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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.

<p>Multiple Signals of Opportunity (multi-SoOp) across different frequencies and polarizations</p> <p>offer a potential breakthrough for remote sensing of root-zone soil moisture (RZSM). Deeper penetration depths of existing communication transmissions in the frequency ranges of 137–138, 240–270, and 360–380 MHz enable the estimation of RZSM by complementing global navigation satellite system reflectometry (GNSS-R) in L-band. The small form factor of the multi-SoOp observatory allows for high spatiotemporal coverage of RSZM by a satellite constellation in a cost-effective manner. This study aims to develop models and tools to define mission requirements for various system parameters that affect observation accuracy and coverage, for the advancement of spaceborne multi-SoOp remote sensing. These parameters include frequency and polarization combinations, observation error, inter-frequency temporal coincidence, and configuration of the satellite constellation. We present the development of a retrieval algorithm and the sensitivity analysis of retrieval accuracy. The retrieval algorithm was evaluated using synthetic observations generated from multiyear time series of in-situ soil moisture (SM) and satellite-based vegetation data. The combined use of both high and low frequencies improves retrieval accuracy by limiting uncertainties from vegetation and surface SM and providing sensitivity to deeper layers. A bivariate model, derived from the sensitivity analysis, facilitates error prediction for future science missions. We introduce a framework for tradespace exploration of the multi-SoOp satellite constellation. A constellation design study indicates that a Walker constellation comprising 24 satellites with 3 orbital planes at 500 km and 50° inclination optimizes the coverage and mission cost under mission requirements. A tower-based field experiment validated the performance of a prototype antenna for multi-SoOp using the interference pattern technique. More field experiments with improved instruments are required to further advance the multi-SoOp technique.</p>

<p>Učestale smene veoma vlažnih i izuzetno su&scaron;nih perioda mogu prouzrokovati značajne negativne<br />posledice na poljoprivredu, životnu sredinu, ekonomiju i na dru&scaron;tvo u celini. Strogi zahtevi<br />poljoprivredne proizvodnje i specifičnosti agroklimatskih uslova i samog područja Vojvodine čine ga<br />veoma vulnerabilnim na pojavu vi&scaron;ka ili manjka vode. Jasnije sagledavanje vremenske i prostorne<br />pojave vi&scaron;ka i manjka vode i procena rizika od njihove pojave moglo bi dovesti do preduzimanja<br />odgovarajućih mera pripreme i ublažavanja negativnih posledica i pre pojave hazardnog događaja.<br />U ovom radu su kreirane metodologije na osnovu kojih su izvr&scaron;ene procene rizika od suficita i<br />deficita vode na melioracionom području Vojvodine. Procene rizika dobijene su kombinacijom procena<br />ranjivosti i procena hazarda, a baziraju se na identifikaciji ključnih faktora koji utiču na pojavu suficita i<br />deficita vode, na njihovoj analizi i adekvatnom vrednovanju i uz kori&scaron;ćenje geografskog informacionog<br />sistema. Ključni faktori su odabrani na osnovu njihovog uticaja na samu pojavu ili na ublažavanje<br />efekata su&scaron;e ili pojave vi&scaron;ka vode, na osnovu dostupnosti podataka i na osnovu istraživanja brojnih<br />autora. Vrednovanje faktora ranjivosti je izvr&scaron;eno objektivnom metodom entropije. Analize hazarda su<br />izvr&scaron;ene savremenim hidrometeorolo&scaron;kim stohastičkim metodama koje sveobuhvatno sagledavaju<br />opasnosti od pojave su&scaron;e ili vi&scaron;ka vode. Rezultati procena ranjivosti, hazarda i rizika su dobijene<br />kori&scaron;ćenjem geografskog informacionog sistema u vidu karata.<br />Metodologije na osnovu kojih su u ovom radu sprovedene procene rizika od suficita i deficita vode<br />na melioracionom području Vojvodine, omogućavaju stvaranje uslova za podizanje pripravnosti na<br />pojavu ekstremnih hidrometeorolo&scaron;kih događaja, unapređenje planiranja meliorativnih mera kao i izbor i<br />dono&scaron;enje optimalnijih odluka vezanih za upravljanje i ulaganje u meliorativne sisteme, a sve u skladu<br />sa načelima i principima održivog razvoja poljoprivrede, vodoprivrede i melioracija.</p> / <p>Frequent changes of very dry and wet periods can cause significant adverse effects on agriculture,<br />environment, economy and the whole society. Strict requirements of agricultural production and special<br />characteristics of agro-climatic conditions of the Vojvodina region makes it very vulnerable to the<br />occurrences of excess water or drought. A clearer understanding of the temporal and spatial occurrence<br />of excess water or droughts and the risk assessment of their occurrence could lead to taking appropriate<br />measures of preparedness and mitigation of negative impacts before the onset of hazardous events.<br />In this dissertation, methodologies of risk assessments of water excess and water deficit on the land<br />reclamation areas in Vojvodina were created. Risk assessments were obtained by a combination of<br />vulnerability assessments and hazard assessments, and they are based on the identification of key factors<br />affecting the occurrence of water excess and water deficit, on their analysis and adequate evaluation and<br />by using geographic information systems. Key factors were selected based on their impact on the<br />occurrence or on mitigation of the effects of drought or excess water, on the basis of data availability<br />and based on the research of numerous authors. Valuation of the vulnerability factors is made by<br />objective method of entropy weighting. Hazard analyses were performed by using contemporary<br />hydrometeorological stochastic methods that comprehensively perceive threats of drought or excess<br />water. Assessments of vulnerability, hazard and risk are preformed in GIS and results are presented in a<br />form of thematic maps.<br />Methodologies for risk assessments of water excess and water deficit in land reclamation areas of<br />Vojvodina region, created or modified in this dissertation, allows the creation of conditions for<br />increasing preparedness to extreme hydro-meteorological events, improvement of land reclamation<br />measures planning as well as improvement of selection and decision-making regarding management and<br />investment in the irrigation and drainage systems, all in accordance with the principles of sustainable<br />agriculture, water management and land reclamation.<br />&nbsp;</p>