Soil Moisture Index To Estimate Water Availability In Coffee Gardens In Karangploso District, Malang Regency

Soemarno Soemarno, Nisfi Fariatul Ifadah, Ajral Muklisin, Rahmanda Muhammad Sukmajati

Abstract


Soil moisture measurements for large areas of coffee plantation using traditional methods are very difficult, labor intensive, highly costs, and time consuming. Soil moisture index (SMI) can be identified with the remote sensing methods, using algorithm data from satellite sensors such as land surface temperature (LST) and vegetation index (NDVI).   This research aimed to analyze soil moisture status using Soil Moisture Index, to know the relationship between NDVI , SMI and coffee production in smallholder coffee plantation in Karangploso, Malang regency. This research was conducted by field observation, and laboratory analysis. There were ten observation plots at the area os smallholder Robusta coffee plantation. Results of this research showed that: (1) The SMI value in smallholder coffee plantation varied in the range of 0.65-0.94 (High – Very High category). (2) The SMI value (Image Method) was a good predictor for estimating the soil's ability to store available water (AWC, Available Water Capacity) (r= 0.7491**). (3) The NDVI value in smallholder coffee plantation varied in the range of 0.26-0.48 (Low – High category). (4) The SMI value (Image Method) was significantly correlated with the SMI value (Field Method) (r= 0.8154**). (5) Values of NDVI and SMI(Image Method) are good predictors for estimating coffee production, the regression model is: Prod = 147.4571 + 876.3815SMI(Citra) + 1203.327NDVI (R2= 0.6646; Sig F= 0.00009).

Keywords


LST (Land Surface Temperature), NDVI (Normalized Difference Vegetation Index), SMI (Soil Moisture Index)

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References


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DOI: https://doi.org/10.17969/rtp.v18i2.43645

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