Spatial-Temporal Patterns of Agricultural Drought in Upper Progo Watershed Based on Remote Sensing and Land Physical Characteristics

Abstract

Agricultural drought is alarmed by meteorological drought characterized by lower year-to-year rainfall. Under long period and continuous water deficits, plants may demonstrate stress symptoms and wilt or die. Furthermore, agricultural drought leads to crop failures and threaten the food security of an area. Progo Hulu sub-watershed is a major agricultural area in Temanggung Regency. Spatial-temporal pattern-based information about agricultural drought can be a basis for decision making in drought mitigation. This study aims to analyze spatial and temporal distribution patterns of drought, analyze the physical characteristics of land and their influence on drought pattern, and establish a prediction model of drought distribution patterns based on four physical characteristics of the land. Landsat 8 imagery is used to determine the spatial and temporal patterns of agricultural drought in Upper Progo watershed using an improved Temperature vegetation Dryness Index (iTVDI). Slope, land use, landform, and soil texture are the physical characteristics of land as the variables to determine the most influential factor of drought pattern. They are analyzed using multiple regression analysis techniques. Pixel samples are obtained through purposive sampling method based on land units. The results reveal that the spatial-temporal distribution of agricultural drought occurs rapidly on the slopes and foothills of Sumbing and Sindoro. These areas have the highest average value of the iTVDI index. Agricultural drought extends gradually in line with the number of days without rainfall. Landform is a physical characteristic that most influences the distribution of agricultural drought. The established model by utilizing four variables of physical characteristics generates an average value which almost similar to the iTVDI value produced by remote sensing data. The model can be useful to estimate drought distribution based on the number of days without rainfall.

Monitoring Rice Crop and Paddy Field Condition Using UAV RGB Imagery

“…In recent years, researchers have tried to develop many applications and conduct research such as planting, pest and disease, irrigation, fertilizer, and precision agriculture in rice production to sustain the crop production [16], [17,18]. One of the most promising research is using the technology of remote sensing (RS), which allows farmers to monitor the growing condition of crops, to timely acquire the information on crop production [19], [20], [21]. Thus, the usage of the remote sensing technology will be helpful since it can produce the temporal and high spatial imagery to monitor the crop condition [22].…”

Spatiotemporal Variability in Soil Water Content Profiles under Young and Mature Oil Palm Plantations in North Bengkulu Regency

“…When a map of soil physical characteristics is available in such an area, then typical spatiotemporal variation in soil water content may be used to estimate soil water balance throughout the year. For example, the spatial and monthly distribution of drought potential in the mapped area can be estimated based on the number of days without rainfall [30]. The rainfall-based information for drought distribution prediction will help growers and decision-makers manage crop cultivation during the dry season, particularly around the study region.…”

The Potential of Agricultural Land Drought Using Normalized Difference Drought Index in Ciampel Subdistrict Karawang Regency

“…Positive IOD occurs when sea surface temperature in the southeastern part of the Indian Ocean decreases or is lower than in the western part of the Indian Ocean, so the wind moves westwards, resulting in a drought in Indonesia, while a negative IOD occurs instead. The impact caused by the 1997 El Nino in Java in the form of the dry season that came earlier (based on February I-III) from the normal period (basis of I-III April) and experienced drought irregularities (the dry season occurred longer than the normal period) especially on the north coast of West Java resulted in a decrease in the area of harvest in Java due to drought during the El Nino period [20]- [22].…”

“…The results showed that dry grades dominate TVDI, and there is a relationship between the two variables that are weak, which means the value of TVDI does not have a big effect on rice productivity and the relationship is negative or not unidirectional, which means the higher the TVDI value then, the lower the productivity value [24]. Another research on TVDI was conducted in Upper Progo Watershed to estimated drought distribution based on the number of days without rainfall [25].…”