SMOS based high resolution soil moisture estimates for desert locust preventive management

Escorihuela, María José; Merlin, Olivier; Stefan, Vivien; Moyano, Gorka; Eweys, Omar Ali; Zribi, Mehrez; Kamara, Serrie I.; Benahi, Ahmed Salem; Ebbe, Mohamed Abdallahi Ould Babah; Chihrane, J.; Ghaout, Saïd; Cissé, Sory; Diakité, Fakaba; Lazar, Mohamed; Pellarin, Thierry; Grippa, Manuela; Cressman, Keith; Piou, Cyril

doi:10.1016/j.rsase.2018.06.002

Cited by 28 publications

(22 citation statements)

References 38 publications

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“…This value is in accordance, although slightly lower, with the SM range proposed by Ref. 89, which is between 0.10 and 0.20 m 3 ∕m 3 . Hopper mortality is closely linked to food shortage, 4 which in arid environments is closely linked with inadequate precipitation.…”

Section: Discussionsupporting

confidence: 92%

Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

Gómez

Salvador

Sanz

et al. 2018

J. Appl. Rem. Sens.

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Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture," J. Appl.Abstract. Desert locusts have attacked crops since antiquity. To prevent or mitigate its effects on local communities, it is necessary to precisely locate its breeding areas. Previous works have relied on precipitation and vegetation index datasets obtained by satellite remote sensing. However, these products present some limitations in arid or semiarid environments. We have explored a parameter: soil moisture (SM); and examined its influence on the desert locust wingless juveniles. We have used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM conditions under different time scenarios. RF obtained the best model performance with very good validation results according to the true skill statistic and receiver operating characteristic curve statistics. It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07 m 3 ∕m 3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA CCI SM product to complement or substitute current monitoring techniques based on precipitation datasets. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Discussionsupporting

confidence: 92%

Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

Gómez

Salvador

Sanz

et al. 2018

J. Appl. Rem. Sens.

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“…The DisPATCh algorithm has been implemented and validated in several climatic regions such as Catalonia, Spain (Merlin et al, 2013), Central Morocco (Merlin et al, 2015), South-Eastern Australia (Malbéteau, Merlin, Molero, Rüdiger, & Bacon, 2016) and two watersheds in the USA . Escorihuela et al (2018) have shown that field measures of soil moisture in desert areas of Algeria, Mali, Mauritania and Morocco correlates well with SMELLS estimates of soil moisture using the DisPATCh algorithm.…”

Section: Introductionmentioning

confidence: 86%

Soil moisture from remote sensing to forecast desert locust presence

Piou

Gay

Benahi

et al. 2019

Journal of Applied Ecology

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Preventive control of desert locusts is based on monitoring recession areas to detect outbreaks. Remote sensing has been increasingly used in the preventive control strategy. Soil moisture is a major ecological driver of desert locust populations but is still missing in the current imagery toolkit for preventive management. By means of statistical analyses, combining field observations of locust presence/absence and soil moisture estimates at 1 km resolution from a disaggregation algorithm, we assess the potential of soil moisture to help preventive management of desert locust. We observe that a soil moisture dynamics increase of above 0.09 cm3/cm3 for 20 days followed by a decrease of soil moisture may increase the chance to observe locusts 70 days later. We estimate the gains in early warning timing compared to using imagery from vegetation to be 3 weeks. We demonstrate that forecasting errors may be reduced by the combination of several types of indicators such as soil moisture and vegetation index in a common statistical model forecasting locust presence. Policy implications. Soil moisture estimates at 1 km resolution should be used to plan desert locust surveys in preventive management. When soil moisture increases in a dry area of potential habitat for the desert locust, field surveys should be conducted two months later to evaluate the need of further preventive actions. Remote sensing estimates of soil moisture could also be used for other applications of integrated pest management.

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“…SMOS data supplements the CWB's information on soil moisture, which allows the indication of soil moisture conditions suitable for plant growth, and threats of droughts and floods. Comparisons of SMOS data against in-situ SM measurements showed good values of correlation (R > 0.7) [25,26] and relatively low RMSE (i.e., 0.04 m 3 m −3 ) [7,22,26].…”

Section: Introductionmentioning

confidence: 92%

Evaluation of Soil Moisture Variability in Poland from SMOS Satellite Observations

2019

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Soil moisture (SM) data play an important role in agriculture, hydrology, and climate sciences. In this study, we examined the spatial-temporal variability of soil moisture using Soil Moisture Ocean Salinity (SMOS) satellite measurements for Poland from a five-year period (2010–2014). SMOS L2 v. 551 datasets (latitudinal rectangle 1600 × 840 km, centered in Poland) averaged for quarterly (three months corresponding to winter, spring, summer, and autumn) and yearly values were used. The results were analysed with the use of classical statistics and geostatistics (using semivariograms) to acquire information about the nature of anisotropy and the lengths and directions of spatial dependences. The minimum (close to zero) and maximum soil moisture values covered the 0.5 m3 m−3 range. In particular quarters, average soil moisture did not exceed 0.2 m3 m−3 and did not drop below 0.12 m3 m−3; the corresponding values in the study years were 0.171 m3 m−3 and 0.128 m3 m−3. The highest variability of SM occurred generally in winter (coefficient of variation, CV, up to 40%) and the lowest value was recorded in spring (around 23%). The average CV for all years was 32%. The quarterly maximum (max) soil moisture contents were well positively correlated with the average soil moisture contents (R2 = 0.63). Most of the soil moisture distributions (histograms) were close to normal distribution and asymmetric data were transformed with the square root to facilitate geostatistical analysis. Isotropic and anisotropic empirical semivariograms were constructed and the theoretical exponential models were well fitted (R2 > 0.9). In general, the structural dependence of the semivariance was strong and moderate. The nugget (C0) values slightly deceased with increasing soil moisture while the sills (C0 + C) increased. The effective ranges of spatial dependence (A) were between 1° and 4° (110–440 km of linear distance). Generally, the ranges were greater for drier than moist soils. Anisotropy of the SM distribution exhibited different orientation with predominance from north-west to south-east in winter and spring and changed for from north-east to south-west or from north to south in the other seasons. The fractal dimension values showed that the distribution of the soil moisture pattern was less diverse (smoother) in the winter and spring, compared to that in the summer and autumn. The soil moisture maps showed occurrence of wet areas (soil moisture > 0.25 m3 m−3) in the north-eastern, south-eastern and western parts and dry areas (soil moisture < 0.05 m3 m−3) mainly in the central part (oriented towards the south) of Poland. The spatial distribution of SM was attributed to soil texture patterns and associated with water holding capacity and permeability. The results will help undertake appropriate steps to minimize susceptibility to drought and flooding in different regions of Poland.

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SMOS based high resolution soil moisture estimates for desert locust preventive management

Cited by 28 publications

References 38 publications

Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

Soil moisture from remote sensing to forecast desert locust presence

Evaluation of Soil Moisture Variability in Poland from SMOS Satellite Observations

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