Analysis of spatiotemporal variability of water productivity in Ethiopian sugar estates: using open access remote sensing source

Gemechu, Moti Girma; Huluka, Taye Alemayehu; Steenbergen, Frank van; Wakjira, Yoseph Cherinet; Chevalking, Simon; Bastiaanssen, Sam Wim

doi:10.1080/19475683.2020.1812716

Cited by 10 publications

(4 citation statements)

References 16 publications

(16 reference statements)

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“…This outcome indicates that the evaluation accuracy of the SPL3SMP_E soil moisture product aligns closely with its intended design accuracy. This finding is consistent with conclusions drawn from research conducted in other regions [49][50][51]. Simultaneously, the consistently negative values of MD may indicate a prevalent underestimation of soil moisture by the SPL3SMP_E soil moisture product in this specific region.…”

Section: Overall Accuracy Of the Spl3smp_e Soil Moisture Productsupporting

confidence: 91%

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Zhu,

Tian,

et al. 2024

Remote Sensing

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Soil moisture (SM) is a crucial environmental variable, and it plays an important role in energy and water cycles. SM data retrieval based on microwave satellite remote sensing has garnered significant attention due to its spatial continuity, wide observational coverage, and relatively low cost. Validating the accuracy of satellite remote sensing SM products is a critical step in enhancing data credibility, which plays a vital role in ensuring the effective application of satellite remote sensing data across various fields. Firstly, this study focused on Henan Province and evaluated the accuracy of the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture (SPL3SMP_E) product along with its application in agriculture. The evaluation was based on in situ SM data from 55 stations in Henan Province. The assessment metrics used in this study include mean difference (MD), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the Pearson correlation coefficient (R). The time span of this study is from 2017 to 2020. The evaluation results indicated that the SPL3SMP_E soil moisture product performs well, as reflected by an ubRMSE value of 0.045 (m3/m3), which was relatively close to the product’s design accuracy of 0.04 (m3/m3). Moreover, the accuracy of the product was unaffected by temporal factors, but the product exhibited strong spatial aggregation, which was closely related to land use types. Then, this study explored the response of the SPL3SMP_E product to irrigation signals. The precipitation and irrigation data from Henan Province were employed to investigate the response of the SPL3SMP_E soil moisture product to irrigation. Our findings revealed that the SPL3SMP_E soil moisture product was capable of capturing over 70% of irrigation events in the study area, indicating its high sensitivity to irrigation signals in this region. In this study, the SPL3SMP_E product was also employed for monitoring agricultural drought in Henan Province. The findings revealed that the collaborative use of the SPL3SMP_E soil moisture product and machine learning algorithms proves highly effective in monitoring significant drought events. Furthermore, the integration of multiple indices demonstrated a notable enhancement in the accuracy of drought monitoring. Such an evaluation holds significant implications for the effective application of satellite remote sensing SM data in agriculture and other domains.

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Section: Overall Accuracy Of the Spl3smp_e Soil Moisture Productsupporting

confidence: 91%

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Zhu,

Tian,

et al. 2024

Remote Sensing

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“…The BWP of irrigated sugarcane in our study is comparable with the estimates of Gemechu et al (2020) who showed values ranging between 7.6 and 9 kg/m 3 in Wonji and 6.7 and 10 kg/m 3 in Metehara (Table 6). However, they show variations with the global WP ranges of sugarcane which range between ∼5 and 7 kg/m 3 in India and 3-6 kg/m 3 in Sao Paulo, Brazil due to the variations related to the soil type, sugar cane varieties, climate and management practices.…”

Section: Comparison Of Global Regional and Local Wpssupporting

confidence: 89%

Green, blue and economic water productivity: a water footprint perspective from the Upper Awash Basin, Central Ethiopia

