetation productivity coupled to a soil decomposition model. Results, including state and uncertainty estimates, were evaluated with a global eddy covariance flux tower network and other independent global model- and remote-sensing based products

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

A Review of Earth Observation-Based Drought Studies in Southeast Asia

Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region

Irrigation Signals Detected from SMAP Soil Moisture Retrievals

Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled land-atmosphere system. One way to improve irrigation representation in models is to assimilate soil moisture observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new Enhanced soil moisture product from the Soil Moisture Active Passive (SMAP) satellite is able to capture irrigation signals over three semi-arid regions in the western United States. This marks an advancement in earth-observing satellite skill and the ability to monitor human impacts on the water cycle