Grand-Couronne, France
Grand-Couronne, France

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Lebourgeois V.,UPR SCA | Chopart J.-L.,CIRAD UPR SCA | Begue A.,CIRAD - Agricultural Research for Development | Le Mezo L.,CIRAD UPR SCA
Agricultural Water Management | Year: 2010

In humid regions, the timing and quantity of a complementary irrigation regime is challenging because of the irregularity of rainfalls events. In this study, we tested the use of a thermal infrared derived empirical crop water stress index (CWSIe) as an in situ measurement of the water status of sugarcane, to better monitor the irrigation scheduling. To do this, we set up a 2-year experiment in Reunion Island, on a trial with plots under different water conditions (rainfed and irrigated). Crop surface temperature was measured daily with infrared radiometers (Apogee Instruments) installed above the canopy, and soil moisture and drainage measurements were used to derive the ratio between actual and maximum evapotranspiration (AET/MET) values that were then averaged on "hydrically homogeneous" time periods (between 7 and 25 days). Only the thermal data acquired on clear days and 1 h after noon in 2007 were used to define the empirical lower and upper baselines required for the calculation of empirical CWSI. The data set acquired in 2008 was used to test the robustness of the method as we used the upper and lower baselines defined in 2007 to calculate CWSIe. The linear regression between AET/MET and (1 - CWSIe) averaged on the same periods (values ranging between 0.4 and 1) showed a significant correlation for both experimental years (global R2 = 0.75 and RMSE = 0.12). This result indicates the effectiveness of the CWSIe to measure the water status of the sugarcane crop, even in humid conditions with a vapor pressure deficit (VPD) between 0.5 and 2.1. We conclude the study by discussing the complementarity of this remote water stress index (CWSIe) with OSIRI water balance modelling tool currently used in Reunion Island for monitoring sugarcane crop irrigation. © 2009 Elsevier B.V. All rights reserved.

Mulianga B.,Kenya Sugar Research Foundation KESREF | Mulianga B.,CIRAD - Agricultural Research for Development | Begue A.,CIRAD - Agricultural Research for Development | Simoes M.,CIRAD - Agricultural Research for Development | And 2 more authors.
Remote Sensing | Year: 2013

This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002-2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R2 = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R2 = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t.ha-1. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the size of MODIS 250 m pixel, and in the context of a 15-month crop cycle with no seasonal cropping calendar. Future satellite missions should permit monitoring of sugarcane yields using image resolutions that facilitate extraction of crop phenology from a group of individual plots. © 2013 by the authors.

Lebourgeois V.,CIRAD UPR SCA | Lebourgeois V.,CIRAD - Agricultural Research for Development | Begue A.,CIRAD - Agricultural Research for Development | Labbe S.,IRSTEA | And 2 more authors.
Precision Agriculture | Year: 2012

Image-based remote sensing is one promising technique for precision crop management. In this study, the use of an ultra light aircraft (ULA) equipped with broadband imaging sensors based on commercial digital cameras was investigated to characterize crop nitrogen status in cases of combined nitrogen and water stress. The acquisition system was composed of two Canon ® EOS 400D digital cameras: an original RGB camera measuring luminance in the Red, Green and Blue spectral bands, and a modified camera equipped with an external band-pass filter measuring luminance in the near-infrared. A 5 month experiment was conducted on a sugarcane (Saccharum officinarum) trial consisting of three replicates. In each replicate, two sugarcane cultivars were grown with two levels of water input (rainfed/irrigated) and three levels of nitrogen (0, 65 and 130 kg/ha). Six ULA flights, coupled with ground crop measurements, took place during the experiment. For nitrogen status characterisation, three indices were tested from the closed canopy: the normalised difference vegetation index (NDVI), the green normalised difference vegetation index (GNDVI), and a broadband version of the simple ratio pigment index (hereafter referred to as the SRPI b), calculated from the ratio between blue and red bands of the digital camera. The indices were compared with two nitrogen crop variables: leaf nitrogen content (N L) and canopy nitrogen content (N C). SRPI b showed the best correlation (R 2 = 0. 7) with N L, independently of the water and the N treatment. NDVI and GNDVI were best correlated with N C values with correlation coefficients of 0. 7 and 0. 64 respectively, but the regression coefficients were dependent on the water and N treatment. These results showed that SRPI b could characterise the nitrogen status of sugarcane crop, even in the case of combined stress, and that such acquisition systems are promising for crop nitrogen monitoring. © 2012 Springer Science+Business Media, LLC.