Hirpa

Adane

Asrat

et al. 2023

Journal of Water and Climate Change

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The rise in global freshwater consumption, inefficient water use and population growth are the main drivers of water scarcity and food insecurity. This study analyzed the green, blue and economic water productivity (GWP, BWP and EWP, respectively) of the rainfed (teff, maize and sorghum) and irrigated (sugarcane) crops along with green water scarcity (GWS) in the Upper Awash Basin using water footprints (WFs) approaches. Teff has the lowest average GWP (∼0.3 kg/m3) and the highest average WF (4205 m3/ton) between 2000 and 2010 with an average EWP of ∼0.3 USD/m3 which is higher than those of other rainfed cereal crops (maize and sorghum). The highest GWS of maize was recorded in Metehara (269 mm/growing period) and the lowest in Debrezeit (70 mm/growing period) with an intermediate value of 90 and 117 mm in Wonji and Melkassa, respectively. All the rainfed crops have lower EWP (less than 0.3 USD/m3) compared with the irrigated sugarcane in the basin. This study demonstrates that increasing the value per unit of green/blue water through increasing yield per unit of supply and switching from high to low WFs crops has significant implications for addressing the water scarcity problem by setting a WP benchmark in the basin.

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“…In addition, crop models, hydrological models, remote sensing, statistics, and geographic information systems are usually used to analyze regional-scale water productivity [ 23 , 24 , 25 , 26 ]. Gemechu et al used remote sensing-derived datasets to map the seasonal and spatial variability of water productivity in sugarcane ( Saccharum officinarum L.) agriculture in three large irrigation schemes (Wonji, Fincha’a, and Metahara) in Ethiopia [ 27 ]. Ahmadpour et al proposed a joint estimation method of spatio-temporal variation of crop water productivity under a deficit irrigation scenario based on the AquaCrop model [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin

Zhang

Wang

Du³

et al. 2023

Plants

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To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temporal distribution characteristics and driving factors of water productivity of the main crops in the Yellow River Basin were analyzed with the spatial autocorrelation analysis, grey correlation analysis, sensitivity analysis, and relative contribution rate. The results showed that the minimum mean values of WPI, WPC, and WPT were 0.22, 0.35, and 0.18 kg/m3 in Qinghai, respectively, the maximum mean value of WPI was 2.11 kg/m3 in Henan, and the maximum mean values of WPC and WPT were 0.71 and 0.61 kg/m3 in Shandong, respectively. The changing trends in WPI and WPT in Qinghai and in WPC in Shandong were insignificant, whereas the WPI, WPC, and WPT in other provinces showed a significant increasing trend. Water productivity displayed a certain spatial clustering feature in the Yellow River Basin in different years, such as a high-high (H-H) aggregation in Henan in 2005, and an H-H aggregation in Shanxi in 2015 for WPI. The water productivity had a significant positive correlation with the consumption of chemical fertilizer with a 100% effective component (CFCEC), effective irrigated area (EIA), plastic film used for agriculture (PFUA), and total power of agricultural machinery (AMTP), while it had a significant negative correlation with the persons engaged in rural areas (PERA). There was a large grey correlation degree between the water productivity and the average annual precipitation (AAP), CFCEC, PFUA, consumption of chemical pesticides (CFC), and AMTP in the Yellow River Basin, but their sensitivity was relatively small. The main driving factors were EIA (8.98%), agricultural water (AW, 15.55%), AMTP (12.64%), CFCEC (12.06%), and CPC (9.77%) for WPI; AMTP (16.46%), CFCEC (13.25%), average annual evaporation (AAE, 12.94%), EIA (10.49%), and PERA (10.19%) for WPC; and EIA (14.26%), AMTP (13.38%), AAP (12.30%), CFCEC (10.49%), and PFUA (9.69%) for WPT in the Yellow River Basin. The results can provide support for improving the utilization efficiency of agricultural water resources, optimizing the allocation of water resources, and implementing high-quality agricultural developments in the Yellow River Basin.

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Analysis of spatiotemporal variability of water productivity in Ethiopian sugar estates: using open access remote sensing source

Abstract: spatiotemporal variability of water productivity in Ethiopian sugar estates: using open access remote sensing source, Annals of GIS,

Cited by 10 publications

References 16 publications

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Green, blue and economic water productivity: a water footprint perspective from the Upper Awash Basin, Central Ethiopia

Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin

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