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

This paper presents the first attempt to include soil moisture information from remote sensing in the tools available to desert locust managers. The soil moisture requirements were first assessed with the users. The main objectives of this paper are: i) to describe and validate the algorithms used to produce a soil moisture dataset at 1 km resolution relevant to desert locust management based on DisPATCh methodology applied to SMOS and ii) the development of an innovative approach to derive high-resolution (100 m) soil moisture products from Sentinel-1 in synergy with SMOS data. For the purpose of soil moisture validation, 4 soil moisture stations where installed in desert areas (one in each user country). The soil moisture 1 km product was thoroughly validated and its accuracy is amongst the best available soil moisture products. Current comparison with in-situ soil moisture stations shows good values of correlation (R>0.7R>0.7) and low RMSE (below 0.04 m3 m−3). The low number of acquisitions on wet dates has limited the development of the soil moisture 100 m product over the Users Areas. The Soil Moisture product at 1 km will be integrated into the national and global Desert Locust early warning systems in national locust centres and at DLIS-FAO, respectively

Coupling long-term prospection data and remote-sensing vegetation index to help in the preventative control of Desert Locust

Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a preventative control avoiding population to reach high and uncontrollable densities. The areas of potential start of gregarization process for Desert Locust are large and preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times

Soil moisture estimates from satellite imagery to improve desert locust forecast

International audienceDesert locust is still a major threat to agriculture in an extensive area from Western Africa to India. The preventive management of Desert locust relies on surveying its potential habitats to find outbreaks as early as possible and control the gregarizing populations. Despite being a major ecological driver of Desert locust populations, soil moisture is missing in the current imagery toolkit for preventive management. The SMELLS project funded by the European Space Agency proposed to develop a product of 1km resolution estimates of soil moisture in 4 countries of Western and Northern Africa to test the potential help of soil moisture in Desert locust preventive management. We used statistical analyses coupling locust presence/absence observations from field surveys with the soil moisture product to evaluate how soil moisture dynamics may influence the development of locust populations. Further analyses aimed in comparing the potential help of soil moisture in preventive management compared to vegetation index, rainfall estimates and soil temperature. Finally, a forecasting model was established with a random-forest approach using both vegetation index and soil moisture. We observed that a soil moisture dynamics of increase above 9% for 20 days followed by a decrease of soil moisture may increase the chance to observe locusts 70 days later. The gain in early warning timing compared to using imagery from vegetation was estimated to be three weeks. We demonstrated that the errors of the forecasting model may be reduced by the combination of structural and dynamical indicators of soil moisture and vegetation index. However, the forecasts of locust presence were not perfect and there were plenty of room for improvements. Nevertheless, we recommend the use of maps of soil moisture estimates in the planning of survey campaign of Desert locust as the gain in timing is substantial compared to vegetation index products

Soil moisture estimates from satellite imagery to improve desert locust forecast

International audienceDesert locust is still a major threat to agriculture in an extensive area from Western Africa to India. The preventive management of Desert locust relies on surveying its potential habitats to find outbreaks as early as possible and control the gregarizing populations. Despite being a major ecological driver of Desert locust populations, soil moisture is missing in the current imagery toolkit for preventive management. The SMELLS project funded by the European Space Agency proposed to develop a product of 1km resolution estimates of soil moisture in 4 countries of Western and Northern Africa to test the potential help of soil moisture in Desert locust preventive management. We used statistical analyses coupling locust presence/absence observations from field surveys with the soil moisture product to evaluate how soil moisture dynamics may influence the development of locust populations. Further analyses aimed in comparing the potential help of soil moisture in preventive management compared to vegetation index, rainfall estimates and soil temperature. Finally, a forecasting model was established with a random-forest approach using both vegetation index and soil moisture. We observed that a soil moisture dynamics of increase above 9% for 20 days followed by a decrease of soil moisture may increase the chance to observe locusts 70 days later. The gain in early warning timing compared to using imagery from vegetation was estimated to be three weeks. We demonstrated that the errors of the forecasting model may be reduced by the combination of structural and dynamical indicators of soil moisture and vegetation index. However, the forecasts of locust presence were not perfect and there were plenty of room for improvements. Nevertheless, we recommend the use of maps of soil moisture estimates in the planning of survey campaign of Desert locust as the gain in timing is substantial compared to vegetation index products

Soil moisture from remote sensing to forecast desert locust presence

International audiencePreventive 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.La lutte préventive contre le criquet pèlerin consiste à détecter le plus tôt possible tout début de pullulation. La télédétection est de plus en plus utilisée dans la stratégie de lutte préventive. Bien que l'humidité du sol soit une variable écologique majeure dans la dynamique des populations de criquet pèlerin, elle manque à l'arsenal d'outils de télédétection pour cette stratégie préventive. A travers des analyses statistiques qui relient les observations de présence/absence des criquets aux estimations d'humidité du sol à une résolution d'un kilomètre issues d'un algorithme de désagrégation, nous évaluons le potentiel de cet indicateur dans la gestion préventive du criquet pèlerin. Nous observons qu'une augmentation de l'humidité du sol au‐dessus de 0.09 cm3/cm3 pendant 20 jours suivie d'une diminution augmente les chances d'observer des criquets pèlerins 70 jours plus tard. Nous estimons que cet indicateur permet de gagner trois semaines dans l'alerte précoce par rapport à l'utilisation d'indicateurs de végétation. Nous démontrons que les erreurs de prévision de présence des criquets peuvent être réduites en combinant dans des modèles statistiques plusieurs types d'indicateurs tels que l'humidité du sol et des indices de végétation. Implications pour les politiques publiques. Nous recommandons l'utilisation opérationnelle des estimations d'humidité du sol à 1 km de résolution dans la lutte préventive contre le criquet pèlerin. Quand l'humidité du sol augmente dans une zone aride d'habitat potentiel du criquet pèlerin, des prospections acridiennes devraient être conduites dans les deux mois suivants afin d’évaluer le besoin d'effectuer d'autres mesures préventives. L'humidité du sol estimée par télédétection pourrait aussi être utile dans la gestion intégrée d'autres ravageurs des cultures