Using CROP CONDITION ASSESSMENT OF GROUNDNUT USING TIME SERIES NDVI DATA IN ANANTAPUR DISTRICT, ANDHRA PRADESH

Assessment of crop condition is essential for crop monitoring and to predict productivity of the crops. The Remote Sensing data can provide near real time information about the seasonal crop status. A normalised difference vegetation index (NDVI) evaluates crop stages by inter-seasonal comparison with spatial and temporal variability. The present study is aimed to assess the crop condition of groundnut in Anantapur district for 2016. Phenological stage retrieval of crop growth is characterised by NDVI. It shows the growth stages for early to high growth period and harvesting period. NDVI images are generated using moderate resolution imaging spectroradiometer (MODIS) reflectance time series data and identified crop area. Composite seasonal NDVI images were classified into clusters using unsupervised classification (ISODATA) and crop temporal spectral response profiles were prepared from the NDVI images from June to November for 2010, 2012 and 2016. The specific NDVI changing patterns were observed with different crops, this indicates the feasibility of crop delineation with time series NDVI. The extent of groundnut cropped area was extracted in the study area using time series NDVI. The deviation of the NDVI is used to understand the crop growth in different stages and Season’s Max NDVI is used to assess the crop condition in the study area. The study revealed that crop productivity is showing a significant change from 2010 to 2016. In 2010, there were 6 mandals having poor or low condition, where as in 2016, 20 mandals were affected. By adopting this approach crop condition maps were generated.


Introduction
The demand for agriculture monitoring is increased for past 20 years, from global level to sub-national level with various applications to enhance the food security. The estimation of crop condition in the early stages of crop growing period can identify the shortages of food surpluses, which support in related decision making (Meng et al., 2008).  (Ray et al, 2014). Canada has the Crop Condition Assessment Programme (CCAP) and Brazil has the Geosafras programme (Zhang et al., 2014). The remote sensing data play a significant role in crop classification and crop health assessment. Vegetation Indices (VI) are reliable in monitoring the vegetation changes.
Normalised Difference Vegetation Index (NDVI) is a widely used index for monitoring the crop conditions with seasonal variations (Gitelson, et. al., 2013) and classification of land use (Franco and Mandla, 2012 (Vani and Mandla, 2017), biodiversity and wildlife distributions and natural disasters (Konda et al, 2018). Crop growth profile monitoring method is the contrast between present year with the previous year crop growing profiles during crop season, can identify the seasonal growth (Murthy et. al., 2014

Study Area and Data
The study area is Anantapur located in The present study area occupies 19, 13,000 hectares. 10, 00,000 hectares area is under rain-fed and 1, 08,000 hectares under irrigation (Rukmani and Manjula, 2009). This is the only drought-prone district with a 10 per cent The NDVI is a measure of the vigour of vegetation at the surface. The magnitude of NDVI shows the level of photosynthetic activity from observed vegetation. The NDVI is very high in NIR portion for healthy vegetation (Rouse et al, 1974).
NDVI is derived as : Eq (1) Where, NIR -Near Infra Red portion of spectrum and RED -Red Portion of the spectrum.
The NDVI values range from -1 to +1.

Extraction of Groundnut Crop Cover:
The prominent crop identification is necessary to generate crop maps which provide the spatial distribution information about the agricultural fields (Zhang et al., 2014). The crop map was generated using MODIS NDVI datasets. The NDVI profiles of different vegetations like forest and scrub area overlap the crop profiles. To avoid the influence the non-agriculture area overlap, masking is essential (Manjunath et al., 2015). The non-agricultural area is mostly occupied with forest and follows land. The end of the NDVI threshold for each season is shown in Figure 2. The SM NDVI represents summer monsoon (June to October), W NDVI represents winter max NDVI and the S is the summer season NDVI (Roy and Josi, 2017). Map masking is used for extraction of agricultural area from LULC level-1. It shows better visualisation.

Results and Discussion
The groundnut crop is the major crop which occupies more than 8 lakh hectares, where as the total crop area is 11 lakh hectares (as per statistics for 2010). The NDVI time series images were used to assess the crop growth using interannual comparisons of NDVI profiles. The crop calender of Anantapur district shows that the kharif season starts at June and ends by November and length of the crop growing period is 60 to 90 days for groundnut crop. The NDVI profiles for the normal year (2010)

Seasonal Patterns of NDVI Deviation
The statistical average of NDVI for entire time series was calculated for the study area. NDVI deviation images between 2010 and 2016 were compared to identify crop condition. The NDVI deviation images are shown in Figure 5. Table 1 shows