This document discusses using machine learning techniques to classify crop types in the Middle Rio Grande region using multi-temporal satellite imagery. Specifically, it aims to 1) classify five main crop types (cotton, pecan, onion, pepper, alfalfa) in cultivated and non-cultivated areas using random forest classification and convolutional neural networks, and 2) determine which method achieves the highest accuracy. The methodology uses 134 bands of Landsat and other satellite data as inputs to random forest and CNN models to classify crop type and cultivation status in 2017, with the goal of improving accuracy over pixel-based methods.

1. USING MULTI-TEMPORAL SATELITE IMAGERYAND
MACHINE LEARNING TO PREDICT CROP TYPES IN
MIDDLE RIO GRANDE REGION
Habibur Howlider*, Saurav Kumar, Thomas Poulose, Deana Pennington
University of Texas at El Paso
*hrhowlider@miners.utep.edu
Introduction
Middle Rio Grande basin covers the watershed of the Rio Grande from
downstream of Elephant Butte (EB) reservoir to the entrance of the Rio
Conchos from Mexico. are the major crops grown in this watershed.
Deep learning (DL) is a powerful state-of-the-art technique for image
processing including multitemporal remote sensing (RS) images.
Although, pixel-based classification algorithms such as Random Forests
(RF) are widely used for cropland mapping but deep learning
classification using neural network are also becoming popular to find
better accuracy while classify the different crop types.
Objective
1.To understand crop type in the both cultivated and non-cultivated area
of the middle Rio Grande Region by classifying area under five main
crops (Cotton, pecan, onion, pepper and alfalfa) using multi-temporal
satellite imagery.
2. To predict and find highest overall accuracy for crop type classification
by using supervised classification algorithm (Random Forest) and deep
learning (Convolutional Neural Network).
Methodology
1. Classification
Features (134 Bands):
 Landsat bands monthly cloud free median (7 x 12 = 84) 30m
 Monthly NDVI (12) 30m
Monthly CRSI (12) 30m
 Global elevation (1) 90m
 Global slope (1) 90m
 Global monthly ET (12) 1000m
 Elephant Bute monthly flows (12)
Targets:
Cultivated / Uncultivated 2017
Crop Type 2017
2. Supervised Classification with Convolutional Neural Networks
Four-level architecture proposed for classification of crop types from
multitemporal satellite imagery.
Level I : Preprocessing
Level II: Supervised Classification
Level III: Postprocessing
Level IV: Geospatial Analysis
Model Workflow
Discussion
For random forest classification using Google Earth Engine, validation
overall accuracy for cultivation/non cultivation was found 94% where
validation accuracy was 73%. Deep learning classification using
convolutional neural network can be added to the predictive model for
increasing the validation accuracy for each dataset.
NDVI =
Landsat 7 Bands
NDVI
CRSI
Elevation
Slope
ET
Monthly Flows
Random
Forest
Classifier
2017 Dataset
All Data
2001 - 2017
Crop Type Area > 2500 acres
Crop Type
Cultivated / Not
Cultivated
Target
Masking
Random
Forest
Classifier
Fig 2 . 3-D CNN Architecture for classification of crop types from
multitemporal satellite imagery
Fig 1. Middle Rio Grande Basin
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000
bnwy nkejdickcde xij
M
j
M
i
N
kn
 
 

Equation for 3D Convolution for Multi-temporal Multi-Spectral
Images:
where wkij is a 3D tensor, k is the temporal indicator and N is its length, n
indicates n-th feature map of previous layer, xcde and ycde are the input and
output activation at location (c, d, e), respectively.
Fig 3: Model workflow of crop classification by a) supervised classification
algorithm (Random Forest) and b) Deep Learning (Convolutional Neural
Network) using Landsat 7 Satellite Imagery
Study
Area
CRSI =
a
b