Abstract Fore-warning plays an essential role for effective drought mitigation planning. A new approach, i.e. fractional wetness (fw), has been proposed towards fore-warning of early season agricultural drought over Indian region. The optimum soil moisture triggers the subsequent crop sowing activities that has been conceptualized in this approach. Shortwave Angle Slope Index (SASI) images derived from time-series (2001–2012) MODIS data was used for fractional-wetness (fw) computation. fw is the surrogate indicator for the percentage moisture available for a given time, based on its long-term range. The zone-wise fwthresholds was developed to estimate the area-favourable-for-crop-sowing (AFCS), which was validated with subsequent months’ crop-planted-area derived from fractional vegetation cover (fc). The mean absolute error (MAE) was found to be ∼5.73% of the agricultural area. The present methodology is capable of fore-warning the agricultural-drought one month prior to the traditional method. Moreover, the comparison of predicted AFCS with all India food-grains area corroborates the feasibility of proposed approach in quantifying the agricultural-drought, in terms of its intensity, extent and progression.

中文翻译：

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印度地区早季农业干旱状况的预警——部分湿度方法

摘要 预警对于有效的抗旱规划起着至关重要的作用。已经提出了一种新方法，即分数湿度（fw），用于预警印度地区的早季农业干旱。最佳土壤湿度会触发随后的作物播种活动，该活动已在此方法中概念化。来自时间序列（2001-2012）MODIS 数据的短波角斜率指数（SASI）图像用于计算分数湿度（fw）。fw 是给定时间内可用水分百分比的替代指标，基于其长期范围。开发了分区 fwthresholds 以估计有利于作物播种的面积 (AFCS)，并通过随后几个月从植被覆盖率 (fc) 得出的作物种植面积进行验证。发现平均绝对误差（MAE）约为农业面积的 5.73%。本方法能够比传统方法提前1个月对农业干旱进行预警。此外，预测的 AFCS 与所有印度粮食面积的比较证实了所提出的方法在量化农业干旱方面的可行性，包括强度、范围和进展。