High resolution thermal and multispectral UAV imagery for precision assessment of apple tree response to water stress

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières(Edited by Pablo Gonzalez-de-Santos and Angela Ribeiro)This manuscript presents a comprehensive methodology to obtain Thermal, Visible and Near Infrared ortho-mosaics, as a previous step for the further image-based assessment of response to water stress of an experimental apple tree orchard. Using this methodology, multi-temporal ortho-mosaics of the field plot were created and accuracy of ortho-rectification and geo-location computed. Unmanned aerial vehicle (UAV) flights were performed on an irrigated apple tree orchard located in Southern France. The 6400 m² plot was composed of 520 apple trees which were disposed in 10 rows. In this field set-up, five well irrigated rows alternated with five rows submitted to progressive summer water constraints. For remote image acquisition, on 4th July, 19th July, 1st August and 6th September UAV flights with three cameras onboard (thermal, visible and near infrared) were performed at solar noon. On 1st August, five successive UAV flights were carried out at 8, 10, 12, 14 and 16 h (solar time). By using selfdeveloped software, frames were automatically extracted from the recorded thermal video and turned in the right image format. The temperature of four different targets (hot, cold, wet and dry bare soil) was continuously measured by the IR120 thermoradiometers during each flight, for radiometric calibration purpose. Based each on thirty images, all ortho-mosaics were successfully obtained. As high spatial resolution imagery requires high precision geo-location, and the root mean squared error (RMSE) of each ortho-mosaic positioning was calculated in order to assess its spatial accuracy. RMSE values were less than twice the pixel size in every case, which allowed a precise overlapping of the mosaics created. Canopy temperature data extracted from thermal images for showed significantly higher temperatures in water stressed trees compared to well irrigated, difference being related to severity of water stress. Thanks to the ultrahigh resolution of remote images obtained (<0.1m spatial resolution for thermal infrared images), and beyond its capacity to delineate efficiently each individual tree, the methodology presented here will also make it possible the analysis of intra-canopy variations and the accurate calculation of vegetation and water stress indices

How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes?

Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R2 = 0.86 *** at the national scale to R2 = 0.26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%-70%) compared to the eastern part (17%-43%); (ii) Theoretical user and producer accuracies for a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region. (Résumé d'auteur

Acquisition d'images thermiques par drone : corrections radiométriques à partir de données terrain

Thermal images have many applications in agronomy, including crop water stress status assessment. Nowadays, the miniaturization of thermal cameras allows installing them onboard the Unmanned Aerial Vehicles (UAV), but this miniaturization leads to some difficulties: the miniaturized thermal cameras have no temperature control system of their sensor. The instability of the miniaturized camera makes a high drift in the acquisition of temperature data so that acquired thermal images don't fit the real temperature of the studying object, so data have to be continuously corrected. We need to have stable reference on field in order to compute the actual temperature value. In this article we present a method for radiometric correction of UAV remote sensed thermal images. We have implemented a device in order to retrieve ground temperature measurements. This device is composed with four targets (cold, hot, dry soil, wet soil) which measured continuously the target temperature thanks to IR120 (Campbell ®) radio-thermometer. A meteorological station is included in this ground system and acquires air temperature and moisture, solar radiation, wind speed and direction every 10 seconds. The images are radiometrically corrected by linear regression from on ground thermal data collected. Corrected images have been compared with mean canopy surface temperature of a sample of 10 trees measured with radio-thermometers. The results showed a good link between data from on ground radio-thermometer and data from thermal camera after radiometric correction. We can conclude that images obtained by this method are of sufficient quality to be used in vegetation water stress studies. (Résumé d'auteur

Évaluation de la très haute résolution spatiale pour le suivi de l'état hydrique des cultures : projet Telerieg

Water is very useful in agriculture to increase yields. However all crops have different water needs and could be irrigated according to their real need: this is the precision irrigation management. It is in this context the Telerieg project aims at improving the water apport of crops by using airborne pictures. We showed that is possible to evaluate the water status of crops from airborne pictures. The method compares evapotranspiration indices calculated from airborne pictures to values measured in the field. First of all this report describes how the necessary data have been collected: field measurements and airborne pictures in the visible, near infrared and thermal infrared bands. By this data, water stress indices are calculated: WDI and S-SEBI with remote sensing informations and the ETR/ETM indices simulated by a model of culture water consumption adjusted with field measurements. Comparaisons between these indices show that such techniques are promising for precision irrigation management.L’eau est une ressource très largement utilisée dans le domaine agricole afin d’augmenter les rendements. Or toutes les cultures n’ont pas les mêmes besoins en eau, et pourraient être irriguées en fonction de leur besoin réel : c’est l’irrigation de précision. C’est dans ce cadre que le projet Telerieg a pour objectif d’améliorer l’apport en eau des cultures grâce à l’utilisation d’images aériennes. Pour atteindre cet objectif nous avons montré qu’il est possible d’approcher l’état hydrique des cultures à partir d’images aériennes. La méthode utilisée est la comparaison d’indices d’évapotranspiration calculés à partir d’images aux indices mesurés sur le terrain. Ce rapport présente dans un premier temps comment ont été acquises les données nécessaire à cette comparaison : les mesures de terrain ainsi que les images aériennes dans le visible, le proche infrarouge et l’infrarouge thermique. A partir de ces données, des indices de stress hydrique sont élaborés : WDI et S-Sebi pour la partie télédétection, l’ETR/ETM simulé par un modèle de culture à partir des mesures de terrain. Les comparaisons entre ces indices montrent que ces techniques sont prometteuses pour la gestion de l’irrigation de précision

Contribution of remote sensing in analysis of crop water stress. Case study on durum wheat

International audiencePrecision irrigation requires frequent information on crop conditions spatial and temporal variability. Image-based remote sensing is one promising techniques for precision irrigation management. In this study, we investigated the use of broad band multispectral (visible, near infrared and thermal infrared bands) and thermal airborne imagery for the characterization of water status of durum wheat crop through two indices: the Water Deficit Index (WDI) and the Simplified Surface Energy Balance Index (S-SEBI). Compari - sons between these two indices and the ratio between actual and potential evapotranspiration (AET/PET) show that such techniques are promising for precision irrigation management