CROP STATE MONITORING USING SATELLITE REMOTE SENSING IN ROMANIA

Transcription

1 CROP STATE MONITORING USING SATELLITE REMOTE SENSING IN ROMANIA Dr. Gheorghe Stancalie National Meteorological Administration Bucharest, Romania Content Introduction Earth Observation (EO) data Drought effects indicators on the vegetation, derived from satellite data Methodology and results Conclusions 1

2 Introduction Among the problems Europe is facing at the beginning of the third millennium, the reduction of the water resources, their degrading quality and the occurrence of ever more severe and frequent droughts are of critical importance. Experiments carried-out with climatic models have shown that in the Southern and South-eastern Europe the precipitation deficit will keep enhancing, in step with the global warming. In Romania, the complex agricultural drought is a climatic hazard phenomenon inducing the worst consequences ever occurred in agriculture. Recent advances on the resolution and availability of satellite geoinformation, coupled with a decrease in its associated costs (most data are free), allow the collection of timely information for vegetation state monitoring. From the Earth Observation (EO) data can be computed different vegetation indices, designed to accentuate a particular vegetation property, and can be estimated some biophysical, biological or structural vegetation parameters. Introduction (cont.) In Romania the use of remote sensing data in agriculture is a quickly developing and promising trend. For a better operative surveillance of the agricultural areas, starting with 2005, the Romanian National Meteorological Administration implemented a dedicated service based on satellite-derived products. The satellite geoinformation, elaborated by the Remote Sensing and GIS Lab, are included and analyzed in the weekly Agrometeorological Bulletin, issue by the Agrometeorological Lab. The paper presents the results of recent studies developed in the framework of national ( Drought monitoring based on space and in-situ data DROMOSIS, Space Technology and Advanced Research Program) and European R&D projects ( Mitigation drought in vulnerable area of the Mures basin MIDMURES, EU- DG Environment Grant no /2010/582303/SUB/D1) carried on in Romania, regarding the use of satellite-derived products for agricultural and pedological drought monitoring. 2

3 EO data used TERRA AQUA/MODIS Surface Reflectance 8-Day L3 Global 500 m products (MOD09A1): Provides bands 1 7 at 500 m resolution in an 8-day gridded level-3 product in the sinusoidal projection. Science Data Sets provided for this product include reflectance values for Bands 1 7, quality assessment, and the day of the year for the pixel along with solar, view, and zenith angles. TERRA AQUA/MODIS Vegetation Indices 16-Day L3 Global 250m products (MOD13Q1): Data are provided every 16 days at 250 m spatial resolution as a gridded level-3 product in the Sinusoidal projection. TERRA AQUA/MODIS Vegetation Indices 16-Day L3 Global 500m (MOD13A1): Data are provided every 16 days at 500 m spatial resolution as a gridded level-3 product in the Sinusoidal projection. TERRA AQUA/MODIS Vegetation Indices 16-Day L3 Global 1 km (MOD13A1): Data are provided every 16 days at 1 km spatial resolution as a gridded level-3 product in the Sinusoidal projection. TERRA AQUA/MODIS Leaf Area Index - LAI and Evapotranspiration: 8 days synthesis, 1 km spatial resolution. EO data used (cont.) The LANDSAT 7 ETM+ data: The main features are: a panchromatic band with 15 m spatial resolution (band 8); visible bands in the spectrum of blue, green, red, near-infrared (NIR), and mid-infrared (MIR) with 30 m spatial resolution (bands 1-5, 7); a thermal infrared channel with 60 m spatial resolution (band 6). The ENVISAT data (from 2002 to April 2012) MERIS products having a global coverage every 3 days, with 260m x 300 m spatial resolution, operating in the solar reflective spectral, in 15 bands (across range 390 nm to 1040 nm) and with a swath width of 1150 km. The SPOT VEGETATION data VGT-S10 products (ten day synthesis) with 1 km resolution are compiled by merging segments acquired in a ten days. All the segments of this period are compared again pixel by pixel to pick out the 'best' ground reflectance values. These products provide data from all spectral bands, the NDVI and auxiliary data on image acquisition parameters. The PROBA V data (launched on the 7 th of May 2013) Proba-V is a new ESA satellite mission to be launch in spring 2013, with the main task to map land cover and vegetation growth across the Earth every two days. This mission is extending the data set of the long-established SPOT Vegetation, but with an improved spatial resolution of 350 m. 3

