# Moisture Interpretation from Landsat Images

I am working on a "land suitability for crop" project. Can anyone let me know how to get any type of information which can be related to moisture index of soil?

I have Landsat 5 data with band 3 and band 5 and working on ArcGIS 9.3. Any type of suggestion is most welcomed.

-
I think you need to expand on moisture index. I would expect you want to sense surface soil colour, infer the soil type and infer Moisture Holding Capacity from that, based on some background relation on soil colour to type. MHC is a long term feature of a soil and determines sustainable levels of production. Current moisture status determines seasonal production, if it is measured to a reasonable depth. Remote sensing can only measure soil moisture directly to a few mm and so it is a long stretch of the imagination to get reliable numbers on soil moisture which relate to land suitability. – Willy Jun 23 '12 at 11:59

Consider integrating DEMs into your research on soil moisture/exposure. I have used the following indices in the past for regression models (Davies et al. 2010):

Site exposure index = slope∗cos(pi∗(aspect−180)/180) (Balice et al. 2000)

Heat load index = 0.039 + [0.808 * cos(l) * cos(s)] – [0.196*sin(l)*sin(s)] – [0.482*cos(a)*sin(s)] (McCune and Keon 2002)

Integrated moisture index = (hill shade*0.5) + (curvature*0.15) + (flow accumulation*0.35) (Iverson et al. 1997)

where, l = latitude s = slope a = folded slope

Of course, you can use these in Arc's raster calculator to produce new rasters from DEMs. See references for the specific details to tailor indices to your needs. Good luck!

References:

Balice, R. G., J. D. Miller, B. P. Oswald, C. Edminister, and S. R. Yool. 2000. Forest surveys and wildfire assessment in the Los Alamos; 1998–1999. Los Alamos, NM, USA Los Alamos National Laboratory. LA-13714-MS. 12 p.

Davies, K.W., Petersen, S.L., Johnson, D.D., Davis, D.B., Madsen, M.D., Zvirdin, D.L., and Bates. J.D. 2010. Estimating Juniper Cover From National Agriculture Imagery Program (NAIP) and Evaluating Relationships Between Potential Cover and Environmental Variables. Rangeland Ecology and Management, 63(6): 630-637.

Iverson, L. R., M. E. Dale, C. T. Scott, and A. Prasad. 1997. A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.). Landscape Ecology 12:331–348. CrossRef, CSA

McCune, B. and D. Keon. 2002. Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science 13:603–606.

-
What are `l` and `s`? What are the units of measurement for hillshade (0..255, 0..1, or something else)? The units for curvature (ESRI often multiplies it by 100 or 400 or something)? For flow accumulation? Note, too, that the first equation assumes the coordinate system's second coordinate truly points north-south. – whuber Jun 22 '12 at 13:13
Thank you for the references, Aaron! (+1). – whuber Jun 22 '12 at 13:48

Please mention the sensor of Landsat 5, is it MSS or TM? Assuming it is Thematic Mapper data, you have visible red and shortwave infrared data. You can directly infer from the band reflectance values about where vegetation patches lie and hence moisture content.

Band 3 (Red) can help you discriminate vegetation slopes and Band 5 (SWIR) can help you discriminate moisture content of soil and vegetation. You can calculate band ratios or normalized band indices to ascertain drought stress or soil moisture.

You can calculate Normalized Difference Water Index (NDWI) or Normalized Difference Soil Index (NDSI) and Normalized Difference Vegetation Index (NDVI).

For Landsat TM, NDWI = (Band 3 - Band 5)/ (Band 3 + Band 5). This is the only index you can calculate with the data available to you. If you have access to Band 4 (NIR), you can additionally compute Soil index,NDVI,NVMI and Soil adjusted vegetation index (SAVI). Hope this helps.

-