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

Temporal Calibration of an Evaporation-Based Spatial Disaggregation Method of SMOS Soil Moisture Data

International audienceThe resolution of current satellite surface soil moisture (SM) estimates is very low, of tens of kilometers, which proves to be insufficient for various agricultural and hydrological applications. Amongst the existing downscaling approaches of remotely sensed SM, DISPATCH (DISaggregationbased on a Physical And Theoretical scale CHange) improves the resolution of SMOS (Soil Moisture and Ocean Salinity) soil moisture data using soil evaporative efficiency (SEE) estimates at high resolution (HR) and a SEE(SM) model implemented at low resolution (LR). Defined as the ratio of actual to potential soil evaporation, SEE can be derived from the remotely sensed land surface temperature (LST) and normalized difference vegetation index (NDVI). The current version of DISPATCH uses a linear SEE(SM) model. This study aims at improving the SEE(SM) model and testing different calibration strategies, to ultimately have more robust and better downscaled SM products. A nonlinear SEE(SM) model is introduced and its influence on the derived HR SM products is studied over a range of conditions. Each model, linear and nonlinear, is calibrated from remote sensing data on a daily and a multi-date basis. The approaches were tested over two mixed dry and irrigated areas in Catalonia, Spain, and over one dry area in Morocco. When using the linear model, better statistical results were generally obtained using a daily calibration (current version of DISPATCH), most notably over one Spanish site. However, the best results were systematically obtained for an annually calibrated nonlinear model, in terms of all metrics considered:correlation coefficient, slope of the linear regression, bias, unbiased root mean square error. In particular, when using the annually calibrated nonlinear SEE (SM) model, the temporal slope of the linear regression between disaggregated and in situ soil moisture increased to 1.16 and 0.75 for one Spanish site and for the Moroccan site (as opposed to 0.44 and 0.58, respectively, when using the linear model with a daily calibration). The temporal correlation coefficient increased to 0.47 and 0.54 over the Spanish sites (as opposed to 0.18 and 0.27, respectively, when using the linear model with a daily calibration). Those contrasted results indicate compensation effects between the model type and the calibration strategy. Taking into account studies that report the strong nonlinear behavior of the SEE with respect to SM, the introduction of the nonlinear SEE(SM) model in DISPATCH,combined with a multi-date calibration, is proven to perform significantly better under various conditions, leading to more robust disaggregated SM products. The SEE modeling based on the nonlinear SM model, with a multi-date calibration, could be integrated into the CATDS—Centre Aval de Traitement des Données SMOS as a future product, as well as into existing evapotranspiration models, which are based on a combination of thermal and microwave data

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