4 Drought effects indicators on the vegetation, derived from satellite data The potential of remote sensing techniques to monitor crop vegetation state is very high because satellite sensors are able to measure the multispectral reflectance and the temperature of the crop canopies; these key physical parameters are related to two important physiological processes: photosynthesis evapotranspiration The percentage of radiation reflected from the vegetation leaves is higher in the near infrared (NIR) than in the green and red parts of the electromagnetic spectrum. This spectral behavior is useful to assess plant vigor and to separate canopy from bare soil. The spectral behavior of the plant leaf, due to water stress, is changing, by reflecting more Red light and absorbing more NIR. Spectral signature of wheat crop and soil (wet and dry) Drought effects indicators on the vegetation crops, derived from satellite data (cont.) The satellite-derived indicators considered as appropriate measure of the crop vegetation state and dryness conditions of a particular agricultural area, are expressed as: Vegetation Indices (VIs): The broadband greenness VIs: the Normalized Difference Vegetation Index (NDVI), the Soil Adjusted Vegetative Index (SAVI), the Enhanced Vegetation Index (EVI), the Modified Soil Vegetation Index (MSAVI), etc; The canopy water content indices: the Normalized Difference Water Index (NDWI), the Normalized Difference Drought Index (NDDI), the Normalized Moisture Index (NMI), the Moisture Stress Index, the Normalized Difference Infrared Index (NDII), etc. The pedological, agrometeorological and biophysical variables: The leaf area index (LAI), the biomass, the fraction of absorbed photosynthetically active radiation (fapar); The soil moisture; The evapotranspiration, etc. 4

5 The Normalized Difference Vegetation Index - NDVI NDVI = ρnir - ρred / ρnir + ρred where: ρred and ρnir stand for the spectral reflectance in the visible (red) and near-infrared regions, respectively. NDVI is one of the most well known, and most frequently used VIs. The combination of its normalized difference formulation and use of the highest absorption and reflectance regions of chlorophyll make it robust over a wide range of conditions. NDVI is very useful for deriving vegetation biophysical parameters, such as the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fapar) and percentage of green cover. NDVI high. saturates in dense vegetation conditions when LAI becomes The value of NDVI ranges from -1 to 1; the common range for green vegetation is 0.2 to 0.8. Comparing current NDVI images with older ones it is possible to assess the positive and negative deviations that occur during the growing season of vegetation and evaluate the state's relative vegetation throughout the growing season. Monitoring the vegetation state in the Romanian Western Plain, in 2011, using NDVI SPOT VEGETATION (1km) The NDVI spatial distribution, Thedistribution, NDVI spatial distribution, The NDVI spatial The NDVI distribution, spatial The NDVI distribution, spatial The NDVI distribution, spatial

6 Monitoring the vegetation state in the Romanian Western Plain (Pecica area) in 2000, 2003, 2005 and 2010, using NDVI TERRA/MODIS (250 m resolution) The NDVI spatial distribution, March 6 April 6 The NDVI spatial distribution, The NDVI April spatial 7 distribution, The May NDVI 8 spatial May 9 distribution, June 9 June 10 August 28 Monitoring the vegetation state in the Romanian Western Plain (Pecica area) for August: 2003, 2006 and 2010, using NDVI LANDSAT ETM+ (30 m resolution) NDVI synthesis for August 2003, 2006 and

7 1 The multi-annual variation of the NDVI AOI AOI The NDVI TERRA MODIS time series in the Romanian Western Plain (Pecica area) The annual variation of the NDVI for Pecica Day % % difference comparing w ith average and and the multi-annual (2000 The NDVI variation indicates that the years 2000 and 2003 were droughty years as the lowest values from the entire series are recorded in the period % difference comparing w ith average The NDVI difference (in %) between the years 2010) average 7

8 The Enhanced Vegetation Index - EVI The enhanced vegetation index (EVI) is an 'optimized' index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a decoupling of the canopy background signal and a reduction in atmosphere influences. EVI is computed following this equation: EVI = G x [(ρ NIR ρ RED ) / (ρ NIR + C1 x ρ RED - C2 x ρ BLUE + L)] Where: ρ NIR, ρ RED and ρ BLUE are partially atmosphere corrected surface reflectances; L is the canopy background adjustment that addresses non-linear, differential NIR and red radiant transfer through a canopy; C1, C2 are the coefficients of the aerosol resistance term and G is the gain factor. The coefficients adopted in the MODIS-EVI algorithm are: L=1;C1 = 6; C2 = 7.5, and G = 2.5. The EVI is most useful in regions, where the NDVI may saturate and is strongly correlated with structural parameters of canopy, such LAI. The value of this index ranges from -1 to 1, the common range for green vegetation being between 0.2 to

