Maize crop monitoring with sentinel-1 radar satellite- case study of Endebess,Kenya
|Volume||JASETD Volume 4 Issue 1 2020|
|Authors||Kuria, T. B, Kirimi, K. F, Gikwa, W.C and Kuria, N. D|
|Article Type||Research article|
|First Published||October 2020|
Monitoring of crop status coupled with knowledge of growth stages of each crop is a requirement for sustainable agriculture management. Monitoring the crop condition manually in the field is labour, cost and time intensive. Remote sensing therefore provides fast, cost effective and timely tools necessary for the effective monitoring of the crops. The acquisition of cloud free optical images in tropical regions remains a big challenge since the cropping season is characterized by high cloud cover. Radar images which are independent of weather conditions were therefore preferred in this study due to their consistent acquisitions. The objective of this study was to analyse the performance of Sentinel-1 (S-1) C-band radar images in monitoring the maize growth by investigating the transferability of the maize phenological characteristics from one season to the other. This was accomplished by comparing the S-1 back scatter values for the 2015 and 2016cropping seasons. The acquired S-1 images were divided into three, according to the acquisition modes: ascending IW1; descending IW1; descending IW3. 18ADCOlngatongo Company maize fields were selected, with the principal maize growth and development stages being defined by the BBCH scale. From the results, the maize phenological development stages could be identified from the images. The backscatter values for fields having coincident planting dates for both 2015 and2016 had higher similarities compared to fields whose planting dates were far apart.The ascending IW1 had the best results. The results were however inconclusive since fewer images were available for 2015 for comparison with 2016. Thus,a comparison across the entire cropping season could not be conclusively undertaken.From the study, it was concluded that the phenological characteristics extracted for one cropping season can establish a baseline for the monitoring of the subsequent cropping seasons, which can be an indicator of expected yields.