Monitoring the Impacts of El Niño on the Extent of Cultivated Fields Using Sar Data Around the Agricultural Region of the Free State, South Africa

Abstract

ABSTRACT:There is continuous challenge in crop monitoring from erratic climatic phenomena such as irregular rainfall episodes during required farming seasons or cloud cover. Remote sensing has offered vital support in the monitoring of such scenarios and informs relevant authorities for better decision making. While optical sensors measure the greenness of vegetation to enable monitoring of its status, their usage is constrained by the continuous cloud cover during crop growth seasons in sub Saharan Africa. Synthetic aperture radar data (SAR) are on the other hand capable of penetrating clouds and are sensitive to the structure and moisture content of target features, thereby providing complementary information for monitoring crop cultivated fields. This study sought to evaluate the sensitivity of Sentinel -1 SAR data to the status of cultivated crop fields that experienced varying rainfall amounts between 2015/2016 and 2016/2017 growing seasons as a result of El Niño induced drought in 2015. Dual polarization composites per season were classified and through sample farms delineated from Google Earth image, backscatter values were extracted for statistical comparisons. The two sample t-test was applied to test significance of the differences between the two seasons at the level of farm status. Results showed an overall significant difference (p-value of 0.003< 0.005) in SAR backscatter sensitivity to cultivated crop fields during and after the El Niño phenomenon. While these results are encouraging for areas that experience clouds during growing seasons, further improvements can be expected by factoring in other variables such as topographic and moisture conditions of farms.

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

“…At the local scale, most often defined as the scale of a field [361] or several fields, researchers have performed a detailed analysis of crop development conditions [139,151,152,268,269,326,362,363].…”

“…The investigations conducted by Klosterman et al [118] are an example of the use of the green chromatic coordinate (GCC) indicator based on the time-series of drone RGB images in forest phenology studies. Burkart et al [269] adopted the method of time-series observation of RGB images provided by UAVs converted into GRVI (green-red vegetation index). Yang et al [39] used RGB data collected from a UAV to determine the dates of the rice phenophases.…”

“…The analyses of wheat and rice represented over 50% of all studies of arable crops [13,24,28,34,38,39,41]. Papers on maize accounted for 10% of the analyzed publications [3,5,65,292], whereas slightly less research has been carried out on vineyards and other crops such as soybean [98,101,132], oat [285], barley [269,301], and horticultural plants [30,212,354] (Figure 13).…”

“…Monitoring of vegetation may also be carried out with the use of the solar-induced fluorescence technique [268]. Radar is another relatively frequently used sensor for monitoring vegetation [151,269,270]. In combination with ground-based data, it can be a valuable source of information.…”