9 The TERRA MODIS - EVI spatial distribution, in the Western part of Romania (Pecica area) between March 6 April , 2003, 2005 and 2010 The EVI spatial distribution, June 10 August 28 The EVI spatial distribution, The EVI March spatial 6 distribution, April The 6 EVI April spatial 7 distribution, May 8 May 9 June 9 EVI 0.80 The multi-annual variation of the EVI EVI AOI 1 EVI AOI Day The TERRA MODIS EVI time series in the Western part of Romania (lower basin of the Mures River, Pecica agricultural area) Day 2007 EVI 2008 The annual variation of the EVI for Pecica The multi-annual EVI analysis reveals lower EVI values in 2000, 2003 and 2007 comparing to the other years

10 The Normalized Difference Water Index (NDWI) NDWI is a satellite-derived index from the near- I (NIR) and short wave IR (SWIR) channels: NDWI = (ρ NIR ρ SWIR ) / (ρ NIR + ρ SWIR ) NDWI index is a good indicator of water content of leaves and is used for detecting and monitoring the humidity of the vegetation cover. The SWIR reflectance reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. NWDI holds considerable potential for drought monitoring because the two spectral bands used for its calculation are responsive to changes in the water content (SWIR band). The value of NDWI ranges from -1 to 1. The common range for green vegetation is -0.1 to 0.4. The NDWI production chain produces every day a maximum value time composite image of the previous ten days from daily TERRA AQUA/MODIS data available at 1 Km spatial resolution. The MODIS NDWI over Romania from

11 The NDWI over Romania, obtained from MODIS MOD09A1 products for 2005 (rainy year) and 2007 (droughty year) NDWI map rainy year NDWI map rainy year NDWI map droughty year NDWI map droughty year The Normalized Difference Drought Index - NDDI NDDI = (NDVI NDWI) / (NDVI + NDWI) NDDI can offer an appropriate measure of the dryness of a particular area, because it combines information on both vegetation and water. NDDI had a stronger response to summer drought conditions than a simple difference between NDVI and NDWI, and is therefore a more sensitive indicator of drought. NDDI had a stronger response to summer drought conditions than a simple difference between NDVI and NDWI, and is therefore a more sensitive indicator of drought. NDDI ranges between -1 and +1. A higher NDDI range indicates more severe drought. The NDDI is characterized by its ease of calculation because it is based on normalized difference (addition and subtraction) and it does not depend on time series data. This index can be an optimal complement to in-situ based indicators or for other indicators based on remote sensing data. 11

12 The NDDI obtained from MODIS - MOD09A1 products (10-days composite) over Romania NDDI map rainy year NDDI map rainy year NDDI map droughty year NDDI map droughty year The Leaf Area Index - LAI The Leaf Area Index (LAI) is a key biophysical canopy descriptor, which play a major role in vegetation physiological processes and ecosystem functioning. Assessment of crop LAI and its spatial distribution are of importance for crop growth monitoring, vegetation stress, crop forecasting, yield predictions and management practices. The Leaf Area Index (LAI), defined as half the total leaf area per unit ground surface area, is a key biophysical canopy descriptor, which play a major role in vegetation physiological processes and ecosystem functioning. LAI are generated globally from various sensors (AVHRR, MODIS, MISR, POLDER, SPOT-VGT, etc.) with data at different spatial resolutions (250 m to 1 3 Km) and temporal frequencies (4-day, 8-day and monthly). The algorithm for generating the MODIS LAI products uses surface reflectance (MOD09) and land cover (MOD12) products. The MODIS LAI algorithm is based on the analysis of multispectral and multi-directional surface reflectance signatures of vegetation elements. 12