Mulianga B.,CIRAD - Agricultural Research for Development | Begue A.,CIRAD - Agricultural Research for Development | Simoes M.,CIRAD - Agricultural Research for Development | Simoes M.,EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária | And 2 more authors.
European Space Agency, (Special Publication) ESA SP | Year: 2012

The method currently used to estimate sugarcane yield in Kenya is made exclusively by a visual physical assessment (VPA). In this method, 15% of the sugarcane fields are sampled and weighted to estimate the total sugarcane production of the following year. This study explores the use of time series of spectral vegetation indices from low resolution satellite images to forecast sugarcane yield in Kenya. Historical yield data (2001 - 2010) for the six sugarcane growing zones in Western Kenya were used to correlate 250 m MODIS-NDVI (MODerate resolution Imaging Spectro-radiometer - Normalized Difference Vegetation Index) with historical yield data for multiple growing seasons. When using the whole data set (6 zones and 10 years), data analysis showed that NDVI is neither related to the annual rain, nor to the sugarcane yield. When splitting the dataset according to the year or to the zone, the analysis showed that the relationship between NDVI and yield is generally significant for a given year, and not for a given zone. These results suggest that at the zone scale, the land use is relatively constant nevertheless there is a high land use heterogeneity between zones. This hypothesis was supported by the good correlation that exists between the slope of NDVI and yield at the zone scale, and the fraction of sugarcane crop on the same area (R2 = 0.75, p < 0.001). These preliminary results showed that MODIS NDVI can be used to assess yearly sugarcane yield at the zone scale, using historical NDVI and yield data. Next step will consist in characterizing the spatial variability of the zones and sectors, through the development of a satellite-derived "landscape" index both sensitive to the cropping practices and to the environmental conditions. The slope between NDVI and yield, calculated per spatial unit, will be related to this landscape index, in order to develop a generic NDVI-sugarcane relationship t hat could be applied in Western Kenya.

Mulianga B.,CIRAD - Agricultural Research for Development | Begue A.,CIRAD - Agricultural Research for Development | Simoes M.,CIRAD - Agricultural Research for Development | Simoes M.,EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária | And 2 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

Characterization of landscapes is crucial in modelling potential soil erosion to ascertain environmental services that are provided by the main land use in the ecosystem. Remote sensing techniques have proved successful in characterization of landscapes. In this study area of a rain-fed Kibos-Miwani sugar zone of Kenya, we used Normalized difference vegetation index (NDVI) data extracted from satellite imagery to characterize the spatial and temporal heterogeneity of the vegetation conditions, and to model potential soil erosion. Data used included Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m NDVI acquired in the period 2000 to 2013; 30 m Landsat5 time series images acquired between November 2010 and June 2011; a 30 m digital elevation model (DEM); and ground observations (land cover and soil characteristics). Temporal NDVI was extracted directly from MODIS 250 m images to study the changes in seasonal vegetation conditions with time, and spatial NDVI was extracted by analysing Landsat5 images at the field scale. NDVI extracted from Landsat images for a specific date, represented vegetation conditions for that simulation period. To compute potential soil erosion, we used Landsat 5 NDVI, the slope, aspect, curvature and soil physical properties as input data sets in the spatially explicit Fuzzy-based dynamic soil erosion model (FuDSEM). Land cover data collected revealed that sugarcane was the main land use, occupying 76% of the land cover. Results were consistent with crop management practices, illustrating a spatially heterogeneous land scape with varied vegetation conditions throughout the year. Out of simulations, we noted a homogeneous low erosion risk in areas with natural land cover with a global mean of 0.42. Medium to intense erosion risk in cropped areas was evident, with erosion risk varying from one pixel to the other. Simulation results suggest that crop management practices (planting and harvesting processes) are the drivers of erosion in sugar cane cultivated areas. © 2013 SPIE.

PubMed | CIRAD UPR SCA, CIRAD - Agricultural Research for Development, IRSTEA, Avion Jaune and Montpellier SupAgro
Type: Journal Article | Journal: Sensors (Basel, Switzerland) | Year: 2016

The use of consumer digital cameras or webcams to characterize and monitor different features has become prevalent in various domains, especially in environmental applications. Despite some promising results, such digital camera systems generally suffer from signal aberrations due to the on-board image processing systems and thus offer limited quantitative data acquisition capability. The objective of this study was to test a series of radiometric corrections having the potential to reduce radiometric distortions linked to camera optics and environmental conditions, and to quantify the effects of these corrections on our ability to monitor crop variables. In 2007, we conducted a five-month experiment on sugarcane trial plots using original RGB and modified RGB (Red-Edge and NIR) cameras fitted onto a light aircraft. The camera settings were kept unchanged throughout the acquisition period and the images were recorded in JPEG and RAW formats. These images were corrected to eliminate the vignetting effect, and normalized between acquisition dates. Our results suggest that 1) the use of unprocessed image data did not improve the results of image analyses; 2) vignetting had a significant effect, especially for the modified camera, and 3) normalized vegetation indices calculated with vignetting-corrected images were sufficient to correct for scene illumination conditions. These results are discussed in the light of the experimental protocol and recommendations are made for the use of these versatile systems for quantitative remote sensing of terrestrial surfaces.

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