Jurnal matematika

Land Moisture Estimation at Agricultural Land
Using MODIS Data Based on NDSI, NDVI,
and NDWI Indices

Abstract
This research is aimed to estimate land moisture condition at agricultural land, especially for paddy field based on MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data with 250 m and 500 m spatial resolution and daily temporal resolution. An index is called Land Moisture Index (LMI) was created from 1st principle component result of NDSI (Normalize Difference Soil Index), NDVI (Normalize Difference Vegetation Index), and NDWI (Normalize Difference Water Index) with equation :
LMI = 0.484*NDSI + 0.687*NDVI + 0.542*NDWI
There is a high correlation between LMI and soil moisture (LM) for the agricultual land with soil moisture <= 75 %, whereas an increase of LM followed by an increase LMI. Finally, an estimation model has been developed to estimate land moisture condition for the agricultural land with equation : LM = 172.2145*exp (-0.76102/IKL) r2 = 0.83 Based on the above method, land moisture can be derived spatially for the agricultural land, especially for paddy field for drought prediction. Keywords : land moisture, MODIS, linier combination, Land Moisture Index, surface soil moisture I. INTRODUCTION Available soil water is one of factor which is the necessary for food crop agriculture, horticulture, plantation, and the forestry. Land moisture information is very needed for planning, monitoring and management of agriculture crop. Soil moisture measurement trough ground survey with conventional equipments like technique gravimetric, tensiometer, neutron probe can give very accurate information but needed expense costly for the very wide regional measurement. Land moisture estimation by using Landsat Thematic Mapper (TM) have been conducted by Dirgahayu (1997) for the area plantation of sugar cane in Jatitujuh, West Java. Soil Brightness Index (SBI) was created by applying the principal component analysis onto band 2nd – 5th of Landsat TM. SBI could be used to estimate land moisture with high correlation. But Landsat TM data which have 30 m spatial resolution only according to be used at certain area and also for the certain time because this satellite own temporal resolution 16 days and now still have problem (SLC off). Meanwhile, information about land moisture oftentimes required for monitoring continuously, because early information about drought very needed to anticipate raisen impact. One of exciting way to monitor land moisture at wide area every day is exploited satellite data such as MODIS data which own moderate resolution and the daily observation. Using Satellite Data can be decreased of costly expense. In this research, the predictor parameter of land moisture derived from combination of modis reflectance which represented by NDWI (Normalize Difference Water Index), NDSI (Normalize Difference Soil Index), and NDVI (Normalize Difference Vegetation Index ) and. Those Land Index have contrast given onto 3 general object on the earth such as water, soil, and vegetation. The objective of research is to develop the estimation model of land moisture by using land moisture index which derived from combination of 3 indices (NDSI, NDVI, and NDWI). 2 II. METHODOLOGY 2.1. Data Used Data used for estimating land moisture are reflectances which derived from daily MODIS data on June, July, and August 2004 with the same time of ground survey to measure land mosiure. 2.2. Field Data Processing Soil sample which taken from field survey have analyzed at soil physic laboratorium by using gravimetric method. Afterwards, land moisture information can be known.; 2.3. MODIS Data Processing 2.3.1. Corrected Reflectance The corrected reflectance from atmosphere effect each channels of MODIS L1B Data produced by level 2 processing. Furthermore are conducted advance processing to improve and repair data quality, ie : Bow-Tie and Geometric Correction to make reflectances data in 250 m (R1, R2) and 500 m (R3 - R7) spatial resolution. 2.3.2. Making Indices Three indices that can represent land condition (wet, dry, bare or vegetated) are NDWI, NDSI, and NDVI. Those indices are influenced by land moisture condition on surface (0 – 20 cm soil depth). Like as NDVI, NDSI and NDWI can be derived based on peak value of spectral response onto general objects (water, soil, vegetation) at wavelength variety which can be shown on Figure 2-1. Figure 2-1. General Spectral Response of Water, Bare Soil, and Vegetation at Variety Wavelength Spectrum Wavelength (μm) Peak values for vegetation object is shown contrastly at wavelength 0.6 μm (red) and 0.8 μm (near infra red). Peak values for water object is shown contrastly at wavelength 0.4 μm (blue) or 0.8 μm and 0.6 μm, while peak values of open area or bare soil lies at 0.8 μm and 1.8 μm(SWIR = short wave infra red). Research result by Dirgahayu (2005) obtain the best of 3 reflectances of MODIS data for estimating land mositure. Those are Red (R1), NIR (R2), and SWIR (R6) reflectances. Based on that result, so NDWI and NDSI can be created like as computing NDVI by using the following formula below (a) NDWI = Normalize Difference Water Index to detect land wetness with equation : NDWI = (R1 – R6)/(R1 + R6) (2-1) (b) NDSI = Normalize Difference Soil Index to detect land dryness with equation : NDSI = (R6 – R2)/(R2 + R6) (2-2) (c) NDVI = Normalize Difference Vegetation Index to detect land greeness with equation : 3 NDVI = (NIR – Red)/(Red + NIR) (2-3) 2.2.3. Analysis Statistic value extraction (minimum, maximum, mean, median and standard deviation) for each reflectane of MODIS data was done in training area of ground survey point under homogenity consideration. Training sample must be considered to composite RGB 6,2,1 image. Correlation and regression analysis was done to know relation among indices and land moisture, especially which have moisture less than 75 %. Principle component transformation will be conducted if there are significant correlations among indices. The result of the first principle component analysis will be used to create a new index hereinafter referred to as Land Moisture Index (LMI) LMI = b1*X1 + b2*X2 + b3*X3 (2-4) Where : X1, X2, and X3 are NDSI, NDVI, NDWI and b1,b2, b3 are vector eigen coeficients For obtaining the best estimation model is conducted model simulation in non liner form (power, exponential, or logarithmic) between LMI and land moisture. The model selected is have high Determination coeficient (R2) and the smallest of standard error (Se). III. RESULT AND DISCUSSION 3.1. The Relatioship between Land Indices and Land Moisture To see relation among each index with land moisture, hence some locations have selected which have < 75 % moisture level. This is conducted because agricultural land with > 75 % moisture is relative more peaceful from drought risk onto crop growth. Extraction result of three indices and land moisture are shown in Table 1. The scatter plot between land moisture and each index and also result of trend analysis are shown in Figure 1 until Figure 5.