13 MODIS LAI products, 1km spatial resolution in the Western part of Romania (lower basin of the Mures River, Pecica agricultural area) Spatial variation of average LAI values (the 6 March to 28 of August) The LAI deviation from the multi-annual average (6 March 6 28 August) The Fraction of Absorbed Photosynthetically Active Radiation - fapar The fapar measures the fraction of the incoming solar radiation (in the µm wavelength region) at the top of the vegetation canopy that contributes to the photosynthetic activity of plants. The fapar is a biophysical variable directly correlated with the primary productivity of the vegetation, since the intercepted PAR is the energy (carried by photons) underlying the biochemical productivity processes of plants. The fapar is one of the 50 Essential Climate Variables recognized by the UN Global Climate Observing System (GCOS) as necessary to characterize the climate of the Earth. fapar parameter is considered a good indicator for detecting and assessing the impact of drought on plant coatings (crops, natural vegetation). So, providing this information to users can be a useful tool in improving water management and agriculture. 13

14 The fapar obtained from SPOT Vegetation (10-days synthesis, 1 km resolution) over Romania, March May 2013 The Evapotranspiration Evapotranspiration (ET) represents all transpiration by vegetation and evaporation from canopy and soil surfaces, expressed in 1dimensional vertical mm/day units. Estimating evapotranspiration using remote sensing techniques have a significant role in the assessment of the Water Footprint (WF) being a key parameter of crop irrigation management, crop water demand assessment and for production modeling in dry land agriculture. The use of EO data for ET estimation is mainly based on land surface temperature (LST) and reflectivity (using different spectral regions) due to satellite ability to spatially integrate over heterogeneous surfaces at a range of resolutions and to routinely generating areal products once long time-series data availability issues are overcome. Examples of satellite derived ET products: MOD 16 Global terrestrial ET - computed globally at 8-day, monthly and annual intervals. every day at 1km, using MODIS Landover, and FPAR/LAI data and global surface meteorology. EUMETSAT Land SAF products. 14

15 The MOD 16 global ET product showing the ET distribution over the Western part of Romania (Pecica area) between 6 March 6 28 August 2000, 2003, 2004, 2005 (cumulative values) The ET spatial distribution March 6 August 28 The ET deviation from average for March 6 August 28 ET computed from high resolution satellite data The approach is based on computing a surface energy balance (SEB) using the surface temperature for estimating the sensible heat flux (H), and obtaining ET as a residual of the energy balance (Allen et al., 2007). The single-layer SEB models implicitly treat the energy exchanges between soil, vegetation and the atmosphere and compute latent heat flux by evaluating net radiant energy, soil heat flux and H. LST derived from the LANDSAT ETM m resolution (left) and the computed ET over the South-Eastern region of Romania (Dobrogea) on 7th of June 2000 (after Serban et al., 2010) 15

16 The soil moisture The soil moisture observation from space cover practically the last 30 years. Different sensors were used for this purpose according to their availability in the main spectral domains: visible near IR, thermal IR, microwave (radar) passive and active. The assimilation of satellite data into models for the retrieval of soil moisture is also a reliable solution. Example of operationally available products: AMSRE/Aqua, AMSR2/GCOM-W1 soil moisture 0.1 and 0.25 deg (10km, 25km) Global coverage; H-SAF surface soil moisture retrieved from METOP/ASCAT data H-07 (25 km resolution) - Global coverage; H-SAF surface soil moisture retrieved from METOP/ASCAT data and downscaled to 1 km resolution; H-SAF/ECMWF soil moisture in root region in 4 layers (0-7 cm, 7-28 cm, cm, cm) with 25 km resolution. The soil moisture index in the soil superficial layer issue from satellite radar data MetOp - ASCAT The satellite products provide daily information on the soil moisture, in the superficial layer (0.5 2cm), inrelativeunitsintherange0%(completydrysoil) to100%(saturatedsoil)

17 Conclusions Recent advances on the resolution and availability of satellite geo-information, coupled with a decrease in its associated costs, allow the collection of timely information for the crops vegetation state monitoring. In Romania the use of remote sensing data in agriculture is a quickly developing and promising trend. For a better operative surveillance of the agricultural areas, starting with 2005, the Romanian National Meteorological Administration implemented a dedicated service based on satellite-derived products. Different vegetation indices and pedological, agrometeorological and biophysical variables, obtained from satellite-dedicated products, or computed from original spectral bands have been used and tested in different study areas over Romania, in order to monitor and assess the vegetation water stress, at different phenological dates. The advantage of multi-annual satellite data and products availability allows the overlay and crosschecking of doughty, normal or rainy years. Thank you for your attention! 17